research_paper_implementation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WcarqQUXNrGB"
   },
   "source": [
    "#  <font color='red'>Table of Contents</font>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xK3qVL4pNt19"
   },
   "source": [
    "[15. Multi Task Learning](#section15)<br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 104
    },
    "colab_type": "code",
    "id": "s0-OQReUNxm9",
    "outputId": "4c3d5b56-ff6e-4519-d172-c382e6a5edce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2  CPU cores\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Research Kernel Link - https://arxiv.org/pdf/2003.02261.pdf\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import itertools\n",
    "import os\n",
    "import sys\n",
    "from prettytable import PrettyTable\n",
    "import pickle\n",
    "import multiprocessing\n",
    "from multiprocessing.pool import ThreadPool\n",
    "from tqdm import tqdm_notebook\n",
    "print(multiprocessing.cpu_count(),\" CPU cores\")\n",
    "\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"axes.grid\"] = False\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, cohen_kappa_score,accuracy_score\n",
    "from sklearn.preprocessing import MultiLabelBinarizer\n",
    "\n",
    "from PIL import Image\n",
    "import cv2\n",
    "\n",
    "import keras\n",
    "from keras import applications\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras import optimizers,Model,Sequential\n",
    "from keras.layers import Input,GlobalAveragePooling2D,Dropout,Dense,Activation,BatchNormalization,GlobalMaxPooling2D,concatenate,Flatten\n",
    "from keras.callbacks.callbacks import EarlyStopping,ReduceLROnPlateau,Callback\n",
    "from keras.initializers import random_normal\n",
    "from keras.models import load_model\n",
    "from keras.losses import binary_crossentropy,categorical_crossentropy,mean_squared_error\n",
    "from keras import backend as K\n",
    "\n",
    "import tensorflow as tf\n",
    "#import shap\n",
    "\n",
    "# Colab Libs...\n",
    "from pydrive.auth import GoogleAuth\n",
    "from pydrive.drive import GoogleDrive\n",
    "from google.colab import auth\n",
    "from oauth2client.client import GoogleCredentials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gsWsEmxVNzmI"
   },
   "source": [
    "#  <a id = 'section15'> <font color='red'>  15. Multi task learning  </font> </a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3ghH0prCN6_F"
   },
   "source": [
    "### <font color='red'> 15.1 Setup Colab Environment </font>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YSpwm4uBCwe3"
   },
   "outputs": [],
   "source": [
    "'''\n",
    "The codes below uses Authentication URL to allow loading of Files and Images from Google Drive to Colab Memory\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DaZG-E-IOE7F"
   },
   "outputs": [],
   "source": [
    "# Importing Libraries\n",
    "#ref - https://buomsoo-kim.github.io/colab/2018/04/16/Importing-files-from-Google-Drive-in-Google-Colab.md/\n",
    "\n",
    "auth.authenticate_user()\n",
    "gauth = GoogleAuth()\n",
    "gauth.credentials = GoogleCredentials.get_application_default()\n",
    "drive = GoogleDrive(gauth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 121
    },
    "colab_type": "code",
    "id": "EbafLLivOG06",
    "outputId": "8dfee3c5-b3cb-4cbf-c86c-ba12c6d25e08"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /content/gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/gdrive',force_remount = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "hmdV3sZDOOsH",
    "outputId": "c12a203d-60de-4702-b612-9bfa5a3c2703"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We are currently in the folder of  /content/gdrive/My Drive/aptos2019\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.chdir('/content/gdrive/My Drive/aptos2019')\n",
    "print(\"We are currently in the folder of \",os.getcwd())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iqbNUH2r6dTa"
   },
   "source": [
    "### <font color='red'> 15.2 Load data and preprocess</font>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kG55l44tCzV1"
   },
   "outputs": [],
   "source": [
    "'''\n",
    "Loading Train and Validation Data from Previously split Train/Test data\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "a1y2H02GOQXu"
   },
   "outputs": [],
   "source": [
    "def load_data():\n",
    "    file = open('df_train_train', 'rb')\n",
    "    df_train_train = pickle.load(file)\n",
    "    file.close()\n",
    "\n",
    "    file = open('df_train_test', 'rb')\n",
    "    df_train_test = pickle.load(file)\n",
    "    file.close()\n",
    "    \n",
    "    return df_train_train,df_train_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 258
    },
    "colab_type": "code",
    "id": "AsdpEXif7Cto",
    "outputId": "69a0a7fe-a4e9-4a8a-efca-8379bb79acc3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2929, 4) (733, 4) \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_code</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>file_path</th>\n",
       "      <th>file_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3396</th>\n",
       "      <td>eb6b1f1c09db</td>\n",
       "      <td>0</td>\n",
       "      <td>./train_images/eb6b1f1c09db.png</td>\n",
       "      <td>eb6b1f1c09db.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>07a3be30563b</td>\n",
       "      <td>2</td>\n",
       "      <td>./train_images/07a3be30563b.png</td>\n",
       "      <td>07a3be30563b.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3204</th>\n",
       "      <td>df3adfd6ba36</td>\n",
       "      <td>2</td>\n",
       "      <td>./train_images/df3adfd6ba36.png</td>\n",
       "      <td>df3adfd6ba36.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3324</th>\n",
       "      <td>e68bdd36e589</td>\n",
       "      <td>0</td>\n",
       "      <td>./train_images/e68bdd36e589.png</td>\n",
       "      <td>e68bdd36e589.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1911</th>\n",
       "      <td>86e7f98f73f1</td>\n",
       "      <td>2</td>\n",
       "      <td>./train_images/86e7f98f73f1.png</td>\n",
       "      <td>86e7f98f73f1.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>1caba2fb38f6</td>\n",
       "      <td>0</td>\n",
       "      <td>./train_images/1caba2fb38f6.png</td>\n",
       "      <td>1caba2fb38f6.png</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id_code diagnosis                        file_path         file_name\n",
       "3396  eb6b1f1c09db         0  ./train_images/eb6b1f1c09db.png  eb6b1f1c09db.png\n",
       "100   07a3be30563b         2  ./train_images/07a3be30563b.png  07a3be30563b.png\n",
       "3204  df3adfd6ba36         2  ./train_images/df3adfd6ba36.png  df3adfd6ba36.png\n",
       "3324  e68bdd36e589         0  ./train_images/e68bdd36e589.png  e68bdd36e589.png\n",
       "1911  86e7f98f73f1         2  ./train_images/86e7f98f73f1.png  86e7f98f73f1.png\n",
       "396   1caba2fb38f6         0  ./train_images/1caba2fb38f6.png  1caba2fb38f6.png"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_train,df_train_test = load_data()\n",
    "print(df_train_train.shape,df_train_test.shape,'\\n')\n",
    "df_train_train.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "cwTSL-XLIYPv",
    "outputId": "2bbf64d0-8382-4ddd-9b8d-8c729e2cc2b0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2930 733\n"
     ]
    }
   ],
   "source": [
    "print(len(os.listdir(\"./train_images_resized_preprocessed/\")),len(os.listdir(\"./test_images_resized_preprocessed/\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "L_-O--AhOXXD"
   },
   "outputs": [],
   "source": [
    "def crop_image_from_gray(img,tol=7):\n",
    "    if img.ndim ==2:\n",
    "        mask = img>tol\n",
    "        return img[np.ix_(mask.any(1),mask.any(0))]\n",
    "    elif img.ndim==3:\n",
    "        gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
    "        mask = gray_img>tol\n",
    "        \n",
    "        check_shape = img[:,:,0][np.ix_(mask.any(1),mask.any(0))].shape[0]\n",
    "        if (check_shape == 0): # image is too dark so that we crop out everything,\n",
    "            return img # return original image\n",
    "        else:\n",
    "            img1=img[:,:,0][np.ix_(mask.any(1),mask.any(0))]\n",
    "            img2=img[:,:,1][np.ix_(mask.any(1),mask.any(0))]\n",
    "            img3=img[:,:,2][np.ix_(mask.any(1),mask.any(0))]\n",
    "    #         print(img1.shape,img2.shape,img3.shape)\n",
    "            img = np.stack([img1,img2,img3],axis=-1)\n",
    "    #         print(img.shape)\n",
    "        return img\n",
    "\n",
    "def circle_crop(img, sigmaX = 30):   \n",
    "    \"\"\"\n",
    "    Create circular crop around image centre    \n",
    "    \"\"\"    \n",
    "    img = crop_image_from_gray(img)    \n",
    "    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    \n",
    "    height, width, depth = img.shape    \n",
    "    \n",
    "    x = int(width/2)\n",
    "    y = int(height/2)\n",
    "    r = np.amin((x,y))\n",
    "    \n",
    "    circle_img = np.zeros((height, width), np.uint8)\n",
    "    cv2.circle(circle_img, (x,y), int(r), 1, thickness=-1)\n",
    "    img = cv2.bitwise_and(img, img, mask=circle_img)\n",
    "    img = crop_image_from_gray(img)\n",
    "    img=cv2.addWeighted(img,4, cv2.GaussianBlur( img , (0,0) , sigmaX) ,-4 ,128)\n",
    "    return img \n",
    "\n",
    "def preprocess_image(file):\n",
    "    input_filepath = os.path.join('./','train_images_resized','{}.png'.format(file))\n",
    "    output_filepath = os.path.join('./','train_images_resized_preprocessed','{}.png'.format(file))\n",
    "    \n",
    "    img = cv2.imread(input_filepath)\n",
    "    img = circle_crop(img) \n",
    "    cv2.imwrite(output_filepath, cv2.resize(img, (IMG_SIZE,IMG_SIZE)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xviDidlMLQtM"
   },
   "outputs": [],
   "source": [
    "'''\n",
    "Defining Global Variables to be used in this notebook\n",
    "'''\n",
    "FACTOR = 4\n",
    "BATCH_SIZE = 8 * FACTOR\n",
    "IMG_SIZE = 512\n",
    "EPOCHS = 20\n",
    "WARMUP_EPOCHS = 5\n",
    "LEARNING_RATE = 1e-4 * FACTOR\n",
    "WARMUP_LEARNING_RATE = 1e-3 * FACTOR\n",
    "HEIGHT = 320\n",
    "WIDTH = 320\n",
    "CANAL = 3\n",
    "N_CLASSES = df_train_train['diagnosis'].nunique()\n",
    "ES_PATIENCE = 5\n",
    "RLROP_PATIENCE = 3\n",
    "DECAY_DROP = 0.5\n",
    "\n",
    "LR_WARMUP_EPOCHS_1st = 2\n",
    "LR_WARMUP_EPOCHS_2nd = 5\n",
    "STEP_SIZE = len(df_train_train) // BATCH_SIZE\n",
    "TOTAL_STEPS_1st = WARMUP_EPOCHS * STEP_SIZE\n",
    "TOTAL_STEPS_2nd = EPOCHS * STEP_SIZE\n",
    "WARMUP_STEPS_1st = LR_WARMUP_EPOCHS_1st * STEP_SIZE\n",
    "WARMUP_STEPS_2nd = LR_WARMUP_EPOCHS_2nd * STEP_SIZE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YQQrFrG8CrSS"
   },
   "source": [
    "### <font color='red'> 15.3 Custom Image Data generator</font>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tnKuoUQUC5Pc"
   },
   "outputs": [],
   "source": [
    "'''\n",
    "This Function creates a custom Image Data generator, \n",
    "Since this is a Multi Output Model, a custom Image data generator is used (which yeild's outputs to next iterator)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-tlnoyOdJ0MM"
   },
   "outputs": [],
   "source": [
    "# custom generator ref - https://classifai.net/blog/multiple-outputs-keras/\n",
    "# ref - https://stackoverflow.com/questions/54143458/convert-categorical-data-back-to-numbers-using-keras-utils-to-categorical\n",
    "\n",
    "def multiple_outputs(generator,dataframe, image_dir, batch_size, height,width, subset):\n",
    "    gen = generator.flow_from_dataframe(\n",
    "        dataframe = dataframe,\n",
    "        x_col = \"file_name\",\n",
    "        y_col = \"diagnosis\",\n",
    "        directory = image_dir,\n",
    "        target_size=(height, width),\n",
    "        batch_size=batch_size,\n",
    "        class_mode='categorical',\n",
    "        subset=subset)\n",
    "    \n",
    "    mlb = MultiLabelBinarizer(classes = range(N_CLASSES))\n",
    "    \n",
    "    while True:\n",
    "        gnext = gen.next()\n",
    "        yield gnext[0], [np.argmax(gnext[1],axis = -1),gnext[1],mlb.fit_transform([list(range(x+1)) for x in np.argmax(gnext[1],axis = -1)])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "colab_type": "code",
    "id": "n8YzFFqHK15Q",
    "outputId": "3093a6cb-2614-48c7-8830-07bde108edb3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:337: UserWarning: This ImageDataGenerator specifies `zca_whitening`, which overrides setting of `featurewise_center`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n"
     ]
    }
   ],
   "source": [
    "train_datagen=ImageDataGenerator(rescale=1./255, rotation_range=360,brightness_range=[0.5, 1.5],\n",
    "                                     zoom_range=[1, 1.2],zca_whitening=True,horizontal_flip=True,\n",
    "                                     vertical_flip=True,fill_mode='constant',cval=0.,validation_split = 0.0)\n",
    "\n",
    "train_generator = multiple_outputs(generator = train_datagen,dataframe = df_train_train,\n",
    "                                   image_dir=\"./train_images_resized_preprocessed/\",\n",
    "                                   batch_size=BATCH_SIZE,height = HEIGHT,width = WIDTH,\n",
    "                                   subset='training')\n",
    "     \n",
    "valid_generator = multiple_outputs(generator = train_datagen,dataframe = df_train_test,\n",
    "                                   image_dir=\"./test_images_resized_preprocessed/\",\n",
    "                                   batch_size=BATCH_SIZE,height = HEIGHT,width = WIDTH,\n",
    "                                   subset='validation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VCFNVYNoOdhB"
   },
   "source": [
    "### <font color='red'> 15.4 Stage 1 (Pre Training) using ResNet50</font>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "Mwx2DeXP8MCO",
    "outputId": "d3bc4b98-0bad-440b-e75f-e0e265ae08c8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras_applications/resnet50.py:265: UserWarning: The output shape of `ResNet50(include_top=False)` has been changed since Keras 2.2.0.\n",
      "  warnings.warn('The output shape of `ResNet50(include_top=False)` '\n"
     ]
    }
   ],
   "source": [
    "'''Implementing this Stage 1 as mentioned in the Research paper'''\n",
    "\n",
    "input_tensor = Input(shape=(HEIGHT, WIDTH, CANAL))\n",
    "base_model = applications.ResNet50(weights=None, include_top=False,input_tensor=input_tensor)\n",
    "base_model.load_weights('resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5')\n",
    "\n",
    "x1 = GlobalAveragePooling2D()(base_model.output)\n",
    "x1 = BatchNormalization()(x1)\n",
    "\n",
    "x2 = GlobalMaxPooling2D()(base_model.output)\n",
    "x2 = BatchNormalization()(x2)\n",
    "\n",
    "x = concatenate([x1,x2])\n",
    "\n",
    "# Regression Head\n",
    "xr = Dense(2048, activation='relu')(x)\n",
    "xr = Dropout(0.5)(xr)\n",
    "xr = Dense(1,activation = 'linear',name = 'regression_output')(xr)\n",
    "\n",
    "# Classification Head\n",
    "xc = Dense(2048, activation='relu')(x)\n",
    "xc = Dropout(0.5)(xc)\n",
    "xc = Dense(N_CLASSES,activation = 'softmax',name = 'classification_output')(xc)\n",
    "\n",
    "# Ordinal Regression Head\n",
    "xo = Dense(2048, activation='relu')(x)\n",
    "xo = Dropout(0.5)(xo)\n",
    "xo = Dense(N_CLASSES,activation = 'softmax',name = 'ordinal_regression_output')(xo)\n",
    "\n",
    "model = Model(inputs = [input_tensor], outputs = [xr,xc,xo])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "DehalZUM9Sf1",
    "outputId": "57eefeda-a4ef-415b-8bbb-ea14027c9f67"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_3 (InputLayer)            (None, 320, 320, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 326, 326, 3)  0           input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 160, 160, 64) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalization)   (None, 160, 160, 64) 256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_99 (Activation)      (None, 160, 160, 64) 0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, 162, 162, 64) 0           activation_99[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 80, 80, 64)   0           pool1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, 80, 80, 64)   4160        max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, 80, 80, 64)   256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_100 (Activation)     (None, 80, 80, 64)   0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, 80, 80, 64)   36928       activation_100[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, 80, 80, 64)   256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_101 (Activation)     (None, 80, 80, 64)   0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, 80, 80, 256)  16640       activation_101[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, 80, 80, 256)  16640       max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, 80, 80, 256)  1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, 80, 80, 256)  1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_33 (Add)                    (None, 80, 80, 256)  0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_102 (Activation)     (None, 80, 80, 256)  0           add_33[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, 80, 80, 64)   16448       activation_102[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, 80, 80, 64)   256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_103 (Activation)     (None, 80, 80, 64)   0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, 80, 80, 64)   36928       activation_103[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, 80, 80, 64)   256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_104 (Activation)     (None, 80, 80, 64)   0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, 80, 80, 256)  16640       activation_104[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, 80, 80, 256)  1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_34 (Add)                    (None, 80, 80, 256)  0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_102[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_105 (Activation)     (None, 80, 80, 256)  0           add_34[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, 80, 80, 64)   16448       activation_105[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, 80, 80, 64)   256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_106 (Activation)     (None, 80, 80, 64)   0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, 80, 80, 64)   36928       activation_106[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, 80, 80, 64)   256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_107 (Activation)     (None, 80, 80, 64)   0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, 