Fixes multiple typos

docs/nlp/customize_encoder.ipynb

@@ -497,7 +497,7 @@
       "source": [
         "#### Customize Feedforward Layer\n",
         "\n",
-        "Similiarly, one could also customize the feedforward layer.\n",
+        "Similarly, one could also customize the feedforward layer.\n",
         "\n",
         "See [the source of `nlp.layers.GatedFeedforward`](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/layers/gated_feedforward.py) for how to implement a customized feedforward layer.\n",
         "\n",

docs/vision/object_detection.ipynb

@@ -218,7 +218,7 @@
         "# Need to provide\n",
         "  # 1. image_dir: where images are present\n",
         "  # 2. object_annotations_file: where annotations are listed in json format\n",
-        "  # 3. output_file_prefix: where to write output convered TFRecords files\n",
+        "  # 3. output_file_prefix: where to write output converted TFRecords files\n",
         "python -m official.vision.data.create_coco_tf_record --logtostderr \\\n",
         "  --image_dir=${TRAIN_DATA_DIR} \\\n",
         "  --object_annotations_file=${TRAIN_ANNOTATION_FILE_DIR} \\\n",

docs/vision/semantic_segmentation.ipynb

@@ -420,7 +420,7 @@
         "exp_config.task.train_data.dtype = 'float32'\n",
         "exp_config.task.train_data.output_size = [HEIGHT, WIDTH]\n",
         "exp_config.task.train_data.preserve_aspect_ratio = False\n",
-        "exp_config.task.train_data.seed = 21 # Reproducable Training Data\n",
+        "exp_config.task.train_data.seed = 21 # Reproducible Training Data\n",
         "\n",
         "# Validation Data Config\n",
         "exp_config.task.validation_data.input_path = val_data_tfrecords\n",
@@ -429,7 +429,7 @@
         "exp_config.task.validation_data.output_size = [HEIGHT, WIDTH]\n",
         "exp_config.task.validation_data.preserve_aspect_ratio = False\n",
         "exp_config.task.validation_data.groundtruth_padded_size = [HEIGHT, WIDTH]\n",
-        "exp_config.task.validation_data.seed = 21 # Reproducable Validation Data\n",
+        "exp_config.task.validation_data.seed = 21 # Reproducible Validation Data\n",
         "exp_config.task.validation_data.resize_eval_groundtruth = True # To enable validation loss"
       ]
     },

research/adversarial_text/graphs.py

@@ -45,7 +45,7 @@
 flags.DEFINE_integer('batch_size', 64, 'Size of the batch.')
 flags.DEFINE_integer('num_timesteps', 100, 'Number of timesteps for BPTT')
 
-# Model architechture
+# Model architecture
 flags.DEFINE_bool('bidir_lstm', False, 'Whether to build a bidirectional LSTM.')
 flags.DEFINE_bool('single_label', True, 'Whether the sequence has a single '
                   'label, for optimization.')

research/audioset/vggish/README.md

@@ -23,7 +23,7 @@ VGGish depends on the following Python packages:
 
 These are all easily installable via, e.g., `pip install numpy` (as in the
 sample installation session below). Any reasonably recent version of these
-packages shold work.
+packages should work.
 
 VGGish also requires downloading two data files:
 

research/audioset/vggish/vggish_smoke_test.py

@@ -17,7 +17,7 @@
 
 This is a simple smoke test of a local install of VGGish and its associated
 downloaded files. We create a synthetic sound, extract log mel spectrogram
-features, run them through VGGish, post-process the embedding ouputs, and
+features, run them through VGGish, post-process the embedding outputs, and
 check some simple statistics of the results, allowing for variations that
 might occur due to platform/version differences in the libraries we use.
 

research/autoaugment/train_cifar.py

@@ -353,7 +353,7 @@ def _compute_final_accuracies(self, meval):
     return valid_accuracy, test_accuracy
 
   def run_model(self):
-    """Trains and evalutes the image model."""
+    """Trains and evaluates the image model."""
     hparams = self.hparams
 
     # Build the child graph

research/cvt_text/base/embeddings.py

@@ -52,7 +52,7 @@
     '00-00', '00-00-00', '0-00-00', '00-00-0000', '0-00-0000', '0000-00-00',
     '00-0-00-0', '00000000', '0:00.000', '00:00.000',
     '0%', '00%', '00.' '0000.', '0.0bn', '0.0m', '0-', '00-',
-    # ontonotes uses **f to represent formulas and -amp- instead of amperstands
+    # ontonotes uses **f to represent formulas and -amp- instead of ampersands
     '**f', '-amp-'
 ]
 SPECIAL_TOKENS = ['<pad>', '<unk>', '<start>', '<end>', '<missing>']

research/efficient-hrl/agents/ddpg_agent.py

@@ -189,7 +189,7 @@ def actor_net(self, states, stop_gradients=False):
     Args:
       states: A [batch_size, num_state_dims] tensor representing a batch
         of states.
-      stop_gradients: (boolean) if true, gradients cannot be propogated through
+      stop_gradients: (boolean) if true, gradients cannot be propagated through
         this operation.
     Returns:
       A [batch_size, num_action_dims] tensor of actions.

research/efficient-hrl/eval.py

@@ -210,7 +210,7 @@ def get_eval_step(uvf_agent,
     mode: a string representing the mode=[train, explore, eval].
 
   Returns:
-    A collect_experience_op that excute an action and store into the
+    A collect_experience_op that execute an action and store into the
     replay_buffer
   """
 

research/efficient-hrl/train.py

@@ -70,7 +70,7 @@ def collect_experience(tf_env, agent, meta_agent, state_preprocess,
     disable_agent_reset: A boolean that disables agent from resetting.
 
   Returns:
-    A collect_experience_op that excute an action and store into the
+    A collect_experience_op that execute an action and store into the
     replay_buffers
   """
   tf_env.start_collect()

research/lfads/distributions.py

@@ -434,7 +434,7 @@ def __init__(self, zs, prior_zs):
     """
     # L = -KL + log p(x|z), to maximize bound on likelihood
     # -L = KL - log p(x|z), to minimize bound on NLL
-    # so 'KL cost' is postive KL divergence
+    # so 'KL cost' is positive KL divergence
     kl_b = 0.0
     for z, prior_z in zip(zs, prior_zs):
       assert isinstance(z, Gaussian)
@@ -475,7 +475,7 @@ def __init__(self, post_zs, prior_z_process):
 
     # L = -KL + log p(x|z), to maximize bound on likelihood
     # -L = KL - log p(x|z), to minimize bound on NLL
-    # so 'KL cost' is postive KL divergence
+    # so 'KL cost' is positive KL divergence
     z0_bxu = post_zs[0].sample
     logq_bxu = post_zs[0].logp(z0_bxu)
     logp_bxu = prior_z_process.logp_t(z0_bxu)

research/lfads/lfads.py

@@ -1127,7 +1127,7 @@ def randomize_example_idxs_mod_batch_size(nexamples, batch_size):
       batch_size: Number of elements in batch.
 
     Returns:
-      The randomized, properly shaped indicies.
+      The randomized, properly shaped indices.
     """
     assert nexamples > batch_size, "Problems"
     bmrem = batch_size - nexamples % batch_size
@@ -1164,7 +1164,7 @@ def shuffle_spikes_in_time(self, data_bxtxd):
     max_counts = np.max(data_bxtxd)
     S_bxtxd = np.zeros([B,T,N])
 
-    # Intuitively, shuffle spike occurances, 0 or 1, but since we have counts,
+    # Intuitively, shuffle spike occurrences, 0 or 1, but since we have counts,
     # Do it over and over again up to the max count.
     for mc in range(1,max_counts+1):
       idxs = np.nonzero(data_bxtxd >= mc)

research/lfads/run_lfads.py

@@ -136,7 +136,7 @@
 flags.DEFINE_integer("ext_input_dim", EXT_INPUT_DIM, "Dimension of external \
 inputs")
 flags.DEFINE_integer("num_steps_for_gen_ic", NUM_STEPS_FOR_GEN_IC,
-                     "Number of steps to train the generator initial conditon.")
+                     "Number of steps to train the generator initial condition.")
 
 
 # If there are observed inputs, there are two ways to add that observed
@@ -193,7 +193,7 @@
                      "Input scaling for rec weights in generator.")
 
 # KL DISTRIBUTIONS
-# If you don't know what you are donig here, please leave alone, the
+# If you don't know what you are doing here, please leave alone, the
 # defaults should be fine for most cases, irregardless of other parameters.
 #
 # If you don't want the prior variance to be learned, set the
@@ -379,7 +379,7 @@
 # what those LL lower bounds mean anymore, and they cannot be compared
 # (AFAIK).
 flags.DEFINE_float("kl_ic_weight", KL_IC_WEIGHT,
-                   "Strength of KL weight on initial conditions KL penatly.")
+                   "Strength of KL weight on initial conditions KL penalty.")
 flags.DEFINE_float("kl_co_weight", KL_CO_WEIGHT,
                    "Strength of KL weight on controller output KL penalty.")
 

research/lfads/utils.py

@@ -49,7 +49,7 @@ def linear(x, out_size, do_bias=True, alpha=1.0, identity_if_possible=False,
   configurations.
 