80, 80, 256)  16640       activation_107[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, 80, 80, 256)  1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_35 (Add)                    (None, 80, 80, 256)  0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_105[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_108 (Activation)     (None, 80, 80, 256)  0           add_35[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, 40, 40, 128)  32896       activation_108[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, 40, 40, 128)  512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_109 (Activation)     (None, 40, 40, 128)  0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, 40, 40, 128)  147584      activation_109[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, 40, 40, 128)  512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_110 (Activation)     (None, 40, 40, 128)  0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, 40, 40, 512)  66048       activation_110[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, 40, 40, 512)  131584      activation_108[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, 40, 40, 512)  2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, 40, 40, 512)  2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_36 (Add)                    (None, 40, 40, 512)  0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_111 (Activation)     (None, 40, 40, 512)  0           add_36[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, 40, 40, 128)  65664       activation_111[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, 40, 40, 128)  512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_112 (Activation)     (None, 40, 40, 128)  0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, 40, 40, 128)  147584      activation_112[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, 40, 40, 128)  512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_113 (Activation)     (None, 40, 40, 128)  0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, 40, 40, 512)  66048       activation_113[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, 40, 40, 512)  2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_37 (Add)                    (None, 40, 40, 512)  0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_111[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_114 (Activation)     (None, 40, 40, 512)  0           add_37[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, 40, 40, 128)  65664       activation_114[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, 40, 40, 128)  512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_115 (Activation)     (None, 40, 40, 128)  0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, 40, 40, 128)  147584      activation_115[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, 40, 40, 128)  512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_116 (Activation)     (None, 40, 40, 128)  0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, 40, 40, 512)  66048       activation_116[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, 40, 40, 512)  2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_38 (Add)                    (None, 40, 40, 512)  0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_114[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_117 (Activation)     (None, 40, 40, 512)  0           add_38[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, 40, 40, 128)  65664       activation_117[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, 40, 40, 128)  512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_118 (Activation)     (None, 40, 40, 128)  0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, 40, 40, 128)  147584      activation_118[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, 40, 40, 128)  512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_119 (Activation)     (None, 40, 40, 128)  0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, 40, 40, 512)  66048       activation_119[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, 40, 40, 512)  2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_39 (Add)                    (None, 40, 40, 512)  0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_117[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_120 (Activation)     (None, 40, 40, 512)  0           add_39[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, 20, 20, 256)  131328      activation_120[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, 20, 20, 256)  1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_121 (Activation)     (None, 20, 20, 256)  0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, 20, 20, 256)  590080      activation_121[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, 20, 20, 256)  1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_122 (Activation)     (None, 20, 20, 256)  0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, 20, 20, 1024) 263168      activation_122[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, 20, 20, 1024) 525312      activation_120[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, 20, 20, 1024) 4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, 20, 20, 1024) 4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_40 (Add)                    (None, 20, 20, 1024) 0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_123 (Activation)     (None, 20, 20, 1024) 0           add_40[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, 20, 20, 256)  262400      activation_123[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, 20, 20, 256)  1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_124 (Activation)     (None, 20, 20, 256)  0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, 20, 20, 256)  590080      activation_124[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, 20, 20, 256)  1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_125 (Activation)     (None, 20, 20, 256)  0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, 20, 20, 1024) 263168      activation_125[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, 20, 20, 1024) 4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_41 (Add)                    (None, 20, 20, 1024) 0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_123[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_126 (Activation)     (None, 20, 20, 1024) 0           add_41[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, 20, 20, 256)  262400      activation_126[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, 20, 20, 256)  1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_127 (Activation)     (None, 20, 20, 256)  0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, 20, 20, 256)  590080      activation_127[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, 20, 20, 256)  1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_128 (Activation)     (None, 20, 20, 256)  0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, 20, 20, 1024) 263168      activation_128[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, 20, 20, 1024) 4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_42 (Add)                    (None, 20, 20, 1024) 0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_126[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_129 (Activation)     (None, 20, 20, 1024) 0           add_42[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, 20, 20, 256)  262400      activation_129[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, 20, 20, 256)  1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_130 (Activation)     (None, 20, 20, 256)  0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, 20, 20, 256)  590080      activation_130[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, 20, 20, 256)  1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_131 (Activation)     (None, 20, 20, 256)  0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, 20, 20, 1024) 263168      activation_131[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, 20, 20, 1024) 4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_43 (Add)                    (None, 20, 20, 1024) 0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_129[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_132 (Activation)     (None, 20, 20, 1024) 0           add_43[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, 20, 20, 256)  262400      activation_132[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, 20, 20, 256)  1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_133 (Activation)     (None, 20, 20, 256)  0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, 20, 20, 256)  590080      activation_133[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, 20, 20, 256)  1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_134 (Activation)     (None, 20, 20, 256)  0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, 20, 20, 1024) 263168      activation_134[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, 20, 20, 1024) 4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_44 (Add)                    (None, 20, 20, 1024) 0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_132[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_135 (Activation)     (None, 20, 20, 1024) 0           add_44[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2a (Conv2D)         (None, 20, 20, 256)  262400      activation_135[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2a (BatchNormalizati (None, 20, 20, 256)  1024        res4f_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_136 (Activation)     (None, 20, 20, 256)  0           bn4f_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2b (Conv2D)         (None, 20, 20, 256)  590080      activation_136[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2b (BatchNormalizati (None, 20, 20, 256)  1024        res4f_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_137 (Activation)     (None, 20, 20, 256)  0           bn4f_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2c (Conv2D)         (None, 20, 20, 1024) 263168      activation_137[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2c (BatchNormalizati (None, 20, 20, 1024) 4096        res4f_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_45 (Add)                    (None, 20, 20, 1024) 0           bn4f_branch2c[0][0]              \n",
      "                                                                 activation_135[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_138 (Activation)     (None, 20, 20, 1024) 0           add_45[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, 10, 10, 512)  524800      activation_138[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, 10, 10, 512)  2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_139 (Activation)     (None, 10, 10, 512)  0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, 10, 10, 512)  2359808     activation_139[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, 10, 10, 512)  2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_140 (Activation)     (None, 10, 10, 512)  0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, 10, 10, 2048) 1050624     activation_140[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, 10, 10, 2048) 2099200     activation_138[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, 10, 10, 2048) 8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, 10, 10, 2048) 8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_46 (Add)                    (None, 10, 10, 2048) 0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_141 (Activation)     (None, 10, 10, 2048) 0           add_46[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, 10, 10, 512)  1049088     activation_141[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, 10, 10, 512)  2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_142 (Activation)     (None, 10, 10, 512)  0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, 10, 10, 512)  2359808     activation_142[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, 10, 10, 512)  2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_143 (Activation)     (None, 10, 10, 512)  0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, 10, 10, 2048) 1050624     activation_143[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, 10, 10, 2048) 8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_47 (Add)                    (None, 10, 10, 2048) 0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_141[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_144 (Activation)     (None, 10, 10, 2048) 0           add_47[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, 10, 10, 512)  1049088     activation_144[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, 10, 10, 512)  2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_145 (Activation)     (None, 10, 10, 512)  0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, 10, 10, 512)  2359808     activation_145[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, 10, 10, 512)  2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_146 (Activation)     (None, 10, 10, 512)  0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, 10, 10, 2048) 1050624     activation_146[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, 10, 10, 2048) 8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_48 (Add)                    (None, 10, 10, 2048) 0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_144[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_147 (Activation)     (None, 10, 10, 2048) 0           add_48[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_3 (Glo (None, 2048)         0           activation_147[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling2d_3 (GlobalM (None, 2048)         0           activation_147[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 2048)         8192        global_average_pooling2d_3[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 2048)         8192        global_max_pooling2d_3[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 4096)         0           batch_normalization_5[0][0]      \n",
      "                                                                 batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dense_7 (Dense)                 (None, 2048)         8390656     concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_8 (Dense)                 (None, 2048)         8390656     concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_9 (Dense)                 (None, 2048)         8390656     concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_7 (Dropout)             (None, 2048)         0           dense_7[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_8 (Dropout)             (None, 2048)         0           dense_8[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_9 (Dropout)             (None, 2048)         0           dense_9[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "regression_output (Dense)       (None, 1)            2049        dropout_7[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "classification_output (Dense)   (None, 5)            10245       dropout_8[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "ordinal_regression_output (Dens (None, 5)            10245       dropout_9[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 48,798,603\n",
      "Trainable params: 48,737,291\n",
      "Non-trainable params: 61,312\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Train all Layers\n",
    "\n",
    "for layer in model.layers:\n",
    "    layer.trainable = True\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "i4NV7v51KIeR",
    "outputId": "77becb7a-618f-49e2-ad15-4689fb485a4c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91 22\n"
     ]
    }
   ],
   "source": [
    "STEP_SIZE_TRAIN = len(df_train_train)//BATCH_SIZE\n",
    "STEP_SIZE_VALID = len(df_train_test)//BATCH_SIZE\n",
    "print(STEP_SIZE_TRAIN,STEP_SIZE_VALID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AGk1j-cmEkCo"
   },
   "outputs": [],
   "source": [
    "''' This Code part includes Class & Function Implementatin of the Cosine Learning Rate Scheduler'''\n",
    "\n",
    "# ref  - https://github.com/dimitreOliveira/APTOS2019BlindnessDetection/blob/master/Best%20solution%20(Bronze%20medal%20-%20163rd%20place)/233%20-%20EfficientNetB5-Reg-Img224%200%2C5data%20Fold1.ipynb\n",
    "\n",
    "def cosine_decay_with_warmup(global_step,\n",
    "                             learning_rate_base,\n",
    "                             total_steps,\n",
    "                             warmup_learning_rate=0.0,\n",
    "                             warmup_steps=0,\n",
    "                             hold_base_rate_steps=0):\n",
    "    \"\"\"\n",
    "    Cosine decay schedule with warm up period.\n",
    "    In this schedule, the learning rate grows linearly from warmup_learning_rate\n",
    "    to learning_rate_base for warmup_steps, then transitions to a cosine decay\n",
    "    schedule.\n",
    "    :param global_step {int}: global step.\n",
    "    :param learning_rate_base {float}: base learning rate.\n",
    "    :param total_steps {int}: total number of training steps.\n",
    "    :param warmup_learning_rate {float}: initial learning rate for warm up. (default: {0.0}).\n",
    "    :param warmup_steps {int}: number of warmup steps. (default: {0}).\n",
    "    :param hold_base_rate_steps {int}: Optional number of steps to hold base learning rate before decaying. (default: {0}).\n",
    "    :param global_step {int}: global step.\n",
    "    :Returns : a float representing learning rate.\n",
    "    :Raises ValueError: if warmup_learning_rate is larger than learning_rate_base, or if warmup_steps is larger than total_steps.\n",
    "    \"\"\"\n",
    "\n",
    "    if total_steps < warmup_steps:\n",
    "        raise ValueError('total_steps must be larger or equal to warmup_steps.')\n",
    "    learning_rate = 0.5 * learning_rate_base * (1 + np.cos(\n",
    "        np.pi *\n",
    "        (global_step - warmup_steps - hold_base_rate_steps\n",
    "         ) / float(total_steps - warmup_steps - hold_base_rate_steps)))\n",
    "    if hold_base_rate_steps > 0:\n",
    "        learning_rate = np.where(global_step > warmup_steps + hold_base_rate_steps,\n",
    "                                 learning_rate, learning_rate_base)\n",
    "    if warmup_steps > 0:\n",
    "        if learning_rate_base < warmup_learning_rate:\n",
    "            raise ValueError('learning_rate_base must be larger or equal to warmup_learning_rate.')\n",
    "        slope = (learning_rate_base - warmup_learning_rate) / warmup_steps\n",
    "        warmup_rate = slope * global_step + warmup_learning_rate\n",
    "        learning_rate = np.where(global_step < warmup_steps, warmup_rate,\n",
    "                                 learning_rate)\n",
    "    return np.where(global_step > total_steps, 0.0, learning_rate)\n",
    "\n",
    "\n",
    "class WarmUpCosineDecayScheduler(Callback):\n",
    "    \"\"\"Cosine decay with warmup learning rate scheduler\"\"\"\n",
    "\n",
    "    def __init__(self,\n",
    "                 learning_rate_base,\n",
    "                 total_steps,\n",
    "                 global_step_init=0,\n",
    "                 warmup_learning_rate=0.0,\n",
    "                 warmup_steps=0,\n",
    "                 hold_base_rate_steps=0,\n",
    "                 verbose=0):\n",
    "        \"\"\"\n",
    "        Constructor for cosine decay with warmup learning rate scheduler.\n",
    "        :param learning_rate_base {float}: base learning rate.\n",
    "        :param total_steps {int}: total number of training steps.\n",
    "        :param global_step_init {int}: initial global step, e.g. from previous checkpoint.\n",
    "        :param warmup_learning_rate {float}: initial learning rate for warm up. (default: {0.0}).\n",
    "        :param warmup_steps {int}: number of warmup steps. (default: {0}).\n",
    "        :param hold_base_rate_steps {int}: Optional number of steps to hold base learning rate before decaying. (default: {0}).\n",
    "        :param verbose {int}: quiet, 1: update messages. (default: {0}).\n",
    "        \"\"\"\n",
    "\n",
    "        super(WarmUpCosineDecayScheduler, self).__init__()\n",
    "        self.learning_rate_base = learning_rate_base\n",
    "        self.total_steps = total_steps\n",
    "        self.global_step = global_step_init\n",
    "        self.warmup_learning_rate = warmup_learning_rate\n",
    "        self.warmup_steps = warmup_steps\n",
    "        self.hold_base_rate_steps = hold_base_rate_steps\n",
    "        self.verbose = verbose\n",
    "        self.learning_rates = []\n",
    "\n",
    "    def on_batch_end(self, batch, logs=None):\n",
    "        self.global_step = self.global_step + 1\n",
    "        lr = K.get_value(self.model.optimizer.lr)\n",
    "        self.learning_rates.append(lr)\n",
    "\n",
    "    def on_batch_begin(self, batch, logs=None):\n",
    "        lr = cosine_decay_with_warmup(global_step=self.global_step,\n",
    "                                      learning_rate_base=self.learning_rate_base,\n",
    "                                      total_steps=self.total_steps,\n",
    "                                      warmup_learning_rate=self.warmup_learning_rate,\n",
    "                                      warmup_steps=self.warmup_steps,\n",
    "                                      hold_base_rate_steps=self.hold_base_rate_steps)\n",
    "        K.set_value(self.model.optimizer.lr, lr)\n",
    "        if self.verbose > 0:\n",
    "            print('\\nBatch %02d: setting learning rate to %s.' % (self.global_step + 1, lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yzSnpqUmFYtD"
   },
   "outputs": [],
   "source": [
    "# Use Cosine LR Scheduler as callback\n",
    "\n",
    "cosine_lr = WarmUpCosineDecayScheduler(learning_rate_base = LEARNING_RATE,\n",
    "                                       total_steps=TOTAL_STEPS_1st,\n",
    "                                       warmup_learning_rate=0.0,\n",
    "                                       warmup_steps=TOTAL_STEPS_1st,\n",
    "                                       hold_base_rate_steps=(2 * STEP_SIZE))\n",
    "\n",
    "callback_list = [cosine_lr_1st]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 776
    },
    "colab_type": "code",
    "id": "Aki2wj75JNuA",
    "outputId": "e61b66a0-59c9-4bd7-c0eb-edfc0035dfab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:716: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n",
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:735: UserWarning: This ImageDataGenerator specifies `zca_whitening`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91/91 [==============================] - 1524s 17s/step - loss: 5.1883 - regression_output_loss: 1.7428 - classification_output_loss: 2.1430 - ordinal_regression_output_loss: 1.3026 - regression_output_accuracy: 0.1956 - classification_output_accuracy: 0.2493 - ordinal_regression_output_accuracy: 0.5438 - val_loss: 4.0071 - val_regression_output_loss: 1.1378 - val_classification_output_loss: 1.5767 - val_ordinal_regression_output_loss: 1.3871 - val_regression_output_accuracy: 0.1991 - val_classification_output_accuracy: 0.1898 - val_ordinal_regression_output_accuracy: 0.5231\n",
      "Epoch 2/20\n",
      "91/91 [==============================] - 233s 3s/step - loss: 4.3981 - regression_output_loss: 1.4690 - classification_output_loss: 1.7545 - ordinal_regression_output_loss: 1.1746 - regression_output_accuracy: 0.2237 - classification_output_accuracy: 0.3951 - ordinal_regression_output_accuracy: 0.5657 - val_loss: 4.1293 - val_regression_output_loss: 1.1813 - val_classification_output_loss: 1.7535 - val_ordinal_regression_output_loss: 1.2246 - val_regression_output_accuracy: 0.2050 - val_classification_output_accuracy: 0.2965 - val_ordinal_regression_output_accuracy: 0.4811\n",
      "Epoch 3/20\n",
      "91/91 [==============================] - 233s 3s/step - loss: 3.7510 - regression_output_loss: 1.3158 - classification_output_loss: 1.4536 - ordinal_regression_output_loss: 0.9817 - regression_output_accuracy: 0.2431 - classification_output_accuracy: 0.5083 - ordinal_regression_output_accuracy: 0.6202 - val_loss: 3.6815 - val_regression_output_loss: 1.2850 - val_classification_output_loss: 1.6249 - val_ordinal_regression_output_loss: 0.8262 - val_regression_output_accuracy: 0.2284 - val_classification_output_accuracy: 0.4074 - val_ordinal_regression_output_accuracy: 0.5772\n",
      "Epoch 4/20\n",
      "91/91 [==============================] - 233s 3s/step - loss: 3.4007 - regression_output_loss: 1.2339 - classification_output_loss: 1.2959 - ordinal_regression_output_loss: 0.8711 - regression_output_accuracy: 0.2462 - classification_output_accuracy: 0.5654 - ordinal_regression_output_accuracy: 0.6704 - val_loss: 3.8909 - val_regression_output_loss: 1.2679 - val_classification_output_loss: 1.9382 - val_ordinal_regression_output_loss: 0.7703 - val_regression_output_accuracy: 0.2191 - val_classification_output_accuracy: 0.4290 - val_ordinal_regression_output_accuracy: 0.7336\n",
      "Epoch 5/20\n",
      "91/91 [==============================] - 231s 3s/step - loss: 3.1984 - regression_output_loss: 1.2321 - classification_output_loss: 1.1672 - ordinal_regression_output_loss: 0.7990 - regression_output_accuracy: 0.2545 - classification_output_accuracy: 0.6191 - ordinal_regression_output_accuracy: 0.7022 - val_loss: 5.4479 - val_regression_output_loss: 1.1788 - val_classification_output_loss: 2.2913 - val_ordinal_regression_output_loss: 1.0087 - val_regression_output_accuracy: 0.1940 - val_classification_output_accuracy: 0.4385 - val_ordinal_regression_output_accuracy: 0.7580\n",
      "Epoch 6/20\n",
      "91/91 [==============================] - 233s 3s/step - loss: 3.0537 - regression_output_loss: 1.1670 - classification_output_loss: 1.1066 - ordinal_regression_output_loss: 0.7801 - regression_output_accuracy: 0.2843 - classification_output_accuracy: 0.6188 - ordinal_regression_output_accuracy: 0.7106 - val_loss: 4.4921 - val_regression_output_loss: 1.0824 - val_classification_output_loss: 2.1887 - val_ordinal_regression_output_loss: 0.8554 - val_regression_output_accuracy: 0.2685 - val_classification_output_accuracy: 0.4398 - val_ordinal_regression_output_accuracy: 0.7546\n",
      "Epoch 7/20\n",
      "91/91 [==============================] - 232s 3s/step - loss: 3.1659 - regression_output_loss: 1.2324 - classification_output_loss: 1.1599 - ordinal_regression_output_loss: 0.7738 - regression_output_accuracy: 0.2587 - classification_output_accuracy: 0.6091 - ordinal_regression_output_accuracy: 0.7144 - val_loss: 3.7538 - val_regression_output_loss: 1.1764 - val_classification_output_loss: 2.1236 - val_ordinal_regression_output_loss: 0.8300 - val_regression_output_accuracy: 0.2098 - val_classification_output_accuracy: 0.4479 - val_ordinal_regression_output_accuracy: 0.7360\n",
      "Epoch 8/20\n",
      "91/91 [==============================] - 233s 3s/step - loss: 3.0609 - regression_output_loss: 1.1830 - classification_output_loss: 1.1071 - ordinal_regression_output_loss: 0.7708 - regression_output_accuracy: 0.2680 - classification_output_accuracy: 0.6278 - ordinal_regression_output_accuracy: 0.7118 - val_loss: 3.7900 - val_regression_output_loss: 1.2193 - val_classification_output_loss: 1.8970 - val_ordinal_regression_output_loss: 0.8225 - val_regression_output_accuracy: 0.2485 - val_classification_output_accuracy: 0.4614 - val_ordinal_regression_output_accuracy: 0.6920\n",
      "Epoch 9/20\n",
      "91/91 [==============================] - 231s 3s/step - loss: 3.1663 - regression_output_loss: 1.2448 - classification_output_loss: 1.1410 - ordinal_regression_output_loss: 0.7805 - regression_output_accuracy: 0.2524 - classification_output_accuracy: 0.6219 - ordinal_regression_output_accuracy: 0.7075 - val_loss: 3.2800 - val_regression_output_loss: 1.2505 - val_classification_output_loss: 1.5863 - val_ordinal_regression_output_loss: 0.8027 - val_regression_output_accuracy: 0.2299 - val_classification_output_accuracy: 0.5247 - val_ordinal_regression_output_accuracy: 0.6815\n",
      "Epoch 10/20\n",
      "91/91 [==============================] - 232s 3s/step - loss: 3.0595 - regression_output_loss: 1.1836 - classification_output_loss: 1.0988 - ordinal_regression_output_loss: 0.7771 - regression_output_accuracy: 0.2780 - classification_output_accuracy: 0.6333 - ordinal_regression_output_accuracy: 0.7116 - val_loss: 3.3471 - val_regression_output_loss: 1.1571 - val_classification_output_loss: 1.4196 - val_ordinal_regression_output_loss: 0.7358 - val_regression_output_accuracy: 0.2618 - val_classification_output_accuracy: 0.5489 - val_ordinal_regression_output_accuracy: 0.6751\n",
      "Epoch 11/20\n",
      "91/91 [==============================] - 232s 3s/step - loss: 3.0657 - regression_output_loss: 1.1723 - classification_output_loss: 1.1267 - ordinal_regression_output_loss: 0.7667 - regression_output_accuracy: 0.2794 - classification_output_accuracy: 0.6101 - ordinal_regression_output_accuracy: 0.7148 - val_loss: 2.3704 - val_regression_output_loss: 0.9302 - val_classification_output_loss: 1.1411 - val_ordinal_regression_output_loss: 0.7102 - val_regression_output_accuracy: 0.3395 - val_classification_output_accuracy: 0.6188 - val_ordinal_regression_output_accuracy: 0.6750\n",
      "Epoch 12/20\n",
      "91/91 [==============================] - 232s 3s/step - loss: 3.0834 - regression_output_loss: 1.2290 - classification_output_loss: 1.0929 - ordinal_regression_output_loss: 0.7616 - regression_output_accuracy: 0.2531 - classification_output_accuracy: 0.6340 - ordinal_regression_output_accuracy: 0.7106 - val_loss: 2.3742 - val_regression_output_loss: 0.8289 - val_classification_output_loss: 1.0466 - val_ordinal_regression_output_loss: 0.6851 - val_regression_output_accuracy: 0.3644 - val_classification_output_accuracy: 0.6293 - val_ordinal_regression_output_accuracy: 0.6943\n",
      "Epoch 13/20\n",
      "91/91 [==============================] - 232s 3s/step - loss: 3.0937 - regression_output_loss: 1.2155 - classification_output_loss: 1.1012 - ordinal_regression_output_loss: 0.7769 - regression_output_accuracy: 0.2668 - classification_output_accuracy: 0.6295 - ordinal_regression_output_accuracy: 0.7079 - val_loss: 1.9974 - val_regression_output_loss: 0.8044 - val_classification_output_loss: 1.0234 - val_ordinal_regression_output_loss: 0.6549 - val_regression_output_accuracy: 0.3889 - val_classification_output_accuracy: 0.6404 - val_ordinal_regression_output_accuracy: 0.7034\n",
      "Epoch 14/20\n",
      "91/91 [==============================] - 234s 3s/step - loss: 3.1043 - regression_output_loss: 1.2112 - classification_output_loss: 1.1203 - ordinal_regression_output_loss: 0.7728 - regression_output_accuracy: 0.2659 - classification_output_accuracy: 0.6229 - ordinal_regression_output_accuracy: 0.7088 - val_loss: 2.0999 - val_regression_output_loss: 0.8312 - val_classification_output_loss: 1.0002 - val_ordinal_regression_output_loss: 0.6631 - val_regression_output_accuracy: 0.3781 - val_classification_output_accuracy: 0.6451 - val_ordinal_regression_output_accuracy: 0.6988\n",
      "Epoch 15/20\n",
      "91/91 [==============================] - 234s 3s/step - loss: 3.1577 - regression_output_loss: 1.2282 - classification_output_loss: 1.1371 - ordinal_regression_output_loss: 0.7925 - regression_output_accuracy: 0.2517 - classification_output_accuracy: 0.6174 - ordinal_regression_output_accuracy: 0.7084 - val_loss: 2.7068 - val_regression_output_loss: 0.8060 - val_classification_output_loss: 1.0136 - val_ordinal_regression_output_loss: 0.6829 - val_regression_output_accuracy: 0.4259 - val_classification_output_accuracy: 0.6467 - val_ordinal_regression_output_accuracy: 0.7013\n",
      "Epoch 16/20\n",
      "91/91 [==============================] - 234s 3s/step - loss: 3.1346 - regression_output_loss: 1.2293 - classification_output_loss: 1.1368 - ordinal_regression_output_loss: 0.7686 - regression_output_accuracy: 0.2680 - classification_output_accuracy: 0.6205 - ordinal_regression_output_accuracy: 0.7136 - val_loss: 2.6054 - val_regression_output_loss: 0.7906 - val_classification_output_loss: 1.0021 - val_ordinal_regression_output_loss: 0.6772 - val_regression_output_accuracy: 0.3981 - val_classification_output_accuracy: 0.6451 - val_ordinal_regression_output_accuracy: 0.7049\n",
      "Epoch 17/20\n",
      "91/91 [==============================] - 232s 3s/step - loss: 3.0998 - regression_output_loss: 1.2101 - classification_output_loss: 1.1175 - ordinal_regression_output_loss: 0.7720 - regression_output_accuracy: 0.2663 - classification_output_accuracy: 0.6167 - ordinal_regression_output_accuracy: 0.7101 - val_loss: 2.6601 - val_regression_output_loss: 0.7803 - val_classification_output_loss: 0.9965 - val_ordinal_regression_output_loss: 0.6467 - val_regression_output_accuracy: 0.4069 - val_classification_output_accuracy: 0.6262 - val_ordinal_regression_output_accuracy: 0.7110\n",
      "Epoch 18/20\n",
      "91/91 [==============================] - 232s 3s/step - loss: 3.1085 - regression_output_loss: 1.2151 - classification_output_loss: 1.1135 - ordinal_regression_output_loss: 0.7800 - regression_output_accuracy: 0.2664 - classification_output_accuracy: 0.6274 - ordinal_regression_output_accuracy: 0.7123 - val_loss: 2.2384 - val_regression_output_loss: 0.7755 - val_classification_output_loss: 1.0271 - val_ordinal_regression_output_loss: 0.6703 - val_regression_output_accuracy: 0.4259 - val_classification_output_accuracy: 0.6373 - val_ordinal_regression_output_accuracy: 0.7096\n",
      "Epoch 19/20\n",
      "91/91 [==============================] - 234s 3s/step - loss: 3.1094 - regression_output_loss: 1.2162 - classification_output_loss: 1.1257 - ordinal_regression_output_loss: 0.7674 - regression_output_accuracy: 0.2732 - classification_output_accuracy: 0.6240 - ordinal_regression_output_accuracy: 0.7088 - val_loss: 2.6748 - val_regression_output_loss: 0.7974 - val_classification_output_loss: 1.0171 - val_ordinal_regression_output_loss: 0.6713 - val_regression_output_accuracy: 0.4167 - val_classification_output_accuracy: 0.6404 - val_ordinal_regression_output_accuracy: 0.7120\n",
      "Epoch 20/20\n",
      "91/91 [==============================] - 233s 3s/step - loss: 3.1323 - regression_output_loss: 1.2185 - classification_output_loss: 1.1430 - ordinal_regression_output_loss: 0.7706 - regression_output_accuracy: 0.2590 - classification_output_accuracy: 0.6212 - ordinal_regression_output_accuracy: 0.7084 - val_loss: 2.3598 - val_regression_output_loss: 0.8361 - val_classification_output_loss: 0.9844 - val_ordinal_regression_output_loss: 0.6649 - val_regression_output_accuracy: 0.3943 - val_classification_output_accuracy: 0.6483 - val_ordinal_regression_output_accuracy: 0.7126\n"
     ]
    }
   ],
   "source": [
    "# ref - https://keras.io/getting-started/functional-api-guide/\n",
    "\n",
    "model.compile(optimizer = optimizers.SGD(lr=LEARNING_RATE),\n",
    "              loss={'regression_output': 'mean_absolute_error', \n",
    "                    'classification_output': 'categorical_crossentropy',\n",
    "                    'ordinal_regression_output' : 'binary_crossentropy'\n",
    "                    },\n",
    "              metrics = ['accuracy'])\n",
    "\n",
    "history = model.fit_generator(generator=train_generator,\n",
    "                              steps_per_epoch=STEP_SIZE_TRAIN,\n",
    "                              validation_data=valid_generator,\n",
    "                              validation_steps=STEP_SIZE_VALID,\n",
    "                              epochs=20,\n",
    "                              callbacks = callback_list,\n",
    "                              verbose=1).history\n",
    "\n",
    "model.save(\"model_pre_training.h5\")\n",
    "f = open(\"history_pre_training\",\"wb\")\n",
    "pickle.dump(history,f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "colab_type": "code",
    "id": "j2rduAwJeWFF",
    "outputId": "045fe918-d3a0-4ae6-ace3-5053424d3811"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.plot(history['regression_output_loss'])\n",
    "plt.plot(history['val_regression_output_loss'])\n",
    "plt.title('Regression Model Loss - Pre Training')\n",
    "plt.ylabel('Loss (MAE)')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='best')\n",
    "plt.xticks(range(1,21))\n",
    "plt.gca().ticklabel_format(axis='both', style='plain', useOffset=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "colab_type": "code",
    "id": "-MhYaj8VfbGo",
    "outputId": "b27560ce-7a28-40cb-a0bb-1065d6aa4b46"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.plot(history['classification_output_loss'])\n",
    "plt.plot(history['val_classification_output_loss'])\n",
    "plt.title('Classification Model Loss - Pre Training')\n",
    "plt.ylabel('Loss (Categorical Cross Entropy)')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='best')\n",
    "plt.xticks(range(1,21))\n",
    "plt.gca().ticklabel_format(axis='both', style='plain', useOffset=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "colab_type": "code",
    "id": "Cr75LfqLfbs1",
    "outputId": "79d1272d-310b-4bb8-9440-16d0f15464ea"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.plot(history['ordinal_regression_output_loss'])\n",
    "plt.plot(history['val_ordinal_regression_output_loss'])\n",
    "plt.title('Ordinal Regression Model Loss - Pre Training')\n",
    "plt.ylabel('Loss (Binary Cross Entropy)')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='best')\n",
    "plt.xticks(range(1,21))\n",
    "plt.gca().ticklabel_format(axis='both', style='plain', useOffset=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WnYIX-05RkIb"
   },
   "source": [
    "### <font color='red'> 15.5 Stage 2 (Main Training) </font>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "CF2lgRdKQPbn",