   Args:
-    x: input The tensor to tranformation.
+    x: input The tensor to transformation.
     out_size: The integer size of non-batch output dimension.
     do_bias (optional): Add a learnable bias vector to the operation.
     alpha (optional): A multiplicative scaling for the weight initialization
@@ -171,7 +171,7 @@ def write_data(data_fname, data_dict, use_json=False, compression=None):
   """Write data in HD5F format.
 
   Args:
-    data_fname: The filename of teh file in which to write the data.
+    data_fname: The filename of the file in which to write the data.
     data_dict:  The dictionary of data to write. The keys are strings
       and the values are numpy arrays.
     use_json (optional): human readable format for simple items

research/object_detection/meta_architectures/center_net_meta_arch.py

@@ -2269,7 +2269,7 @@ def __new__(cls,
         estimation, the class id should correspond to the "human" class. Note
         that the ID is 0-based, meaning that class 0 corresponds to the first
         non-background object class.
-      keypoint_indices: A list of integers representing the indicies of the
+      keypoint_indices: A list of integers representing the indices of the
         keypoints to be considered in this task. This is used to retrieve the
         subset of the keypoints from gt_keypoints that should be considered in
         this task.
@@ -4247,7 +4247,7 @@ def postprocess(self, prediction_dict, true_image_shapes, **params):
           tf.one_hot(
               postprocess_dict[fields.DetectionResultFields.detection_classes],
               depth=num_classes), tf.bool)
-      # Surpress the scores of those unselected classes to be zeros. Otherwise,
+      # Suppress the scores of those unselected classes to be zeros. Otherwise,
       # the downstream NMS ops might be confused and introduce issues.
       multiclass_scores = tf.where(
           class_mask, multiclass_scores, tf.zeros_like(multiclass_scores))

research/object_detection/meta_architectures/faster_rcnn_meta_arch.py

@@ -88,7 +88,7 @@
   `_postprocess_rpn` is run as part of the `_predict_second_stage` using
   `resized_image_shapes` to clip proposals, perform non-max suppression and
   normalize them. Subsequently, `_postprocess_box_classifier` is run as part of
-  `_predict_third_stage` using `true_image_shapes` to clip detections, peform
+  `_predict_third_stage` using `true_image_shapes` to clip detections, perform
   non-max suppression and normalize them. In this case, the `postprocess` method
   skips both `_postprocess_rpn` and `_postprocess_box_classifier`.
 """

research/rebar/rebar.py

@@ -864,7 +864,7 @@ def get_rebar_gradient(self):
     return total_grads, debug, variance_objective
 
 ###
-# Create varaints
+# Create variants
 ###
 class SBNSimpleMuProp(SBN):
   def _create_loss(self):

research/rebar/utils.py

@@ -27,7 +27,7 @@
 import tensorflow as tf
 
 #
-# Python utlities
+# Python utilities
 #
 def exp_moving_average(x, alpha=0.9):
   res = []

research/vid2depth/model.py

@@ -183,7 +183,7 @@ def build_loss(self):
             if i > j:
               # Need to inverse egomotion when going back in sequence.
               egomotion_mult *= -1
-            # For compatiblity with SfMLearner, interpret all egomotion vectors
+            # For compatibility with SfMLearner, interpret all egomotion vectors
             # as pointing toward the middle frame.  Note that unlike SfMLearner,
             # each vector captures the motion to/from its next frame, and not
             # the center frame.  Although with seq_length == 3, there is no

research/vid2depth/repo.bzl

@@ -1,6 +1,6 @@
 """ TensorFlow Http Archive
 
-Modified http_arhive that allows us to override the TensorFlow commit that is
+Modified http_archive that allows us to override the TensorFlow commit that is
 downloaded by setting an environment variable. This override is to be used for
 testing purposes.
 

research/vid2depth/util.py

@@ -59,7 +59,7 @@ def is_a_numpy_array(obj):
 
 
 def count_parameters(also_print=True):
-  """Cound the number of parameters in the model.
+  """count the number of parameters in the model.
 
   Args:
     also_print: Boolean.  If True also print the numbers.