    "outputId": "318d7da7-3581-49cc-a2a6-026150bdbdbe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_3 (InputLayer)            (None, 320, 320, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 326, 326, 3)  0           input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 160, 160, 64) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalization)   (None, 160, 160, 64) 256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_99 (Activation)      (None, 160, 160, 64) 0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, 162, 162, 64) 0           activation_99[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 80, 80, 64)   0           pool1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, 80, 80, 64)   4160        max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, 80, 80, 64)   256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_100 (Activation)     (None, 80, 80, 64)   0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, 80, 80, 64)   36928       activation_100[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, 80, 80, 64)   256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_101 (Activation)     (None, 80, 80, 64)   0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, 80, 80, 256)  16640       activation_101[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, 80, 80, 256)  16640       max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, 80, 80, 256)  1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, 80, 80, 256)  1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_33 (Add)                    (None, 80, 80, 256)  0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_102 (Activation)     (None, 80, 80, 256)  0           add_33[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, 80, 80, 64)   16448       activation_102[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, 80, 80, 64)   256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_103 (Activation)     (None, 80, 80, 64)   0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, 80, 80, 64)   36928       activation_103[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, 80, 80, 64)   256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_104 (Activation)     (None, 80, 80, 64)   0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, 80, 80, 256)  16640       activation_104[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, 80, 80, 256)  1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_34 (Add)                    (None, 80, 80, 256)  0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_102[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_105 (Activation)     (None, 80, 80, 256)  0           add_34[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, 80, 80, 64)   16448       activation_105[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, 80, 80, 64)   256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_106 (Activation)     (None, 80, 80, 64)   0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, 80, 80, 64)   36928       activation_106[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, 80, 80, 64)   256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_107 (Activation)     (None, 80, 80, 64)   0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, 80, 80, 256)  16640       activation_107[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, 80, 80, 256)  1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_35 (Add)                    (None, 80, 80, 256)  0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_105[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_108 (Activation)     (None, 80, 80, 256)  0           add_35[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, 40, 40, 128)  32896       activation_108[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, 40, 40, 128)  512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_109 (Activation)     (None, 40, 40, 128)  0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, 40, 40, 128)  147584      activation_109[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, 40, 40, 128)  512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_110 (Activation)     (None, 40, 40, 128)  0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, 40, 40, 512)  66048       activation_110[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, 40, 40, 512)  131584      activation_108[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, 40, 40, 512)  2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, 40, 40, 512)  2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_36 (Add)                    (None, 40, 40, 512)  0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_111 (Activation)     (None, 40, 40, 512)  0           add_36[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, 40, 40, 128)  65664       activation_111[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, 40, 40, 128)  512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_112 (Activation)     (None, 40, 40, 128)  0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, 40, 40, 128)  147584      activation_112[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, 40, 40, 128)  512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_113 (Activation)     (None, 40, 40, 128)  0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, 40, 40, 512)  66048       activation_113[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, 40, 40, 512)  2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_37 (Add)                    (None, 40, 40, 512)  0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_111[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_114 (Activation)     (None, 40, 40, 512)  0           add_37[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, 40, 40, 128)  65664       activation_114[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, 40, 40, 128)  512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_115 (Activation)     (None, 40, 40, 128)  0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, 40, 40, 128)  147584      activation_115[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, 40, 40, 128)  512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_116 (Activation)     (None, 40, 40, 128)  0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, 40, 40, 512)  66048       activation_116[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, 40, 40, 512)  2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_38 (Add)                    (None, 40, 40, 512)  0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_114[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_117 (Activation)     (None, 40, 40, 512)  0           add_38[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, 40, 40, 128)  65664       activation_117[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, 40, 40, 128)  512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_118 (Activation)     (None, 40, 40, 128)  0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, 40, 40, 128)  147584      activation_118[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, 40, 40, 128)  512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_119 (Activation)     (None, 40, 40, 128)  0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, 40, 40, 512)  66048       activation_119[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, 40, 40, 512)  2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_39 (Add)                    (None, 40, 40, 512)  0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_117[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_120 (Activation)     (None, 40, 40, 512)  0           add_39[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, 20, 20, 256)  131328      activation_120[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, 20, 20, 256)  1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_121 (Activation)     (None, 20, 20, 256)  0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, 20, 20, 256)  590080      activation_121[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, 20, 20, 256)  1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_122 (Activation)     (None, 20, 20, 256)  0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, 20, 20, 1024) 263168      activation_122[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, 20, 20, 1024) 525312      activation_120[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, 20, 20, 1024) 4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, 20, 20, 1024) 4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_40 (Add)                    (None, 20, 20, 1024) 0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_123 (Activation)     (None, 20, 20, 1024) 0           add_40[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, 20, 20, 256)  262400      activation_123[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, 20, 20, 256)  1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_124 (Activation)     (None, 20, 20, 256)  0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, 20, 20, 256)  590080      activation_124[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, 20, 20, 256)  1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_125 (Activation)     (None, 20, 20, 256)  0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, 20, 20, 1024) 263168      activation_125[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, 20, 20, 1024) 4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_41 (Add)                    (None, 20, 20, 1024) 0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_123[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_126 (Activation)     (None, 20, 20, 1024) 0           add_41[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, 20, 20, 256)  262400      activation_126[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, 20, 20, 256)  1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_127 (Activation)     (None, 20, 20, 256)  0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, 20, 20, 256)  590080      activation_127[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, 20, 20, 256)  1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_128 (Activation)     (None, 20, 20, 256)  0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, 20, 20, 1024) 263168      activation_128[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, 20, 20, 1024) 4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_42 (Add)                    (None, 20, 20, 1024) 0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_126[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_129 (Activation)     (None, 20, 20, 1024) 0           add_42[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, 20, 20, 256)  262400      activation_129[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, 20, 20, 256)  1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_130 (Activation)     (None, 20, 20, 256)  0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, 20, 20, 256)  590080      activation_130[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, 20, 20, 256)  1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_131 (Activation)     (None, 20, 20, 256)  0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, 20, 20, 1024) 263168      activation_131[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, 20, 20, 1024) 4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_43 (Add)                    (None, 20, 20, 1024) 0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_129[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_132 (Activation)     (None, 20, 20, 1024) 0           add_43[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, 20, 20, 256)  262400      activation_132[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, 20, 20, 256)  1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_133 (Activation)     (None, 20, 20, 256)  0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, 20, 20, 256)  590080      activation_133[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, 20, 20, 256)  1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_134 (Activation)     (None, 20, 20, 256)  0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, 20, 20, 1024) 263168      activation_134[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, 20, 20, 1024) 4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_44 (Add)                    (None, 20, 20, 1024) 0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_132[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_135 (Activation)     (None, 20, 20, 1024) 0           add_44[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2a (Conv2D)         (None, 20, 20, 256)  262400      activation_135[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2a (BatchNormalizati (None, 20, 20, 256)  1024        res4f_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_136 (Activation)     (None, 20, 20, 256)  0           bn4f_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2b (Conv2D)         (None, 20, 20, 256)  590080      activation_136[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2b (BatchNormalizati (None, 20, 20, 256)  1024        res4f_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_137 (Activation)     (None, 20, 20, 256)  0           bn4f_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2c (Conv2D)         (None, 20, 20, 1024) 263168      activation_137[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2c (BatchNormalizati (None, 20, 20, 1024) 4096        res4f_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_45 (Add)                    (None, 20, 20, 1024) 0           bn4f_branch2c[0][0]              \n",
      "                                                                 activation_135[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_138 (Activation)     (None, 20, 20, 1024) 0           add_45[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, 10, 10, 512)  524800      activation_138[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, 10, 10, 512)  2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_139 (Activation)     (None, 10, 10, 512)  0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, 10, 10, 512)  2359808     activation_139[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, 10, 10, 512)  2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_140 (Activation)     (None, 10, 10, 512)  0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, 10, 10, 2048) 1050624     activation_140[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, 10, 10, 2048) 2099200     activation_138[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, 10, 10, 2048) 8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, 10, 10, 2048) 8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_46 (Add)                    (None, 10, 10, 2048) 0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_141 (Activation)     (None, 10, 10, 2048) 0           add_46[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, 10, 10, 512)  1049088     activation_141[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, 10, 10, 512)  2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_142 (Activation)     (None, 10, 10, 512)  0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, 10, 10, 512)  2359808     activation_142[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, 10, 10, 512)  2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_143 (Activation)     (None, 10, 10, 512)  0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, 10, 10, 2048) 1050624     activation_143[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, 10, 10, 2048) 8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_47 (Add)                    (None, 10, 10, 2048) 0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_141[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_144 (Activation)     (None, 10, 10, 2048) 0           add_47[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, 10, 10, 512)  1049088     activation_144[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, 10, 10, 512)  2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_145 (Activation)     (None, 10, 10, 512)  0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, 10, 10, 512)  2359808     activation_145[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, 10, 10, 512)  2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_146 (Activation)     (None, 10, 10, 512)  0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, 10, 10, 2048) 1050624     activation_146[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, 10, 10, 2048) 8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_48 (Add)                    (None, 10, 10, 2048) 0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_144[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_147 (Activation)     (None, 10, 10, 2048) 0           add_48[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_3 (Glo (None, 2048)         0           activation_147[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling2d_3 (GlobalM (None, 2048)         0           activation_147[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 2048)         8192        global_average_pooling2d_3[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 2048)         8192        global_max_pooling2d_3[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 4096)         0           batch_normalization_5[0][0]      \n",
      "                                                                 batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dense_7 (Dense)                 (None, 2048)         8390656     concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_8 (Dense)                 (None, 2048)         8390656     concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_9 (Dense)                 (None, 2048)         8390656     concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_7 (Dropout)             (None, 2048)         0           dense_7[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_8 (Dropout)             (None, 2048)         0           dense_8[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_9 (Dropout)             (None, 2048)         0           dense_9[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "regression_output (Dense)       (None, 1)            2049        dropout_7[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "classification_output (Dense)   (None, 5)            10245       dropout_8[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "ordinal_regression_output (Dens (None, 5)            10245       dropout_9[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 72,333,195\n",
      "Trainable params: 48,737,291\n",
      "Non-trainable params: 23,595,904\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:297: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "  'Discrepancy between trainable weights and collected trainable'\n"
     ]
    }
   ],
   "source": [
    "# Freezing the Encoder Layers Training..(Only Last 14 layers are trainable) for 5 Epochs (Warming up the Weights for Main Training)\n",
    "\n",
    "for layer in model.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "for i in range(-14,0):\n",
    "  model.layers[i].trainable = True\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5dJgeOMnRl3G"
   },
   "outputs": [],
   "source": [
    "# ref - https://github.com/umbertogriffo/focal-loss-keras/blob/master/losses.py\n",
    "\n",
    "'''Below Functions create custom loss functions - Categorical Focal Loss, Binary Focal Loss'''\n",
    "\n",
    "def binary_focal_loss(gamma=2., alpha=.25):\n",
    "    \"\"\"\n",
    "    Binary form of focal loss.\n",
    "      FL(p_t) = -alpha * (1 - p_t)**gamma * log(p_t)\n",
    "      where p = sigmoid(x), p_t = p or 1 - p depending on if the label is 1 or 0, respectively.\n",
    "    References:\n",
    "        https://arxiv.org/pdf/1708.02002.pdf\n",
    "    Usage:\n",
    "     model.compile(loss=[binary_focal_loss(alpha=.25, gamma=2)], metrics=[\"accuracy\"], optimizer=adam)\n",
    "    \"\"\"\n",
    "    def binary_focal_loss_fixed(y_true, y_pred):\n",
    "        \"\"\"\n",
    "        :param y_true: A tensor of the same shape as `y_pred`\n",
    "        :param y_pred:  A tensor resulting from a sigmoid\n",
    "        :return: Output tensor.\n",
    "        \"\"\"\n",
    "        pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))\n",
    "        pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))\n",
    "\n",
    "        epsilon = K.epsilon()\n",
    "        # clip to prevent NaN's and Inf's\n",
    "        pt_1 = K.clip(pt_1, epsilon, 1. - epsilon)\n",
    "        pt_0 = K.clip(pt_0, epsilon, 1. - epsilon)\n",
    "\n",
    "        return -K.mean(alpha * K.pow(1. - pt_1, gamma) * K.log(pt_1)) \\\n",
    "               -K.mean((1 - alpha) * K.pow(pt_0, gamma) * K.log(1. - pt_0))\n",
    "\n",
    "    return binary_focal_loss_fixed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JDC7DXOGtaa-"
   },
   "outputs": [],
   "source": [
    "def categorical_focal_loss(gamma=2., alpha=.25):\n",
    "    \"\"\"\n",
    "    Softmax version of focal loss.\n",
    "           m\n",
    "      FL = ∑  -alpha * (1 - p_o,c)^gamma * y_o,c * log(p_o,c)\n",
    "          c=1\n",
    "      where m = number of classes, c = class and o = observation\n",
    "    Parameters:\n",
    "      alpha -- the same as weighing factor in balanced cross entropy\n",
    "      gamma -- focusing parameter for modulating factor (1-p)\n",
    "    Default value:\n",
    "      gamma -- 2.0 as mentioned in the paper\n",
    "      alpha -- 0.25 as mentioned in the paper\n",
    "    References:\n",
    "        Official paper: https://arxiv.org/pdf/1708.02002.pdf\n",
    "        https://www.tensorflow.org/api_docs/python/tf/keras/backend/categorical_crossentropy\n",
    "    Usage:\n",
    "     model.compile(loss=[categorical_focal_loss(alpha=.25, gamma=2)], metrics=[\"accuracy\"], optimizer=adam)\n",
    "    \"\"\"\n",
    "    def categorical_focal_loss_fixed(y_true, y_pred):\n",
    "        \"\"\"\n",
    "        :param y_true: A tensor of the same shape as `y_pred`\n",
    "        :param y_pred: A tensor resulting from a softmax\n",
    "        :return: Output tensor.\n",
    "        \"\"\"\n",
    "\n",
    "        # Scale predictions so that the class probas of each sample sum to 1\n",
    "        y_pred /= K.sum(y_pred, axis=-1, keepdims=True)\n",
    "\n",
    "        # Clip the prediction value to prevent NaN's and Inf's\n",
    "        epsilon = K.epsilon()\n",
    "        y_pred = K.clip(y_pred, epsilon, 1. - epsilon)\n",
    "\n",
    "        # Calculate Cross Entropy\n",
    "        cross_entropy = -y_true * K.log(y_pred)\n",
    "\n",
    "        # Calculate Focal Loss\n",
    "        loss = alpha * K.pow(1 - y_pred, gamma) * cross_entropy\n",
    "\n",
    "        # Compute mean loss in mini_batch\n",
    "        return K.mean(loss, axis=1)\n",
    "\n",
    "    return categorical_focal_loss_fixed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
    },
    "colab_type": "code",
    "id": "tyP6URxCtk_G",
    "outputId": "30cc874e-4bea-4ab2-8c4f-dd541d062c23"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:716: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n",
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:735: UserWarning: This ImageDataGenerator specifies `zca_whitening`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91/91 [==============================] - 148s 2s/step - loss: 2.5590 - regression_output_loss: 2.3655 - classification_output_loss: 0.0419 - ordinal_regression_output_loss: 0.1518 - regression_output_accuracy: 0.2552 - classification_output_accuracy: 0.6226 - ordinal_regression_output_accuracy: 0.6634 - val_loss: 1.7189 - val_regression_output_loss: 1.1061 - val_classification_output_loss: 0.0382 - val_ordinal_regression_output_loss: 0.1158 - val_regression_output_accuracy: 0.3981 - val_classification_output_accuracy: 0.6188 - val_ordinal_regression_output_accuracy: 0.7685\n",
      "Epoch 2/5\n",
      "91/91 [==============================] - 128s 1s/step - loss: 2.6535 - regression_output_loss: 2.4601 - classification_output_loss: 0.0431 - ordinal_regression_output_loss: 0.1503 - regression_output_accuracy: 0.2615 - classification_output_accuracy: 0.6271 - ordinal_regression_output_accuracy: 0.6781 - val_loss: 1.4155 - val_regression_output_loss: 1.1397 - val_classification_output_loss: 0.0335 - val_ordinal_regression_output_loss: 0.1182 - val_regression_output_accuracy: 0.3912 - val_classification_output_accuracy: 0.6593 - val_ordinal_regression_output_accuracy: 0.7555\n",
      "Epoch 3/5\n",
      "91/91 [==============================] - 128s 1s/step - loss: 2.6969 - regression_output_loss: 2.5007 - classification_output_loss: 0.0412 - ordinal_regression_output_loss: 0.1551 - regression_output_accuracy: 0.2573 - classification_output_accuracy: 0.6333 - ordinal_regression_output_accuracy: 0.6877 - val_loss: 1.1441 - val_regression_output_loss: 1.1301 - val_classification_output_loss: 0.0364 - val_ordinal_regression_output_loss: 0.1143 - val_regression_output_accuracy: 0.3935 - val_classification_output_accuracy: 0.6358 - val_ordinal_regression_output_accuracy: 0.7778\n",
      "Epoch 4/5\n",
      "91/91 [==============================] - 130s 1s/step - loss: 2.5449 - regression_output_loss: 2.3492 - classification_output_loss: 0.0418 - ordinal_regression_output_loss: 0.1539 - regression_output_accuracy: 0.2524 - classification_output_accuracy: 0.6188 - ordinal_regression_output_accuracy: 0.6742 - val_loss: 1.3834 - val_regression_output_loss: 1.0916 - val_classification_output_loss: 0.0343 - val_ordinal_regression_output_loss: 0.1150 - val_regression_output_accuracy: 0.4054 - val_classification_output_accuracy: 0.6451 - val_ordinal_regression_output_accuracy: 0.7902\n",
      "Epoch 5/5\n",
      "91/91 [==============================] - 130s 1s/step - loss: 2.4767 - regression_output_loss: 2.2821 - classification_output_loss: 0.0425 - ordinal_regression_output_loss: 0.1521 - regression_output_accuracy: 0.2684 - classification_output_accuracy: 0.6236 - ordinal_regression_output_accuracy: 0.6790 - val_loss: 1.0212 - val_regression_output_loss: 1.0832 - val_classification_output_loss: 0.0363 - val_ordinal_regression_output_loss: 0.1151 - val_regression_output_accuracy: 0.4090 - val_classification_output_accuracy: 0.6451 - val_ordinal_regression_output_accuracy: 0.7793\n"
     ]
    }
   ],
   "source": [
    "# ref - https://keras.io/getting-started/functional-api-guide/\n",
    "\n",
    "model.compile(optimizer = optimizers.Adam(lr=WARMUP_LEARNING_RATE),\n",
    "              loss={'regression_output': mean_squared_error, \n",
    "                    'classification_output': categorical_focal_loss(alpha=.25, gamma=2) ,\n",
    "                    'ordinal_regression_output' : binary_focal_loss(alpha=.25, gamma=2)\n",
    "                    },\n",
    "              metrics = ['accuracy'])\n",
    "\n",
    "history = model.fit_generator(generator=train_generator,\n",
    "                              steps_per_epoch=STEP_SIZE_TRAIN,\n",
    "                              validation_data=valid_generator,\n",
    "                              validation_steps=STEP_SIZE_VALID,\n",
    "                              epochs=5,\n",
    "                              callbacks = callback_list,\n",
    "                              verbose=1).history\n",
    "\n",
    "model.save(\"model_main_training.h5\")\n",
    "f = open(\"history_main_training\",\"wb\")\n",
    "pickle.dump(history,f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "HGzThk-2QeP7",
    "outputId": "a1e74209-0ab0-468a-f3fb-03cde94e8311"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_3 (InputLayer)            (None, 320, 320, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 326, 326, 3)  0           input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 160, 160, 64) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalization)   (None, 160, 160, 64) 256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_99 (Activation)      (None, 160, 160, 64) 0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, 162, 162, 64) 0           activation_99[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 80, 80, 64)   0           pool1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, 80, 80, 64)   4160        max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, 80, 80, 64)   256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_100 (Activation)     (None, 80, 80, 64)   0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, 80, 80, 64)   36928       activation_100[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, 80, 80, 64)   256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_101 (Activation)     (None, 80, 80, 64)   0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, 80, 80, 256)  16640       activation_101[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, 80, 80, 256)  16640       max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, 80, 80, 256)  1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, 80, 80, 256)  1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_33 (Add)                    (None, 80, 80, 256)  0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_102 (Activation)     (None, 80, 80, 256)  0           add_33[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, 80, 80, 64)   16448       activation_102[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, 80, 80, 64)   256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_103 (Activation)     (None, 80, 80, 64)   0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, 80, 80, 64)   36928       activation_103[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, 80, 80, 64)   256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_104 (Activation)     (None, 80, 80, 64)   0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, 80, 80, 256)  16640       activation_104[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, 80, 80, 256)  1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_34 (Add)                    (None, 80, 80, 256)  0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_102[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_105 (Activation)     (None, 80, 80, 256)  0           add_34[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, 80, 80, 64)   16448       activation_105[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, 80, 80, 64)   256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_106 (Activation)     (None, 80, 80, 64)   0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, 80, 80, 64)   36928       activation_106[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, 80, 80, 64)   256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_107 (Activation)     (None, 80, 80, 64)   0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, 80, 80, 256)  16640       activation_107[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, 80, 80, 256)  1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_35 (Add)                    (None, 80, 80, 256)  0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_105[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_108 (Activation)     (None, 80, 80, 256)  0           add_35[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, 40, 40, 128)  32896       activation_108[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, 40, 40, 128)  512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_109 (Activation)     (None, 40, 40, 128)  0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, 40, 40, 128)  147584      activation_109[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, 40, 40, 128)  512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_110 (Activation)     (None, 40, 40, 128)  0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, 40, 40, 512)  66048       activation_110[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, 40, 40, 512)  131584      activation_108[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, 40, 40, 512)  2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, 40, 40, 512)  2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_36 (Add)                    (None, 40, 40, 512)  0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_111 (Activation)     (None, 40, 40, 512)  0           add_36[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, 40, 40, 128)  65664       activation_111[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, 40, 40, 128)  512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_112 (Activation)     (None, 40, 40, 128)  0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, 40, 40, 128)  147584      activation_112[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, 40, 40, 128)  512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_113 (Activation)     (None, 40, 40, 128)  0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, 40, 40, 512)  66048       activation_113[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, 40, 40, 512)  2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_37 (Add)                    (None, 40, 40, 512)  0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_111[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_114 (Activation)     (None, 40, 40, 512)  0           add_37[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, 40, 40, 128)  65664       activation_114[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, 40, 40, 128)  512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_115 (Activation)     (None, 40, 40, 128)  0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, 40, 40, 128)  147584      activation_115[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, 40, 40, 128)  512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_116 (Activation)     (None, 40, 40, 128)  0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, 40, 40, 512)  66048       activation_116[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, 40, 40, 512)  2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_38 (Add)                    (None, 40, 40, 512)  0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_114[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_117 (Activation)     (None, 40, 40, 512)  0           add_38[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, 40, 40, 128)  65664       activation_117[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, 40, 40, 128)  512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_118 (Activation)     (None, 40, 40, 128)  0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, 40, 40, 128)  147584      activation_118[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, 40, 40, 128)  512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_119 (Activation)     (None, 40, 40, 128)  0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, 40, 40, 512)  66048       activation_119[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, 40, 40, 512)  2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_39 (Add)                    (None, 40, 40, 512)  0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_117[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_120 (Activation)     (None, 40, 40, 512)  0           add_39[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, 20, 20, 256)  131328      activation_120[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, 20, 20, 256)  1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_121 (Activation)     (None, 20, 20, 256)  0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, 20, 20, 256)  590080      activation_121[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, 20, 20, 256)  1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_122 (Activation)     (None, 20, 20, 256)  0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, 20, 20, 1024) 263168      activation_122[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, 20, 20, 1024) 525312      activation_120[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, 20, 20, 1024) 4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, 20, 20, 1024) 4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_40 (Add)                    (None, 20, 20, 1024) 0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_123 (Activation)     (None, 20, 20, 1024) 0           add_40[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, 20, 20, 256)  262400      activation_123[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, 20, 20, 256)  1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_124 (Activation)     (None, 20, 20, 256)  0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, 20, 20, 256)  590080      activation_124[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, 20, 20, 256)  1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_125 (Activation)     (None, 20, 20, 256)  0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, 20, 20, 1024) 263168      activation_125[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, 20, 20, 1024) 4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_41 (Add)                    (None, 20, 20, 1024) 0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_123[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_126 (Activation)     (None, 20, 20, 1024) 0           add_41[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, 20, 20, 256)  262400      activation_126[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, 20, 20, 256)  1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_127 (Activation)     (None, 20, 20, 256)  0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, 20, 20, 256)  590080      activation_127[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, 20, 20, 256)  1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_128 (Activation)     (None, 20, 20, 256)  0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, 20, 20, 1024) 263168      activation_128[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, 20, 20, 1024) 4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_42 (Add)                    (None, 20, 20, 1024) 0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_126[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_129 (Activation)     (None, 20, 20, 1024) 0           add_42[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, 20, 20, 256)  262400      activation_129[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, 20, 20, 256)  1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_130 (Activation)     (None, 20, 20, 256)  0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, 20, 20, 256)  590080      activation_130[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, 20, 20, 256)  1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_131 (Activation)     (None, 20, 20, 256)  0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, 20, 20, 1024) 263168      activation_131[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, 20, 20, 1024) 4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_43 (Add)                    (None, 20, 20, 1024) 0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_129[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_132 (Activation)     (None, 20, 20, 1024) 0           add_43[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, 20, 20, 256)  262400      activation_132[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, 20, 20, 256)  1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_133 (Activation)     (None, 20, 20, 256)  0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, 20, 20, 256)  590080      activation_133[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, 20, 20, 256)  1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_134 (Activation)     (None, 20, 20, 256)  0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, 20, 20, 1024) 263168      activation_134[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, 20, 20, 1024) 4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_44 (Add)                    (None, 20, 20, 1024) 0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_132[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_135 (Activation)     (None, 20, 20, 1024) 0           add_44[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2a (Conv2D)         (None, 20, 20, 256)  262400      activation_135[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2a (BatchNormalizati (None, 20, 20, 256)  1024        res4f_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_136 (Activation)     (None, 20, 20, 256)  0           bn4f_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2b (Conv2D)         (None, 20, 20, 256)  590080      activation_136[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2b (BatchNormalizati (None, 20, 20, 256)  1024        res4f_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_137 (Activation)     (None, 20, 20, 256)  0           bn4f_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2c (Conv2D)         (None, 20, 20, 1024) 263168      activation_137[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2c (BatchNormalizati (None, 20, 20, 1024) 4096        res4f_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_45 (Add)                    (None, 20, 20, 1024) 0           bn4f_branch2c[0][0]              \n",
      "                                                                 activation_135[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_138 (Activation)     (None, 20, 20, 1024) 0           add_45[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, 10, 10, 512)  524800      activation_138[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, 10, 10, 512)  2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_139 (Activation)     (None, 10, 10, 512)  0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, 10, 10, 512)  2359808     activation_139[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, 10, 10, 512)  2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_140 (Activation)     (None, 10, 10, 512)  0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, 10, 10, 2048) 1050624     activation_140[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, 10, 10, 2048) 2099200     activation_138[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, 10, 10, 2048) 8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, 10, 10, 2048) 8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_46 (Add)                    (None, 10, 10, 2048) 0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_141 (Activation)     (None, 10, 10, 2048) 0           add_46[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, 10, 10, 512)  1049088     activation_141[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, 10, 10, 512)  2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_142 (Activation)     (None, 10, 10, 512)  0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, 10, 10, 512)  2359808     activation_142[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, 10, 10, 512)  2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_143 (Activation)     (None, 10, 10, 512)  0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, 10, 10, 2048) 1050624     activation_143[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, 10, 10, 2048) 8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_47 (Add)                    (None, 10, 10, 2048) 0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_141[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_144 (Activation)     (None, 10, 10, 2048) 0           add_47[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, 10, 10, 512)  1049088     activation_144[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, 10, 10, 512)  2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_145 (Activation)     (None, 10, 10, 512)  0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, 10, 10, 512)  2359808     activation_145[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, 10, 10, 512)  2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_146 (Activation)     (None, 10, 10, 512)  0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, 10, 10, 2048) 1050624     activation_146[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, 10, 10, 2048) 8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_48 (Add)                    (None, 10, 10, 2048) 0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_144[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_147 (Activation)     (None, 10, 10, 2048) 0           add_48[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_3 (Glo (None, 2048)         0           activation_147[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling2d_3 (GlobalM (None, 2048)         0           activation_147[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 2048)         8192        global_average_pooling2d_3[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 2048)         8192        global_max_pooling2d_3[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 4096)         0           batch_normalization_5[0][0]      \n",
      "                                                                 batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dense_7 (Dense)                 (None, 2048)         8390656     concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_8 (Dense)                 (None, 2048)         8390656     concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_9 (Dense)                 (None, 2048)         8390656     concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_7 (Dropout)             (None, 2048)         0           dense_7[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_8 (Dropout)             (None, 2048)         0           dense_8[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_9 (Dropout)             (None, 2048)         0           dense_9[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "regression_output (Dense)       (None, 1)            2049        dropout_7[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "classification_output (Dense)   (None, 5)            10245       dropout_8[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "ordinal_regression_output (Dens (None, 5)            10245       dropout_9[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 25,264,011\n",
      "Trainable params: 25,202,699\n",
      "Non-trainable params: 61,312\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:297: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "  'Discrepancy between trainable weights and collected trainable'\n"
     ]
    }
   ],
   "source": [
    "# Now Unfreeze all Layers and make all layers as trainable = True (Train for 45 Epochs)\n",
    "\n",
    "for layer in model.layers:\n",
    "    layer.trainable = True\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "qpmGwpjCQec9",
    "outputId": "0a3e573e-e8b1-4767-8af4-881c30a979b6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/45\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:716: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n",
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:735: UserWarning: This ImageDataGenerator specifies `zca_whitening`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91/91 [==============================] - 276s 3s/step - loss: 2.6057 - regression_output_loss: 2.4114 - classification_output_loss: 0.0416 - ordinal_regression_output_loss: 0.1527 - regression_output_accuracy: 0.2708 - classification_output_accuracy: 0.6212 - ordinal_regression_output_accuracy: 0.6714 - val_loss: 1.3157 - val_regression_output_loss: 1.1729 - val_classification_output_loss: 0.0368 - val_ordinal_regression_output_loss: 0.1214 - val_regression_output_accuracy: 0.3991 - val_classification_output_accuracy: 0.6278 - val_ordinal_regression_output_accuracy: 0.7587\n",
      "Epoch 2/45\n",
      "91/91 [==============================] - 241s 3s/step - loss: 2.4529 - regression_output_loss: 2.2618 - classification_output_loss: 0.0424 - ordinal_regression_output_loss: 0.1487 - regression_output_accuracy: 0.2569 - classification_output_accuracy: 0.6229 - ordinal_regression_output_accuracy: 0.6735 - val_loss: 1.7067 - val_regression_output_loss: 1.1409 - val_classification_output_loss: 0.0369 - val_ordinal_regression_output_loss: 0.1155 - val_regression_output_accuracy: 0.3951 - val_classification_output_accuracy: 0.6281 - val_ordinal_regression_output_accuracy: 0.7438\n",
      "Epoch 3/45\n",
      "91/91 [==============================] - 241s 3s/step - loss: 2.5664 - regression_output_loss: 2.3732 - classification_output_loss: 0.0412 - ordinal_regression_output_loss: 0.1521 - regression_output_accuracy: 0.2735 - classification_output_accuracy: 0.6316 - ordinal_regression_output_accuracy: 0.6672 - val_loss: 2.0701 - val_regression_output_loss: 1.1213 - val_classification_output_loss: 0.0351 - val_ordinal_regression_output_loss: 0.1133 - val_regression_output_accuracy: 0.3997 - val_classification_output_accuracy: 0.6265 - val_ordinal_regression_output_accuracy: 0.7654\n",
      "Epoch 4/45\n",
      "91/91 [==============================] - 241s 3s/step - loss: 2.4378 - regression_output_loss: 2.2440 - classification_output_loss: 0.0422 - ordinal_regression_output_loss: 0.1515 - regression_output_accuracy: 0.2715 - classification_output_accuracy: 0.6191 - ordinal_regression_output_accuracy: 0.6863 - val_loss: 1.1621 - val_regression_output_loss: 1.0134 - val_classification_output_loss: 0.0353 - val_ordinal_regression_output_loss: 0.1176 - val_regression_output_accuracy: 0.4338 - val_classification_output_accuracy: 0.6230 - val_ordinal_regression_output_accuracy: 0.7681\n",
      "Epoch 5/45\n",
      "91/91 [==============================] - 239s 3s/step - loss: 2.4370 - regression_output_loss: 2.2472 - classification_output_loss: 0.0407 - ordinal_regression_output_loss: 0.1491 - regression_output_accuracy: 0.2776 - classification_output_accuracy: 0.6355 - ordinal_regression_output_accuracy: 0.6830 - val_loss: 1.0710 - val_regression_output_loss: 1.0957 - val_classification_output_loss: 0.0343 - val_ordinal_regression_output_loss: 0.1168 - val_regression_output_accuracy: 0.4074 - val_classification_output_accuracy: 0.6528 - val_ordinal_regression_output_accuracy: 0.7438\n",
      "Epoch 6/45\n",
      "91/91 [==============================] - 241s 3s/step - loss: 2.6006 - regression_output_loss: 2.4035 - classification_output_loss: 0.0441 - ordinal_regression_output_loss: 0.1530 - regression_output_accuracy: 0.2777 - classification_output_accuracy: 0.6174 - ordinal_regression_output_accuracy: 0.6714 - val_loss: 1.8871 - val_regression_output_loss: 1.1121 - val_classification_output_loss: 0.0347 - val_ordinal_regression_output_loss: 0.1129 - val_regression_output_accuracy: 0.4211 - val_classification_output_accuracy: 0.6167 - val_ordinal_regression_output_accuracy: 0.7713\n",
      "Epoch 7/45\n",
      "91/91 [==============================] - 242s 3s/step - loss: 2.5434 - regression_output_loss: 2.3441 - classification_output_loss: 0.0439 - ordinal_regression_output_loss: 0.1553 - regression_output_accuracy: 0.2732 - classification_output_accuracy: 0.6184 - ordinal_regression_output_accuracy: 0.6641 - val_loss: 1.1593 - val_regression_output_loss: 1.1086 - val_classification_output_loss: 0.0356 - val_ordinal_regression_output_loss: 0.1171 - val_regression_output_accuracy: 0.4059 - val_classification_output_accuracy: 0.6451 - val_ordinal_regression_output_accuracy: 0.7840\n",
      "Epoch 8/45\n",
      "91/91 [==============================] - 242s 3s/step - loss: 2.3885 - regression_output_loss: 2.1927 - classification_output_loss: 0.0425 - ordinal_regression_output_loss: 0.1532 - regression_output_accuracy: 0.2718 - classification_output_accuracy: 0.6177 - ordinal_regression_output_accuracy: 0.6738 - val_loss: 1.5889 - val_regression_output_loss: 1.0249 - val_classification_output_loss: 0.0352 - val_ordinal_regression_output_loss: 0.1232 - val_regression_output_accuracy: 0.4136 - val_classification_output_accuracy: 0.6265 - val_ordinal_regression_output_accuracy: 0.7870\n",
      "Epoch 9/45\n",
      "91/91 [==============================] - 240s 3s/step - loss: 2.5741 - regression_output_loss: 2.3783 - classification_output_loss: 0.0413 - ordinal_regression_output_loss: 0.1546 - regression_output_accuracy: 0.2577 - classification_output_accuracy: 0.6264 - ordinal_regression_output_accuracy: 0.6735 - val_loss: 1.3560 - val_regression_output_loss: 1.0342 - val_classification_output_loss: 0.0355 - val_ordinal_regression_output_loss: 0.1123 - val_regression_output_accuracy: 0.3864 - val_classification_output_accuracy: 0.6278 - val_ordinal_regression_output_accuracy: 0.7571\n",
      "Epoch 10/45\n",
      "91/91 [==============================] - 242s 3s/step - loss: 2.4638 - regression_output_loss: 2.2701 - classification_output_loss: 0.0420 - ordinal_regression_output_loss: 0.1518 - regression_output_accuracy: 0.2767 - classification_output_accuracy: 0.6274 - ordinal_regression_output_accuracy: 0.6690 - val_loss: 1.2165 - val_regression_output_loss: 1.0579 - val_classification_output_loss: 0.0325 - val_ordinal_regression_output_loss: 0.1172 - val_regression_output_accuracy: 0.3812 - val_classification_output_accuracy: 0.6574 - val_ordinal_regression_output_accuracy: 0.7809\n",
      "Epoch 11/45\n",
      "91/91 [==============================] - 241s 3s/step - loss: 2.5283 - regression_output_loss: 2.3342 - classification_output_loss: 0.0401 - ordinal_regression_output_loss: 0.1539 - regression_output_accuracy: 0.2694 - classification_output_accuracy: 0.6343 - ordinal_regression_output_accuracy: 0.6766 - val_loss: 0.9950 - val_regression_output_loss: 0.9897 - val_classification_output_loss: 0.0358 - val_ordinal_regression_output_loss: 0.1185 - val_regression_output_accuracy: 0.4196 - val_classification_output_accuracy: 0.6451 - val_ordinal_regression_output_accuracy: 0.7571\n",
      "Epoch 12/45\n",
      "91/91 [==============================] - 242s 3s/step - loss: 2.4852 - regression_output_loss: 2.2945 - classification_output_loss: 0.0417 - ordinal_regression_output_loss: 0.1489 - regression_output_accuracy: 0.2697 - classification_output_accuracy: 0.6305 - ordinal_regression_output_accuracy: 0.6717 - val_loss: 1.1872 - val_regression_output_loss: 1.1434 - val_classification_output_loss: 0.0368 - val_ordinal_regression_output_loss: 0.1154 - val_regression_output_accuracy: 0.3935 - val_classification_output_accuracy: 0.6281 - val_ordinal_regression_output_accuracy: 0.7623\n",
      "Epoch 13/45\n",
      "91/91 [==============================] - 240s 3s/step - loss: 2.6784 - regression_output_loss: 2.4792 - classification_output_loss: 0.0423 - ordinal_regression_output_loss: 0.1573 - regression_output_accuracy: 0.2587 - classification_output_accuracy: 0.6271 - ordinal_regression_output_accuracy: 0.6805 - val_loss: 1.8442 - val_regression_output_loss: 1.1268 - val_classification_output_loss: 0.0352 - val_ordinal_regression_output_loss: 0.1211 - val_regression_output_accuracy: 0.3981 - val_classification_output_accuracy: 0.6312 - val_ordinal_regression_output_accuracy: 0.7731\n",
      "Epoch 14/45\n",
      "91/91 [==============================] - 242s 3s/step - loss: 2.4568 - regression_output_loss: 2.2692 - classification_output_loss: 0.0415 - ordinal_regression_output_loss: 0.1460 - regression_output_accuracy: 0.2732 - classification_output_accuracy: 0.6222 - ordinal_regression_output_accuracy: 0.6672 - val_loss: 0.5801 - val_regression_output_loss: 1.0692 - val_classification_output_loss: 0.0347 - val_ordinal_regression_output_loss: 0.1129 - val_regression_output_accuracy: 0.4054 - val_classification_output_accuracy: 0.6577 - val_ordinal_regression_output_accuracy: 0.7587\n",
      "Epoch 15/45\n",
      "91/91 [==============================] - 242s 3s/step - loss: 2.5843 - regression_output_loss: 2.3919 - classification_output_loss: 0.0428 - ordinal_regression_output_loss: 0.1495 - regression_output_accuracy: 0.2542 - classification_output_accuracy: 0.6174 - ordinal_regression_output_accuracy: 0.6749 - val_loss: 1.5426 - val_regression_output_loss: 1.0508 - val_classification_output_loss: 0.0340 - val_ordinal_regression_output_loss: 0.1168 - val_regression_output_accuracy: 0.4136 - val_classification_output_accuracy: 0.6281 - val_ordinal_regression_output_accuracy: 0.7438\n",
      "Epoch 16/45\n",
      "91/91 [==============================] - 241s 3s/step - loss: 2.5985 - regression_output_loss: 2.4033 - classification_output_loss: 0.0421 - ordinal_regression_output_loss: 0.1530 - regression_output_accuracy: 0.2590 - classification_output_accuracy: 0.6247 - ordinal_regression_output_accuracy: 0.6704 - val_loss: 1.9170 - val_regression_output_loss: 1.1714 - val_classification_output_loss: 0.0366 - val_ordinal_regression_output_loss: 0.1190 - val_regression_output_accuracy: 0.3975 - val_classification_output_accuracy: 0.6262 - val_ordinal_regression_output_accuracy: 0.7666\n",
      "Epoch 17/45\n",
      "91/91 [==============================] - 241s 3s/step - loss: 2.5710 - regression_output_loss: 2.3776 - classification_output_loss: 0.0401 - ordinal_regression_output_loss: 0.1535 - regression_output_accuracy: 0.2690 - classification_output_accuracy: 0.6340 - ordinal_regression_output_accuracy: 0.6745 - val_loss: 1.6233 - val_regression_output_loss: 1.2364 - val_classification_output_loss: 0.0346 - val_ordinal_regression_output_loss: 0.1213 - val_regression_output_accuracy: 0.3827 - val_classification_output_accuracy: 0.6512 - val_ordinal_regression_output_accuracy: 0.7654\n",
      "Epoch 18/45\n",
      "91/91 [==============================] - 240s 3s/step - loss: 2.5903 - regression_output_loss: 2.3901 - classification_output_loss: 0.0436 - ordinal_regression_output_loss: 0.1567 - regression_output_accuracy: 0.2643 - classification_output_accuracy: 0.6072 - ordinal_regression_output_accuracy: 0.6683 - val_loss: 1.1960 - val_regression_output_loss: 1.1060 - val_classification_output_loss: 0.0359 - val_ordinal_regression_output_loss: 0.1169 - val_regression_output_accuracy: 0.4136 - val_classification_output_accuracy: 0.6157 - val_ordinal_regression_output_accuracy: 0.7670\n",
      "Epoch 19/45\n",
      "91/91 [==============================] - 241s 3s/step - loss: 2.4183 - regression_output_loss: 2.2278 - classification_output_loss: 0.0417 - ordinal_regression_output_loss: 0.1487 - regression_output_accuracy: 0.2663 - classification_output_accuracy: 0.6191 - ordinal_regression_output_accuracy: 0.6849 - val_loss: 1.4947 - val_regression_output_loss: 1.1662 - val_classification_output_loss: 0.0340 - val_ordinal_regression_output_loss: 0.1179 - val_regression_output_accuracy: 0.4148 - val_classification_output_accuracy: 0.6420 - val_ordinal_regression_output_accuracy: 0.7823\n",
      "Epoch 20/45\n",
      "91/91 [==============================] - 241s 3s/step - loss: 2.5433 - regression_output_loss: 2.3527 - classification_output_loss: 0.0411 - ordinal_regression_output_loss: 0.1495 - regression_output_accuracy: 0.2663 - classification_output_accuracy: 0.6326 - ordinal_regression_output_accuracy: 0.6731 - val_loss: 1.0675 - val_regression_output_loss: 1.1498 - val_classification_output_loss: 0.0349 - val_ordinal_regression_output_loss: 0.1153 - val_regression_output_accuracy: 0.3843 - val_classification_output_accuracy: 0.6435 - val_ordinal_regression_output_accuracy: 0.7824\n",
      "Epoch 21/45\n",
      "91/91 [==============================] - 240s 3s/step - loss: 2.5463 - regression_output_loss: 2.3495 - classification_output_loss: 0.0421 - ordinal_regression_output_loss: 0.1546 - regression_output_accuracy: 0.2611 - classification_output_accuracy: 0.6281 - ordinal_regression_output_accuracy: 0.6697 - val_loss: 1.2289 - val_regression_output_loss: 1.1678 - val_classification_output_loss: 0.0366 - val_ordinal_regression_output_loss: 0.1179 - val_regression_output_accuracy: 0.4117 - val_classification_output_accuracy: 0.6246 - val_ordinal_regression_output_accuracy: 0.7792\n",
      "Epoch 22/45\n",
      "91/91 [==============================] - 240s 3s/step - loss: 2.5133 - regression_output_loss: 2.3202 - classification_output_loss: 0.0423 - ordinal_regression_output_loss: 0.1513 - regression_output_accuracy: 0.2856 - classification_output_accuracy: 0.6187 - ordinal_regression_output_accuracy: 0.6620 - val_loss: 0.9225 - val_regression_output_loss: 1.1472 - val_classification_output_loss: 0.0354 - val_ordinal_regression_output_loss: 0.1140 - val_regression_output_accuracy: 0.4182 - val_classification_output_accuracy: 0.6528 - val_ordinal_regression_output_accuracy: 0.7824\n",
      "Epoch 23/45\n",
      "91/91 [==============================] - 242s 3s/step - loss: 2.6395 - regression_output_loss: 2.4462 - classification_output_loss: 0.0414 - ordinal_regression_output_loss: 0.1519 - regression_output_accuracy: 0.2538 - classification_output_accuracy: 0.6170 - ordinal_regression_output_accuracy: 0.6738 - val_loss: 1.0413 - val_regression_output_loss: 1.1223 - val_classification_output_loss: 0.0379 - val_ordinal_regression_output_loss: 0.1192 - val_regression_output_accuracy: 0.3951 - val_classification_output_accuracy: 0.6142 - val_ordinal_regression_output_accuracy: 0.7731\n",
      "Epoch 24/45\n",
      "91/91 [==============================] - 242s 3s/step - loss: 2.5023 - regression_output_loss: 2.3112 - classification_output_loss: 0.0421 - ordinal_regression_output_loss: 0.1490 - regression_output_accuracy: 0.2559 - classification_output_accuracy: 0.6302 - ordinal_regression_output_accuracy: 0.6901 - val_loss: 0.8381 - val_regression_output_loss: 0.9481 - val_classification_output_loss: 0.0331 - val_ordinal_regression_output_loss: 0.1106 - val_regression_output_accuracy: 0.4132 - val_classification_output_accuracy: 0.6530 - val_ordinal_regression_output_accuracy: 0.7760\n",
      "Epoch 25/45\n",
      "91/91 [==============================] - 241s 3s/step - loss: 2.6684 - regression_output_loss: 2.4708 - classification_output_loss: 0.0437 - ordinal_regression_output_loss: 0.1543 - regression_output_accuracy: 0.2742 - classification_output_accuracy: 0.6077 - ordinal_regression_output_accuracy: 0.6717 - val_loss: 1.0361 - val_regression_output_loss: 1.0448 - val_classification_output_loss: 0.0371 - val_ordinal_regression_output_loss: 0.1208 - val_regression_output_accuracy: 0.3843 - val_classification_output_accuracy: 0.6204 - val_ordinal_regression_output_accuracy: 0.7824\n",
      "Epoch 26/45\n",
      "91/91 [==============================] - 239s 3s/step - loss: 2.5254 - regression_output_loss: 2.3346 - classification_output_loss: 0.0433 - ordinal_regression_output_loss: 0.1480 - regression_output_accuracy: 0.2807 - classification_output_accuracy: 0.6149 - ordinal_regression_output_accuracy: 0.6763 - val_loss: 1.3770 - val_regression_output_loss: 1.0849 - val_classification_output_loss: 0.0351 - val_ordinal_regression_output_loss: 0.1123 - val_regression_output_accuracy: 0.4101 - val_classification_output_accuracy: 0.6309 - val_ordinal_regression_output_accuracy: 0.7524\n",
      "Epoch 27/45\n",
      "91/91 [==============================] - 241s 3s/step - loss: 2.6538 - regression_output_loss: 2.4615 - classification_output_loss: 0.0435 - ordinal_regression_output_loss: 0.1487 - regression_output_accuracy: 0.2562 - classification_output_accuracy: 0.6174 - ordinal_regression_output_accuracy: 0.6738 - val_loss: 0.6840 - val_regression_output_loss: 1.0273 - val_classification_output_loss: 0.0347 - val_ordinal_regression_output_loss: 0.1128 - val_regression_output_accuracy: 0.4012 - val_classification_output_accuracy: 0.6358 - val_ordinal_regression_output_accuracy: 0.7670\n",
      "Epoch 28/45\n",
      "91/91 [==============================] - 242s 3s/step - loss: 2.5414 - regression_output_loss: 2.3432 - classification_output_loss: 0.0437 - ordinal_regression_output_loss: 0.1544 - regression_output_accuracy: 0.2649 - classification_output_accuracy: 0.6039 - ordinal_regression_output_accuracy: 0.6783 - val_loss: 1.3909 - val_regression_output_loss: 1.0818 - val_classification_output_loss: 0.0342 - val_ordinal_regression_output_loss: 0.1160 - val_regression_output_accuracy: 0.3997 - val_classification_output_accuracy: 0.6543 - val_ordinal_regression_output_accuracy: 0.7500\n",
      "Epoch 29/45\n",
      "91/91 [==============================] - 241s 3s/step - loss: 2.5639 - regression_output_loss: 2.3747 - classification_output_loss: 0.0406 - ordinal_regression_output_loss: 0.1487 - regression_output_accuracy: 0.2694 - classification_output_accuracy: 0.6312 - ordinal_regression_output_accuracy: 0.6735 - val_loss: 1.4953 - val_regression_output_loss: 1.1237 - val_classification_output_loss: 0.0335 - val_ordinal_regression_output_loss: 0.1233 - val_regression_output_accuracy: 0.3959 - val_classification_output_accuracy: 0.6498 - val_ordinal_regression_output_accuracy: 0.7965\n",
      "Epoch 30/45\n",
      "91/91 [==============================] - 242s 3s/step - loss: 2.5414 - regression_output_loss: 2.3490 - classification_output_loss: 0.0408 - ordinal_regression_output_loss: 0.1518 - regression_output_accuracy: 0.2704 - classification_output_accuracy: 0.6264 - ordinal_regression_output_accuracy: 0.6714 - val_loss: 0.9315 - val_regression_output_loss: 1.0248 - val_classification_output_loss: 0.0354 - val_ordinal_regression_output_loss: 0.1219 - val_regression_output_accuracy: 0.4043 - val_classification_output_accuracy: 0.6435 - val_ordinal_regression_output_accuracy: 0.7469\n",
      "Epoch 31/45\n",
      "91/91 [==============================] - 239s 3s/step - loss: 2.5769 - regression_output_loss: 2.3838 - classification_output_loss: 0.0428 - ordinal_regression_output_loss: 0.1502 - regression_output_accuracy: 0.2640 - classification_output_accuracy: 0.6208 - ordinal_regression_output_accuracy: 0.6788 - val_loss: 1.1225 - val_regression_output_loss: 1.0994 - val_classification_output_loss: 0.0340 - val_ordinal_regression_output_loss: 0.1095 - val_regression_output_accuracy: 0.4069 - val_classification_output_accuracy: 0.6372 - val_ordinal_regression_output_accuracy: 0.7571\n",
      "Epoch 32/45\n",
      "91/91 [==============================] - 241s 3s/step - loss: 2.6521 - regression_output_loss: 2.4553 - classification_output_loss: 0.0434 - ordinal_regression_output_loss: 0.1533 - regression_output_accuracy: 0.2497 - classification_output_accuracy: 0.6208 - ordinal_regression_output_accuracy: 0.6939 - val_loss: 1.3525 - val_regression_output_loss: 1.1354 - val_classification_output_loss: 0.0353 - val_ordinal_regression_output_loss: 0.1220 - val_regression_output_accuracy: 0.3935 - val_classification_output_accuracy: 0.6466 - val_ordinal_regression_output_accuracy: 0.7654\n",
      "Epoch 33/45\n",
      "91/91 [==============================] - 242s 3s/step - loss: 2.5124 - regression_output_loss: 2.3189 - classification_output_loss: 0.0429 - ordinal_regression_output_loss: 0.1506 - regression_output_accuracy: 0.2535 - classification_output_accuracy: 0.6157 - ordinal_regression_output_accuracy: 0.6787 - val_loss: 0.9671 - val_regression_output_loss: 1.1081 - val_classification_output_loss: 0.0330 - val_ordinal_regression_output_loss: 0.1169 - val_regression_output_accuracy: 0.4398 - val_classification_output_accuracy: 0.6404 - val_ordinal_regression_output_accuracy: 0.7377\n",
      "Epoch 34/45\n",
      "91/91 [==============================] - 242s 3s/step - loss: 2.6016 - regression_output_loss: 2.4028 - classification_output_loss: 0.0430 - ordinal_regression_output_loss: 0.1556 - regression_output_accuracy: 0.2701 - classification_output_accuracy: 0.6153 - ordinal_regression_output_accuracy: 0.6745 - val_loss: 1.3711 - val_regression_output_loss: 1.1191 - val_classification_output_loss: 0.0349 - val_ordinal_regression_output_loss: 0.1198 - val_regression_output_accuracy: 0.3801 - val_classification_output_accuracy: 0.6325 - val_ordinal_regression_output_accuracy: 0.7524\n",
      "Epoch 35/45\n",
      "91/91 [==============================] - 240s 3s/step - loss: 2.4743 - regression_output_loss: 2.2823 - classification_output_loss: 0.0421 - ordinal_regression_output_loss: 0.1499 - regression_output_accuracy: 0.2786 - classification_output_accuracy: 0.6264 - ordinal_regression_output_accuracy: 0.6791 - val_loss: 1.7574 - val_regression_output_loss: 1.0110 - val_classification_output_loss: 0.0349 - val_ordinal_regression_output_loss: 0.1176 - val_regression_output_accuracy: 0.3796 - val_classification_output_accuracy: 0.6327 - val_ordinal_regression_output_accuracy: 0.7824\n",
      "Epoch 36/45\n",
      "91/91 [==============================] - 241s 3s/step - loss: 2.5761 - regression_output_loss: 2.3771 - classification_output_loss: 0.0425 - ordinal_regression_output_loss: 0.1566 - regression_output_accuracy: 0.2597 - classification_output_accuracy: 0.6143 - ordinal_regression_output_accuracy: 0.6835 - val_loss: 1.1672 - val_regression_output_loss: 1.0815 - val_classification_output_loss: 0.0360 - val_ordinal_regression_output_loss: 0.1181 - val_regression_output_accuracy: 0.3880 - val_classification_output_accuracy: 0.6325 - val_ordinal_regression_output_accuracy: 0.7729\n",
      "Epoch 37/45\n",
      "91/91 [==============================] - 241s 3s/step - loss: 2.4685 - regression_output_loss: 2.2738 - classification_output_loss: 0.0430 - ordinal_regression_output_loss: 0.1516 - regression_output_accuracy: 0.2774 - classification_output_accuracy: 0.6136 - ordinal_regression_output_accuracy: 0.6894 - val_loss: 1.4146 - val_regression_output_loss: 1.0558 - val_classification_output_loss: 0.0343 - val_ordinal_regression_output_loss: 0.1114 - val_regression_output_accuracy: 0.4198 - val_classification_output_accuracy: 0.6327 - val_ordinal_regression_output_accuracy: 0.7654\n",
      "Epoch 38/45\n",
      "91/91 [==============================] - 242s 3s/step - loss: 2.5798 - regression_output_loss: 2.3880 - classification_output_loss: 0.0416 - ordinal_regression_output_loss: 0.1502 - regression_output_accuracy: 0.2604 - classification_output_accuracy: 0.6274 - ordinal_regression_output_accuracy: 0.6742 - val_loss: 1.3468 - val_regression_output_loss: 1.1095 - val_classification_output_loss: 0.0362 - val_ordinal_regression_output_loss: 0.1231 - val_regression_output_accuracy: 0.4259 - val_classification_output_accuracy: 0.6188 - val_ordinal_regression_output_accuracy: 0.7639\n",
      "Epoch 39/45\n",
      "91/91 [==============================] - 240s 3s/step - loss: 2.4339 - regression_output_loss: 2.2404 - classification_output_loss: 0.0426 - ordinal_regression_output_loss: 0.1507 - regression_output_accuracy: 0.2682 - classification_output_accuracy: 0.6226 - ordinal_regression_output_accuracy: 0.6725 - val_loss: 1.3313 - val_regression_output_loss: 1.0387 - val_classification_output_loss: 0.0350 - val_ordinal_regression_output_loss: 0.1187 - val_regression_output_accuracy: 0.4132 - val_classification_output_accuracy: 0.6546 - val_ordinal_regression_output_accuracy: 0.7776\n",
      "Epoch 40/45\n",
      "91/91 [==============================] - 242s 3s/step - loss: 2.6115 - regression_output_loss: 2.4146 - classification_output_loss: 0.0428 - ordinal_regression_output_loss: 0.1542 - regression_output_accuracy: 0.2597 - classification_output_accuracy: 0.6157 - ordinal_regression_output_accuracy: 0.6801 - val_loss: 1.0509 - val_regression_output_loss: 1.0729 - val_classification_output_loss: 0.0310 - val_ordinal_regression_output_loss: 0.1147 - val_regression_output_accuracy: 0.4259 - val_classification_output_accuracy: 0.6636 - val_ordinal_regression_output_accuracy: 0.7546\n",
      "Epoch 41/45\n",
      "91/91 [==============================] - 241s 3s/step - loss: 2.5829 - regression_output_loss: 2.3884 - classification_output_loss: 0.0433 - ordinal_regression_output_loss: 0.1512 - regression_output_accuracy: 0.2538 - classification_output_accuracy: 0.6098 - ordinal_regression_output_accuracy: 0.6735 - val_loss: 2.1936 - val_regression_output_loss: 1.1888 - val_classification_output_loss: 0.0342 - val_ordinal_regression_output_loss: 0.1200 - val_regression_output_accuracy: 0.3770 - val_classification_output_accuracy: 0.6435 - val_ordinal_regression_output_accuracy: 0.7587\n",
      "Epoch 42/45\n",
      "91/91 [==============================] - 242s 3s/step - loss: 2.4547 - regression_output_loss: 2.2621 - classification_output_loss: 0.0420 - ordinal_regression_output_loss: 0.1504 - regression_output_accuracy: 0.2639 - classification_output_accuracy: 0.6247 - ordinal_regression_output_accuracy: 0.6742 - val_loss: 1.3943 - val_regression_output_loss: 1.1778 - val_classification_output_loss: 0.0368 - val_ordinal_regression_output_loss: 0.1198 - val_regression_output_accuracy: 0.3735 - val_classification_output_accuracy: 0.6327 - val_ordinal_regression_output_accuracy: 0.7685\n",
      "Epoch 43/45\n",
      "91/91 [==============================] - 242s 3s/step - loss: 2.6138 - regression_output_loss: 2.4205 - classification_output_loss: 0.0421 - ordinal_regression_output_loss: 0.1513 - regression_output_accuracy: 0.2555 - classification_output_accuracy: 0.6212 - ordinal_regression_output_accuracy: 0.6759 - val_loss: 1.3556 - val_regression_output_loss: 1.1130 - val_classification_output_loss: 0.0334 - val_ordinal_regression_output_loss: 0.1151 - val_regression_output_accuracy: 0.4043 - val_classification_output_accuracy: 0.6404 - val_ordinal_regression_output_accuracy: 0.7701\n",
      "Epoch 44/45\n",
      "91/91 [==============================] - 240s 3s/step - loss: 2.7060 - regression_output_loss: 2.5132 - classification_output_loss: 0.0413 - ordinal_regression_output_loss: 0.1518 - regression_output_accuracy: 0.2678 - classification_output_accuracy: 0.6194 - ordinal_regression_output_accuracy: 0.6788 - val_loss: 0.8575 - val_regression_output_loss: 1.1137 - val_classification_output_loss: 0.0362 - val_ordinal_regression_output_loss: 0.1174 - val_regression_output_accuracy: 0.4353 - val_classification_output_accuracy: 0.6420 - val_ordinal_regression_output_accuracy: 0.7603\n",
      "Epoch 45/45\n",
      "91/91 [==============================] - 242s 3s/step - loss: 2.5888 - regression_output_loss: 2.3928 - classification_output_loss: 0.0419 - ordinal_regression_output_loss: 0.1541 - regression_output_accuracy: 0.2531 - classification_output_accuracy: 0.6295 - ordinal_regression_output_accuracy: 0.6669 - val_loss: 0.9573 - val_regression_output_loss: 1.1088 - val_classification_output_loss: 0.0368 - val_ordinal_regression_output_loss: 0.1220 - val_regression_output_accuracy: 0.3796 - val_classification_output_accuracy: 0.6389 - val_ordinal_regression_output_accuracy: 0.7654\n"
     ]
    }
   ],
   "source": [
    "# ref - https://keras.io/getting-started/functional-api-guide/\n",
    "\n",
    "model.compile(optimizer = optimizers.Adam(lr=LEARNING_RATE),\n",
    "              loss={'regression_output': mean_squared_error, \n",
    "                    'classification_output': categorical_focal_loss(alpha=.25, gamma=2) ,\n",
    "                    'ordinal_regression_output' : binary_focal_loss(alpha=.25, gamma=2)\n",
    "                    },\n",
    "              metrics = ['accuracy'])\n",
    "\n",
    "history = model.fit_generator(generator=train_generator,\n",
    "                              steps_per_epoch=STEP_SIZE_TRAIN,\n",
    "                              validation_data=valid_generator,\n",
    "                              validation_steps=STEP_SIZE_VALID,\n",
    "                              epochs=45,\n",
    "                              callbacks = callback_list,\n",
    "                              verbose=1).history\n",
    "\n",
    "model.save(\"model_main_training.h5\")\n",
    "f = open(\"history_main_training\",\"wb\")\n",
    "pickle.dump(history,f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "colab_type": "code",
    "id": "PBIobuYSNDbu",
    "outputId": "2558bd40-9a2f-4324-eff9-48acf4855698"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(history['regression_output_loss'])\n",
    "plt.plot(history['val_regression_output_loss'])\n",
    "plt.title('Regression Model Loss - Main Training')\n",
    "plt.ylabel('Loss (MSE)')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='best')\n",
    "plt.xticks(range(1,46))\n",
    "plt.gca().ticklabel_format(axis='both', style='plain', useOffset=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "colab_type": "code",
    "id": "XQlUK5u8NDjt",
    "outputId": "3eec29b9-abf5-409a-9aac-c41fd31c9fb0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rNMtO/CilnCGE+BCIkFKuE0JYAcuAQCADePTGJDSl2piOJiGcfcv2EDQJ4V2XnQgKCpIRERG6eEqKkUq5mkfHT7bxxgMtmBziXeX2Zm2JYt6OeJaO70gP3wY6iFBRFEVRFEW51enTp/Hz8zN0GLVOWa+rEOKQlPI/62TodQyhlHIjsPGWbe+X+joPGHGXNqbfZvtOYGdVY1Rqhz1xmplFuzdz0kl7L/RuxqbIS7y15gR/T+mhJp5RFKXGu5h1ncFzwwhwteeVvr60bFLP0CEpiqIoRkDNnKHUCnti03Coa0HLxrq5wLEyN2Xmw224kHWdWVuiddKmoiiKIc35J4as3EL2n0lnwDehPP/LYeJScgwdVqXkFxVzIjmLzZEXKSouMXQ4iqIoNZrq9lBqPCklu2PT6ObjhImJuPsDyinIw4EnOjfl//Ym8mBAY9o3ddBZ24pibK4XFPPG6uO82McHH2e1qk9tE3M5m98PJfNksCcv9m7GotAEfgw7w8YTFxka6MpLfZrh7mht6DDLdDWvkFMXrnLywlXt/1nEpeRQVKIZ8jJnZABDA41/WndFURRjpRJCpcaLupRNWk4+3XRULlra6w+0YOvpFKb+fpwNL3bHyvzuE9YoSk20Jy6NdccuUFBUwoLH2xs6HEXHZm6Ooq6FGc/38sHO2pzX+jXnyWAPFuyKZ+nes/x59DyPdHDjhd4+NLarY5AYpZSkZOdz8kIWJ89f5dRFTRKYlJF785gGtpa0alKPPn7OtGpix7R1J9l6OkUlhIqiKFWgEkKlxtsTq9vxg6XZWJoxY2hrxi05yNztcbzWr7nOz6EoxiBMOw53y6lLxKfm4N3AxsARKbpy4EwGW0+n8Hq/5tSv+7+ldBxtLHlnYEsmdPdi3o44VhxI4vdDyYzp5M6zIT40sLW8Q6tVU1IiSUy/pun1u3ij9y+LtJyCm8d4OFrj72LHyA5utGpSj5ZN6uFsa/WvdnZFp7LxxEUKi0vU+rGKoiiVpBJCpcbbHZuKj7ON3u5qhzR3Zlg7Fxbsiqe/fyNaNbHTy3kUxZDC49Pwd7Ej5nI2C3cl8PnwNoYOSdEBKSWfbjpNw3qWjA/2LPOYhvWs+HBIayb28OLbbXEs3XuWlQfOMS7Yg2d6eOlkPda8wmKOncsk4uwVDmn/ZV0vBMDcVNDM2ZaQ5s60alKPVk3s8Gtsi62V+V3b7e3nzK8R5zh4JoOuPrq/KagoinIvULfTlBotr7CYA2cy9NI7WNp7A1tib23OG6uPqwkMlFonJTuPmMs5DPBvzMgObqw5ksylrDxDh6XowJaTlzmSlMmU+3zvukara31rPh/ehn+m9OD+Vg1ZsCue7p/v4KutMWTnFVbovCnZeWw6cZGP/jrFkHlhtJ62hZEL9zFrSzRn06/xQKtGfP6wP3+90I3ID/qx8aXuzB4RwJPBnnT0dChXMgjQzccJC1MTtkWlVCi+2iwnv4jiEv0tKaYotU2vXr3YsmXLv7Z99dVXTJ48uczjQ0JCuLGc3YABA8jMzPzPMdOnT2f27Nn/2V7a2rVrOXXq1M3v33//fbZu3VrR8HVC9RAqNVpE4hXyi0r0nhDWr2vBh0Na8+zyw/yw5wyTelZ9rUNFMRbhcekABPs4Ut/aguX7k/gx7AxvD1DrQtVkRcUlzNwShY+zDcPbl3+MnVcDG75+NJBnQ3yY808MX22N5afwRJ7p4c3Yrk2xtvj3pUNJiSQ2JYeIsxkcSrxCxNkrN8f9WZiZEOBqx4TuXgQ1rU/7pvX/VbZaVXUtzeji7cj2qBTeG9RSZ+3WRJm5BXz5Tww/7zuLdwMbXu/XnL4tGyKE7iZbU5TScguKSEi9RmuXml05NWrUKFauXEm/fv1ublu5ciUzZ86862M3btx412NuZ+3atQwaNIiWLTV/uz788MNKt1VVqodQqdFC41IxNxV08nTU+7n6t25Ev1YNmfNPDAmpNXOqdkUpS1hcGnZ1zGnVxA43B2sG+jdm+b6zZOVWrFdIMS6rIpJJSL3G1H7NMavE+LrmjWxZ8Hh71j/fjUA3ez7fHEWPmTv4cc8Z9sanM3d7LOOWHKDth3/T76vdvPNHJLtjU/FrbMs7A/xY82xXIqf347dJXXmzfwvua9lQp8ngDX38nDmTdu2e/btcVFzCsr2JhMzeyc/7zjKsnSvFJZKJyw4xfMFeDiZmGDpEpZaRUrL+2AX6fLGLQd/uYXPkRUOHVCXDhw9nw4YNFBRoxjAnJiZy4cIFVqxYQVBQEK1atWLatGllPtbDw4O0NM0Y/BkzZuDr60u3bt2Ijv7fkmWLFi2iQ4cOBAQE8PDDD5Obm0t4eDjr1q3j9ddfp23btsTHxzNu3Dh+//13ALZt20ZgYCD+/v6MHz+e/Pz8m+ebNm0a7dq1w9/fn6ioKJ28BiohVGq00Jg02rnXr5aF44UQfDSkNRZmJry55gQlqiRHqQWklITHp9PFyxFT7bItk3p6c62gmJ/3nzVwdEpl5RYUMWdrDEFN69O3ZcMqteXvaseSJzuyenIXmjnb8uFfpxi1aB+z/47h/JXrDGzTmNkjAtj5WggH37mP7x8P4ukeXrRzr4+Fmf4vM3o1dwZg+z1YNro3Pp1B3+7hvT9P4teo3s3S27+n9OCTof6cy8hlxIK9TPi/g8RczjZ0uEotEHXpKqMW7eOFFUeob21Bi0a2vLnmBJev1txhBg4ODnTs2JFNmzYBmt7BRx55hBkzZhAREcHx48fZtWsXx48fv20bhw4dYuXKlRw9epSNGzdy8ODBm/uGDRvGwYMHOXbsGH5+fixevJiuXbsyePBgZs2axdGjR/H2/l/lWV5eHuPGjePXX3/lxIkTFBUVMX/+/Jv7nZycOHz4MJMnT75rWWp5qZJRpcZKy8nn1MWrvF6NM38617PivYEtmbr6OL8cSOKxzk2r7dyKog9n03M5n3mdST29bm5r2aQePX0bsCTsDE9181TLrdRAP+45Q2p2Pgsea6ezksH2TR1YMbEzh85mcPV6EYHu9jqZcKaq3Bysad7Qlm2nU5jQ3evuD6gFkq/k8snG02w8cQkX+zrMH9OOB1o3uvlem5maMLqTO0MDXfgx7AwLdsbzwFe7ebidK1P6+tLE3jBLiyg1V9b1Qub8E8OyfWextTLj44daM6qjO2fSrjHo21BeXXWMpeM7Vn096E1vwqUTugn6hkb+0P+zOx5yo2x0yJAhrFy5ksWLF7Nq1SoWLlxIUVERFy9e5NSpU7RpU/aEa6GhoQwdOhRra816roMHD765LzIyknfffZfMzExycnL+VZpalujoaDw9PfH19QVg7NixzJs3j5dffhnQJJgA7du3Z82aNeV7De5C9RAqNdaNafK7VfPMciOCXAn2ceSzTVFcyLxeredWFF0Li9f8Ht06Q+PkEG/Scgr47VCyIcJSqiA9J58FuxK4v2VD2jd10Hn77Zs60KuFs1Ekgzf09nPmYGLGzZlLa6vrBcXM+SeGPl/sYntUClPu82Xbqz3p79+4zMS/joUpz/XyYffUXowP9uTPoxcImb2TTzaeJjO3oIwzKMq/lZRIVh5IotfsnSzdm8iojm7seDWExzo3xdRE4ONsw7sDW7InLo0fw84YOtxKGzJkCNu2bePw4cPk5ubi4ODA7Nmz2bZtG8ePH2fgwIHk5VWuF3TcuHHMnTuXEydOMG3atEq3c4OlpWZJIFNTU4qKiqrU1g2qh1CpsXbHpGFvbV7tg5mFEHw6tA39vtrNu2sjWTw2SA3aV2qs8Lh0GtWzwsup7r+2d/J0oK2bPYt2JzCqg1ulxqAphjF3Rxy5BUVMfeDeWTe1Twtn5u+MZ3dMKg8GNDF0ODonpWTDiYt8suE0F7LyGNSmMW8N8MOlnD199eta8O6glowL9mDOP7EsCk1gxYEkng3x4clgD1UFoJTpSNIVpq87ybHkLIKa1mf64I5lXnON6eTOzugUZm6OJtjHCb/G9Sp/0rv05OmLjY0NvXr1Yvz48YwaNYqrV69St25d7OzsuHz5Mps2bSIkJOS2j+/Rowfjxo3jrbfeoqioiPXr1/PMM88AkJ2dTePGjSksLGT58uW4uLgAYGtrS3b2f0u5mzdvTmJiInFxcfj4+LBs2TJ69uypl+d9g/qEV2okKSV74lIJ9nG6Oe6pOrk7WvNav+Zsj0ph3bEL1X5+RdGFkhJJeHwaXX0c/3NTQwjB5BBvkjJy2Rh5yUARKhWVlJ7Lz/vOMrKDGz7OtoYOp9oEutenvrV5rRxHeOrCVR5duI/nfzmCnbUFv07szNzR7cqdDJbmWt+aLx4JYNNL3eng4cDnm6MImbWTlQeSqryk0tW8Qg6dzWD5/rNM+zOSkd/vZdTCfWoJmxooNTuf1387xtDvwrmYlcdXI9vy26Qut70BL4Tg84fbUK+OOS+vPEpeYXE1R6wbo0aN4tixY4waNYqAgAACAwNp0aIFo0ePJjg4+I6PbdeuHSNHjiQgIID+/fvToUOHm/s++ugjOnXqRHBwMC1atLi5/dFHH2XWrFkEBgYSHx9/c7uVlRVLlixhxIgR+Pv7Y2JiwqRJk3T/hEsRUtb+iTGCgoLkjfVClNoh5nI298/ZzWfD/Hm0o7tBYigukTw8P5yz6dfY+kpPHG0sDRKHolTWyQtZDPxmD1+MCODhMpYlKCmR9J2zC0szUza82E31hNcAL608wpaTl9j1ei8a1rMydDjV6pVfj7I9OoVD7/Y1yI1CXcu4VsAXf0ez4kASdnXMefX+5ozq6K7T57Y/IZ3PNkdxJCkT7wZ1mfpAC+6/y1IV+UXFJKReI/pSNlGXsom5nE30pWzOlxpCYWNphm9DG2Iu59DIzopVz3TBQQ8zzFanlKt5ZF4vxLdh7b3RUlhcwv+FJ/L11ljyiooZ382TF3o3w6acE/ftiE7hySUHeTLYg2kPtir3eU+fPo2fn1rmSNfKel2FEIeklEG3HqtKRpUaKTRWO35Qz+sP3ompiWDm8DYM/CaU6etP8e2oQIPFoiiV8b/1B8v+PTIxETzT05upvx9nd2waPX0bVGd4SgVFns/iz6MXeK6X9z2XDIJmHOGaI+c5knSFIA/dj52sLkXFJfy87yxztsaSk1/EE108mHKfL3bW5jo/VycvR9ZM7sqWk5eZuSWKZ5Ydop27PW/29yOoaX3OXckl+pIm4Yu6nE3MpWzOpF2jSDvLtrmpwLuBDe2b1md0J3daNLLFt6EtrvXrIIRgX0I6Y388wLglB1g+oRO2Vrp/DvqWmp3P/J3xmlmXJWx8qVut7H0Pi0tj+rqTxKbk0NO3Ae8/2BLvBjYVaqNXc2fGdmnKkrBEQpo7q8+MGkQlhEqNFBqbipdTXVzrWxs0Dt+GtjzfqxlztsYwOKBJlad3V5TqFBafhleDujSyu33y8FBbF778O4YFO+PVh7uR+3xzFPWtzXmmp/fdD66FujdrgJmJYFtUSo1NCMPj0vhg/SmiL2cT7OPI+4Na0byRfpMPIQQPtG7EfX7O/H4omTlbY3jk+71YmZuQV/i/MlI3hzo0b1iP+1s1pHmjerRoZIuHY907Li3S2cuR78a0Y+KyQzy9NIKfnuxYY8YrXrlWwMLQBH4KS6SguISH2rqwLeoyU38/zm+TutaKXmjQzFg7Y8NpNkVews2hDoueCOI+P+dKV4S8NcCP8Ph0XvvtGJtf6q6qp2oIlRAqNU5+UTH7EzJ4JOi/JW6GMDnEm40nLvLu2hN08nKgXg28A6pUTvKVXJxsLGvMBU5pBUUlHDiTwcPt7vx7ZGFmwoTunny84TRHz2XS1s2+miJUKiI0NpXQ2DTeG9Tynv0bZFfHnA4eDmw/ncIbD7S4+wOMzD+nLvP00ghc69dhwWPt6dfqzqWbumZmasKjHd0Z0taFn/ed5ULWdZo3tKV5I1uaNbQtd9ngrfr4NeSLEQFMWXWU5385wvzH2mFuxJNUXc0rZHHoGRbvOcO1giIGBzThpT7N8Gpgw9oj53n516P8FJ7IU908DR1qlVwvKGbh7gTm74oD4NW+vjzdw6vKn2dW5qZ8/WggD80L4801J1j4eHs13KAGUAmhUuMcOnuF64XFdGtmHL0VFmYmzBzehqHfhfHpxuXupOoAACAASURBVCg+HeZv6JCUanAk6QojFuzFq0Fd5o1uR7MaNq7k6LlMcguKb1suWtqjHd35ZlssC3bGs+Dx9tUQnVIRJSWSzzZF4Vq/Do91NsyYamPRx8+Zjzec5lxGLm4Ohq0gqQgpJXN3xNHU0ZotL/cw6E2mOhamPN1Dt+s5PhToQnZeIe/9eZKpvx/nixEBVV+vTseu5RfxU3giC3cnkHW9kP6tGzGlr++/xgwOaduE9ccuMGtLFPf5OdPUse4dWjQuRcUlRF64SlhcGnvj0zmYmEF+UQkD/Rvz9sDyz1hbHi2b1OP1fs2ZsfE0Kw+eY1Q55nqQUqrEUYcqOkeM8d6iUZTb2BObhpmJoLOX8ZQEBbjZM6G7FysOJBGuXddNqb2y8wp5aeVRGthaknGtgAfn7uHXg0kV/gNsSGFxaZgI6OLleNdjbSzNGNvVgy2nLhGfmlMN0SkVsf74BU5euMpr9zfH0qzm9VbrUh8/Tdl+TZttNOLsFY6dy2RCN88aWXFQHo938eC1+33548h5PvzrlNH8vcwrLOaH0AR6zNzBrC3RtG9an79e6Mb8x9r/ZwIZIQQzhvpjbmLCG6uPU1JiHM+hLFJKoi9l8+OeM0z4vwgCP/yHh+aFMWtLNGk5+Yzp1JTfJnVh3pjKzVh7N0918yTYx5EP158i4S6fG1ZWVqSnpxvNz0RNJ6UkPT0dK6vyjyVXPYRKjRMam0agu73RDU6fcp8vW05e4q01J9j8Ug/qWNTOD3UF3v/zJMlXcln1TBfcHayZsuoob6w+QVhcOjOGtja6n82yhMen0drFrtwTVYzt6sHC3Qks3JXA58Pb6Dk6pbzyi4qZtSWalo3rMbgWrr9XUZ5OdfFyqsu2qBTGdvUwdDjltmh3AvbW5gxv72boUPTquV4+ZOYW8sOeM9SrY84rfX0NFkt+UTGrDp5j7o44Ll/Np5uPE6/c70s79/p3fFwjOyveHeTHG6tP8MuBJB7r3LSaIr4zKSXnMq4TFp9GeHw6e+PTSMspAKCpozWDAprQ1duRLt6OOFXDuD4TE8EXI9rywNe7efnXo6ye3PW2pcKurq4kJyeTmpqq97juFVZWVri6ln9olUoIlRol41oBkReymHKf4T5EbqeOhSmfDvNn9KL9zNkaw9sD1BTKtdEfR5L548h5Xr6v2c2JK5aO78T8nXF8+U8Mx5Iz+XZUIG1cjXes3bX8Io4kZTKhe/nLwpxsLBnZwY0VB5KY0tf3jhPRKNVn+b4kkq9cZ+l4f6MrwTOU3i2cWbr3LNfyi6hbyXFv1SkhNYd/Tl/m+V4+tf5GohCCdwb6cTWvkG+2xWJXx7zax+IVFpew5nAy32yL43zmdTp41OfrRwPpXI5qiRseCXJj/bGLfLrxNL1aOOulh608Uq7mER6fTnh8GmFx6TeX/nC2taR7swZ08Xakq7ejwSbga2RnxadD/Zm8/DBfbY3h9X5lj+01NzfH07Nmj8ms6Yz/L6WilBIWl4aU0N2Ay03cSVdvJ0Z1dOeH0AQG+jcmQE3AUaskpefy3tqTdPCoz/O9fG5uNzURPN+7GZ28HHlxxREenh/Om/39GB/sYZRjIg4kZlBUIgn2Kf8FEMDT3b1Yvj+JH8POqBseRuBqXiHfbo8l2MfRaP8mGkJvP2d+2HOGPXFp9GvVyNDh3NXiPWcwNzHhiS4ehg6lWggh+GSoP1evF/HRX6eoZ2XGiCD994wWl0jWHTvP11tjSUzPJcDVjk+H+dO9mVOF/04LIfh0mD/9vtrNW2tO8H9Pdqi2v/XFJZKvt8awMfIScSmaUky7OuZ08XJkUk8vung74d2grtF89vT3b8yI9q58tzOenr7OdPQ0nuE+yv+oMYRKjRIam0o9KzOj7n15a0ALGtha8sbq4xQUldz9AUqNUFhcwosrjyAEzBnZFrMySl86eDiw6aXu9PR15qO/TjHh/yK4cq3AANHeWXhcGhamJgQ1rdgHs5uDNYPaNGb5vrNk5RbqKTqlvBbuSuBKbiFvPuBnNBd/xqCDhwO2lmZsP2384wjTc/L5/VAyQwNdaGB770zPb2Zqwtej2tK9mRNvrD7O5shLejtXcYnkr+MX6PfVbqb8eow6Fmb88EQQa58Lpodvg0r/7rg5WPPGAy3YHZPK6sPndRx12aSUvLs2km+2x9HYzoq3B7Tgrxe6cfi9vix4vD2Pd/HAx9nG6P4eTBvcSjO84tejZF1Xnx3GSCWENdj1gmIKi++dhENKyZ7YNIJ9nIx6/Z96VubMeMifqEvZfPlPjKHDUXTkq60xHD2XyafD/O9YfmNvbcGiJ9oz7cGWhMam0f/rUA6cyajGSO8uLC6ddk3tK1We9kwPb64VFGsWaVYMJuVqHj/sSeDBgCb4u9oZOhyjYm5qQo/mDdgenWLUk34A/LwvifyiEiZ0v/fK5SzNTFnwWHsC3Ox5ccUR9sTqdkK2/KJiVhxIos8XO3n+lyMAzBvdjg0vdOO+lrpZ0uPxzk3p4FGfD9efJOVqXpXbu5tZW6JZcSCJZ0O8WfZUJyb28Ka1i51RXxOBZmKyr0a25dLVPN7/M9LQ4ShlUAlhDRV9KZvuM3cwZtH+eyYpjE+9xoWsPLrVgNKo+1o2ZFRHNxbsimfb6cuGDkepor3x6Xy3M55HglwZ1ObuE3cIIXgy2JM1z3bFytyERxfu5ZttsRQbwcVpxrUCTl28SrB35X6PWjapR0/fBiwJO0NeYbGOo1PKa85Wzc/T6/c3N3QoRqlPC2dSs/M5cT7L0KHcVl5hMUv3JtKreYMat2yNrtS1NGPJuA54OtVl4rIIjiRdqXKb2XmFLNwdT/fPd/DWmhPYWpkzf0w7trzcg4FtGut0rK2JieDzh9uQX1TCO2sj9TpL5sLd8Xy3M57Rndx5vV/N+70PdK/Pi72b8efRC/x5tHp6VJXyUwlhDXQiOYuRC/dSVFLCgcQMZm6OMnRI1WJPrGb2qR5Gsv7g3Ux7sBUtG9fjlVXHOJeRa+hwlEq6cq2AKb8exdOxLtMebFWhx7Z2seOvF7szOKAJX/4Tw2M/7OdyNdxFvpO98ekAdC3H+oO3MznEm7ScAn47lKyrsIxWfpHxJb1xKTmsijjHmE5NcXesOWvtVaeQ5s6YCNhmxMtP/HHkPOnXCnS+5l9NY29twbKnOtLA1pJxSw4SfSm7Uu2k5eQze0s0wZ9t55ONUTRraMPPT3Vi3fPB9PdvrLdeNK8GNrx6vy//nLrMX8cv6uUcqw6e45ONUQxs05iPhrQ2upLQ8nqulzftm9bn3T8iSb6irouMiUoIa5iIxAxGL9qHjaUZ657rxriuHiwKPcPmSP38ETImobFpNHW0rjGLDVuZm/LdmHaUlEie/+WwUV5Y1jTnMnIZ++MB+n65q1rWw5NS8uaa46Rfy+ebUYGVmrHQxtKMOSPbMnN4G46ey2TA16HsjDbcRWpYfBo2lmYEVKHMsJOnA23d7Fm0O4GiWlahkF9UTHh8GjM3R/Hgt3to8d5mnlt+2KgqMWZtiaKOuSkv9Pa5+8H3KIe6FrRzr8/2KOOs0CgpkfwQmkCrJvXKtRZobedcz4qfn+qEpZkJjy/eT1J6+ZOFcxm5vP9nJMGfbWfezjiCfZz487lglk/oTLdKTBhTGU918yLAzZ5p606SnpOv07Y3R17kzTXH6d7MiTmPtDX68tA7MTM14auRbZHAK78eM4qqGUVDJYQ1SHhcGo8vPkADW0vN+meO1rw9wI8AN3te/+04iWnXDB2i3hQUlbAvIb3GzaTn4VSXWSPacCw5i0833hs9ufpQrL14un/Obg6dvUJaTj7Dvgu/2dulL78cSGLLyctM7deC1i6VT6CEEJppyl8IvnkX/NONpw2SZITFpdHZy6HMSXHKSwjB5BBvkjJy2ajHySCqg5SSmMvZ/BCawLglB2j7wT+MXrSfhbsTsDI34eF2rmw4cZEXVxwxiqTw0NkMtpy8zMQeXjhWw1piNVlvP2ciz1/lUpZhe+XLsiM6hfjUa0zs4VVje3t0zc3Bmp8ndKKguIQxi/fdtZoi6tJVXl55hJDZO1lxIImH2rqw9ZWezNeOS6xOpiaCWcPbkJ1XyPT1p3TWblhcGi+uOEqAmz3fP94eC7Oaf9nu5mDNB4NbcSAxgwW74g0djqJV83+y7hE7olIY99NB3B2sWflMZ5po17yxMDNh3uhATE0Fk5cfNsiYHiklCXrurTmSdIVrBcV086kZ5aKlPdC6MeODPfkpPJENeionqc2iL2UzbH44H284TRdvR/6e0oM/n+tGA1tLnvhxP79FnNPLeWMvZ/PRX6fo3sxJZ+tk+Tjbsva5YMZ0cuf73QmMWLC3WsuJk6/kcjY9l66VHD9YWl+/hng3qMuCnfF6HTejD6nZ+aw9cp5XVh2l86fbuH/Obj7ecJqkjFweCXJl0RNBHHm/L79N6srsEQG8N6glmyIv8fKvRw3aIyql5LNNUTjZWN6Tk5BUVJ8WDQFN8mVsFoUm0NjOigH+jQ0dilHxbWjLT092JCOngCcWHyAz97+zNB9MzGD8Twd54KtQ/jl1mfHBHoRO7c3nw9vg3cDGAFFr+Da05YXezVh/7AJ/n6z6jbJj5zKZuDQCT6e6LBnXAWuL2rNS3LB2Lgxs05g5/8RwPDnT0OEo6DkhFEI8IISIFkLECSHeLGO/pRDiV+3+/UIIj1v2uwshcoQQr2m/dxNC7BBCnBJCnBRCvKTP+I3FphMXmbgsguYNbVk5sTPOtv9eENq1vjVzRrbl9MWrTF93slpjk1Ly4V+n6P3FLlYcSNLbefbEpWFqIujiXTNLa97s34JAd3veWH1c78lzbZFfVMyX/8Qw6NtQzmXk8vWjbVk8Nogm9nVwd7Rm9eSudPR04PXfjzNrS5ROZxPMKyzmhRVHqGthxhePBOh0EgIrc1NmDPVn3uh2xKfmMOCb0GorawuP0/SoBldh/OANJiaCZ3p6c+riVUJ1PDugruUVFhMam8onG0/T/+tQOszYysu/HmVHVApBHg58/rA/YW/2ZvurIXwwpDV9WzbE1sr85uOf6ubJOwP82HD8Iq+sOmawpHDr6RQOJl7h5fua1aqLQ33xbWiDi30dthnZ8hMnkrPYl5DB+GBPzKvQU19btXWzZ9ETQZxJu8a4JQe5ll9ESYlk2+nLDJ8fzogFezl6LpNX+/oS9mZv3hnYkkZ2VndvuBpMDvHGr3E93l0bWaWleeJSshm35AAONhYsfaoj9tYWOozS8IQQfPKQPw1sLXl55VFyC4oMHdI9T+jrzq4QwhSIAfoCycBBYJSU8lSpY54F2kgpJwkhHgWGSilHltr/OyCB/VLK2UKIxkBjKeVhIYQtcAh4qHSbZQkKCpIRERG6forV4o8jyby66hiB7vVZ8mQH6pW6SLnV7C3RzN0Rx6zhbaplkVcpJZ9uimLh7gTsrc0pKZFsfbXnfxJWXRgyLwxTAWueDdZ529XlQuZ1Bn4TSsN6VvzxbHClpvy/Vxw6e4U3Vh8nLiWHoYEuvDeoJQ51//uBWFhcwntrI1l58BwD2zTmixEBWJlX/XX9YP1JloQl8uO4IHprexn04VxGLhOXHeJS1nXC3+yj95+Jl1YeISwunYPv9NFJmVpBUQk9Zu7A06kuKyZ21kGEupNXWMzP+86yMzqVA4kZFBSVYG4qCGrqQLdmTvRo1oBWTepVKNmfvzOezzdH8VDbJnxRzWN5iopL6P91KMUlki1TeqhEopze/zOS3yKSOfJ+X538bdCFF1ccYXtUCuFv9b7jZ/q9bsvJSzy7/DABrnZcyy8m+nI2LvZ1mNjDi0eC3Iz2MzTyfBZD5oUxLNCFWSMCKvz45Cu5DJ+/l6ISye+TuuDhVFcPURqHvfHpjP5hH492cOfTYf6A5tqyoLiEvIISrhcWc72wmLwb/xcUk1dUzHXtvrwb+wo0+4N9nHRyw7M2E0IcklIG3bpdn7cYOwJxUsoEbQArgSFA6eRtCDBd+/XvwFwhhJBSSiHEQ8AZ4ObAOCnlReCi9utsIcRpwOWWNmuNX/Yn8c7aE3TxcmTRE0F3ndBiSl9fDidd4b0/I2ntYodf43p6je+Lv2NYuDuBJ7o05YkuHgz4OpSP/jrNt6MCdXqezNwCTiRn8kLvZjptt7o1sa/DnJFtefKng7z/Z2SlPihqu2v5RczaEs3/7U2kcT0rljzZgV7NnW97vLmpCZ8O88fTqS6fbY7i/JXrLHoiqEoLPO+ISmFJWCLjunroNRkEzViKD4e0YsSCvfx26BxPdPHQ27mklITHp9PV21FnY5YszEyY0N2Tjzec5ui5TNpW87id2ykqLuGllUfYcvIyvg1teLxzU7o1c6KTp0OVetYmh3hTIiWztkRjYiKYNTygWpLCa/lFvP/nSWJTcljwWDuVDFZAH7+GLN17lr3x6fRqcfu/JdXlfOZ1Npy4yPhgD5UM3kW/Vo2Y+XAbXv3tGL4NbZgzMoBBbZoY/c9/axc7JvX0Yt6OeAYFNKGnb/mHuqRm5/P44gPkFhTx6zO1OxkE6OLtyMQeXny/K4G/T166mQBWpq9KCJi/K57pD7ZibFcPncda2+kzIXQBSg/uSQY63e4YKWWRECILcBRC5AFvoOldfK2sxrXlpYHAfp1GbSQW7znDR3+dolfzBsx/rH257myamgi+fjSQgd+E8uzyw6x7PvhfZU+69M22WObuiGNURzemP9gKExPBs728+WprLA+3cyHkDhfxFRUen06JhB6+Nf+uT0hzZ57v5cO32+Po4OnAI3ruyZVS8tuhZA4lXuGTYf5GPTvZrphU3l5zggtZ13mic1Nef6AFNuWY1VMITeliU0drXv71KEO/C+PHcR3wrcS6XinZebz22zFaNLLlzf4tKvM0KiyoaX0C3e35IfQMYzo11dt7FJuSQ2p2PsE+ui27frSjO99si2XBzngWPN5ep21XhpSSd/6IZMvJy0x7sCVPBut2rN1zvXwoLpF8+U8MpkKzBpkuS4pvFXk+ixdXHOFM+jWe7+VDv1aN9Hau2khzE8CUbVGXjSIhXLLnDADjdPxzWVs93N6V7r5OONW11Ovvma690LsZmyMv8dbq42yZ0qNc12JX8woZ++MBLmZd5+enOun9pr6xeLVvcyzNTEnPyaeOuSl1LEyxMtf803xvQh3zW7dp/rc0/9++omLJiyuPMG3dSZKv5PJWf78a9TNjaMY6CGE6MEdKmVPWnWwhhA2wGnhZSnm1rAaEEBOBiQDu7u76i1QP5m6PZfbfMfRv3YivHw2s0KxSDWwtmTu6HaMW7ePN1SeYOzpQ5zOYfbczji//iWF4e1dmPOR/8xducog3649d4N21kfw9pYfOxriExqZia2lGgKtx9D5U1cv3+XLo7BXeWxuJvx57cq8XFPPen5H8rl0rrlszJx4MuPui6tXtyrUCPvrrFGuOnMe7QV1+e6YLQR4OFW7ngdaN+dWuDhOWRvDwd+F891g7uldgzcqSEsmrq46Rk1/Eyomdq628TAjBMz28mfTzITZHXmJgG/1MMhEWpxnnp4sJZUqzsTRjbFcP5u6IIz41x6CTOgDM3BLNrxHneKG3j86TwRte7NOM4hLJ19tiMTURfDLUX+cXHiUlksV7zjBzSxSOdS1ZPqGTzt+7e4GVuSndfJzYfjoFOUQadEbPq3mFrDx4jkFtGuOinRhOuTt9DEPRNytzU2YOD2D4gnA+3xzFxw/53/H4vMJiJvwUoZnxeGxQpT4DayoLMxNe6etb5XbMTWHBY+35cP1JFoWe4Xzmdb58pK1BSsUvZeUZzbjW8tJnv/t5oHT3h6t2W5nHCCHMADsgHU1P4kwhRCLwMvC2EOJ57XHmaJLB5VLKNbc7uZRyoZQySEoZ1KBBzZiZUkrJrC1RzP47hqGBLnw7qmLJ4A0dPR2Y2q85G05c5KfwRJ3G+ENoAjM3RzOkbZP/3Bm3NDPlk6H+JF+5ztdbY3VyPiklu2PS6OLtWKVp8o3JjZ5cuzrmPLv8MNl5lR94fjuJadcYNj+c3w8l82JvH3ycbZi3I86oZoOUUrL+2AXu+3IX645d4PlePmx4sXuVPggD3OxZ+1wwLvXrMG7JQX7ZX/6Jjn4MO0NobBrvDWpJs0r0LlZF35YN8XSqy/e79TdjZ1hcOu4O+lnHc2xXDyxMTVi4K0HnbVfED6EJzN8Zz5hO7jq5wLiTl+9rxgu9fVh58Bzv/hmp00mNUrLzGLvkADM2nqZXc2c2vdRdJYNV0MfPmQtZeURVcsFzXVl5IImc/CKe7n5vL0R/r2jftD7jgz35eV/SHZdIKiwu4bnlhzl4NoMvR7bVaYXVvcbURDB9cCveHejHxhOXGPPDfjKu/XemWn0pLC7h222x9Ji5g10xqdV2Xl3Q5xX2QaCZEMJTCGEBPAqsu+WYdcBY7dfDge1So7uU0kNK6QF8BXwipZwrNLf2FgOnpZRf6jH2andjts55O+IZ1dGNL0YEVCkBmtjDi74tGzJjw2kOJ13RSYxL9yby8YbTDPBvxBcjyh4708nLkZFBbvyw5wwnL2RV+ZyJ6bmcz7xe49YfvJsGtpZ8OyqQpIxc3lx9QqdJwN8nL/Hg3D1cyLzOknEdeOX+5kzu6U3UpWyjmX79YtZ1nl4awQsrjuBSvw7rX+jGa/2a6+ROnot9HX6b1IXuzZx4+48TzNhw6q6L30aez+LzzVHc37IhYzpVf0WBqYng6e5eHNfOPqhrRcUl7E9I13m56A1ONpaM7ODGmiPJBlvzbfWhZD7ecJqBbRrz4ZDWeu8JEkLwSl9fJod488v+JN5fF6mT3+PtUZfp/1UoBxMzmDG0Nd8/3p76ZUyopJTfjXHI26MM9/evsLiEJWGJdPFyrNKapkrN8tr9zWnqaM0bq4+XOZNmSYlk6u/H2RaVwodDWjPYCKt4ahohBBO6e/HdmHacOJ/Fw/PDOZuu/3W6oy9lM+y7cL74J4Z+rRvhX8N+z/WWEEopi4DngS3AaWCVlPKkEOJDIcRg7WGL0YwZjANeAf6zNMUtgoHHgd5CiKPafwP09BSqTXGJ5O0/TrAkLJEngz10Un4khGD2iAAa21vx/PLDVb5D8sv+JN7/8yR9Wzbk60cD75isvjWgBfWtzXl7zYm7XojfzZ5YzR2WipT+1RSdvBx57X5NT+7SvWer3F5RcQmfbYpi4rJDeDjW5a8Xut0cMzO4bRNc7Oswd7thewlLSiTL95/l/i93sycujXcG+LFmcledl83aWpnzwxNBjO3SlEWhZ5j886HbTmudW1DEiyuO4FjXks8fbmOwkrJh7VxwsrHg+926X6j3xPkssvOL9NrL9HR3L0qkpqe1um09dZmpq4/TzceJLx+pnoleQPN3dmq/5jzTw4uf9yXxwfpTlf79yissZvq6k4z/KYIGtpasf74bYzo1VYuW64BzPSvauNqx7XT1LO9Slg3HL3IxK4+JPVTv4L2kjoUpnw1rQ1JGLl/8HfOvfTc6Av44cp5X+/ryeOemBoqydhrg35hfJnTiSm4Bw74L54iOOkduVVRcwrwdcTz4reZG/Pwx7fh2VGCZM6MbM73W4EkpN0opfaWU3lLKGdpt70sp12m/zpNSjpBS+kgpO96YkfSWNqZLKWdrv94jpRRSyjZSyrbafxv1+Rz0rai4hFdXHWXFgXM838uH9we11NkFgF0dc+aPaU/atQJe/vVopUuafos4x9t/nKB3C2fmjg686wxf9tYWvDeoJceSs1i2N7FS57xhd2warvXr0NRR92VuxuCZHl70aeHMxxtOcfRc5RdnTc3O57HF+1mwK57Rndz5bVKXf5UGmpuaMKmnF4eTMtl/Rvc9UOUhpWTisgje+UMzC+6Wl3vwdA8vvZUCm5ma8MGQ1kx/sCVbT19m5Pf7uHz1v71XH6w7xZn0a3w5MsCgPTFW5qaM7eLBzuhUonVc2hauLVfqqsd1PN0crBnUpjHL952t0vpbFXXgTAbP/XKY1k3q8f3j7bE0q97xIkII3uzfggndPPkpPJGP/jpd4aQw9nI2D80L46dwzU3Btc8FV3vZcm3Xp0VDjpzLJC0nv9rPLaVk4e4EfJxtKjTjpFI7dPF25LHO7vwYduZfFVvfbIvjp/BExgd78nxvHwNGWHsFeTiwZnJX6lqaMWrRPracvKTT9mMvZ/Pw/HBmbYnmvpbO/D2lB/399TMPgL7VjkFZNVRBUQkvrDjC2qMXeL1fc17r11znd4Nbu9gx/cFW7I5JZe6OuAo/fu2R80xdfZzuzZz4bky7cl9sDQ5oQvdmTsz+O4aLWdcrfF7QlNjsi0+ne7MGtfYuuYmJ4ItHAnC2teK55YfJzK14T25EYgYDvwnlSFIms0cE8MlQ/zJLL0cEueFkY8m8Svwc6MLGE5fYejqF1+735ZenO9HUsXqm0x4X7MkPY4NISM3hoXlhnLrwv3moNhy/yK8R55jc09soxmg93qUpdcxNWbhbt2Px9sSm4de4Ho42lV+Oozye6eHNtYJiZv8dTWE1LN5+6sJVnvq/g7jWr8OSJzvedWkefRFC8M5AP54M9uDHsDN8srF8SaGUkp/3nWXQt3tIzc5nybgOTHuwldGsl1eb9PFzRkrYGV3943r2xqdz6uJVnu7uqWY9vEe98UALGtezYurvx8krLOb/whOZszWGh9u58u5Av1p7jWMMvBrYsObZrrRoVI9JPx9iiQ6qWIpLJN/vimfgt3tIyshl7uhAvhvTXu+fsfqkEkIDySss5pllEWyKvMR7g1ryXC/93R0a1dGNoYEuzNkaw57YtHI/bsPxi7yy6iidPR1Z+HhQhS5ShBB8/FBrCotLmL7uZGXC5ti5TLLzi2rd+MFb2Vtb8N2YdqRm5/PKqmPl7smVUvJDaAKPLtyHtYUpa58LZnh719seb2VuyoTunoTGpnE8ufK9DhSnCgAAIABJREFUkZVRUFTC55ujaNHIlskhPtX+4de7RUN+m9QVgBELwtkedZnzmdd5a81xAtzsmaLnCUjKy97agpEd3Pjz6PlK30i5VV5hMYeSrhCsx97BG1o2qcfoTu4s23eWIXPDiDxf9XHEt3M2/RpjlxzAxtKMpU91Mnh5jhCC9we1vFmm/NnmqDsmhVeuFfDMskO8uzaSjp4ObHq5u1Esi1BbtWpSj4b1LNkeVf1lowtDE3CysWBIW5dqP7diHGytzPlkmD9xKTmM/+kg09ad5D6/hnz+sO5nKFb+y8nGkhVPd6avX0M+WH+Kj/46VemqufjUHIYvCOfTTVH0at6Av6f0ZFCbmj/2UyWEBnAtv4gnlxxkZ0wqnw7z56lu+l2PSAjBjKGtaeZsw0srj5Rr0ofNkZd4ceURgpo6sHhcEHUsKn7HuqljXV66rxlbTl7m70p004fGpmEi9FvmZiwC3Ox5d5Af26NSWFCOMWQ5+UU898thPt5wmt4tnFn3QrdyjcMb08mdelZmfLdD9+PU7mTZvrOaCXT6tzDYWogtm9Rj7XPBeDaoy4T/i2D0on2USPjm0bZGtdDxU908kcCSsESdtBeReIWCohKCfarnxsonQ/1Z8Fg7UnPyGTIvjE83nuZ6QbFOz5FyNY/HFx+gqLiEZU91NJop/IXQzHD3WGd3vt+VwKwt0WUmheHxaTzw9W52RKfw7kA//u/JjjVyav2aRAhB7xbO7I5Jo6BI/73XN8RczmZndCr/z95dh0d1bQ0c/u0IBAIEd01wCRR3h0JpC0UKpaWlt1CF6q3d+1Vv3ahTatShSIUKtMXdiwQP7pAAgQQIkf39sSYlQGSSzJmJrPd58gyZOXNmA8nM2Xsvua1dTd35LeC61ivP4BZVWbozmja1SvP+8IzrMSjPKlLIn/G3tGBk+5p8tng39323lvMJ7n82JSXLIvw17yxi1/E43hnWjI9uaUG54nl3VzA1/Un0gSOnz7PjWCxv3diUm1p7p6Jh0UIBfHhzC84lJDHmu7UZhnPN2XKUsZPW0rRqCJ/f3ipH/QRHdwqlfsXiPDNjE7HxaRf1SM+iHcdpUrUkJYvmrcTc7BrRtgbXNa3MG39sY/mu9EtUbz96huvfXyxNb/vWZ8KIFpRwo+ktyCrlyPY1mbXpCJHHvFOCPeZcAu/N3UHH2mV9nj9ToUQQU+5qR48GFdgbfZb/DWjktdBVd1UrXZR+TSrx3Yp9nPZAS5IlO6MI8DO0ruW9vlZ9Gldi9sNdGNKiKhMW7qLPOwtZGul+dEJGYs4lcOvnK4mKjWfi7a2pXT535doZY3j++sbc1Lo6H87fybi/LhaSSEhK5rVZW7n50xUEFw7gx3s7MKpTqO4QeEn3+hWIjU9k1R7v5VF/umgXQYF+3KIFQxTwzHUNeea6hnx6W9airpRnpG5LMWvTEYZ/stytoou7o+IYOmEZL/y2hU51yvHXQ53p36xKvgr11QmhD4SVK8aCR7tyw1Xph/c5oXb5YrwyKJzVe0/y2qytaR6zYPtx7vlmLQ0qleCLf7WmWA5zcgL9/XhpYBOOnD7PG39sc/t5MecSWLf/FJ3zebhoasYYXh7YhJplgxk76W+OnblyJ/fndQfp//4STp9L5NtRbbmrS1iW35BGdqhFkUB/xs/3Ts+4D+dFEnMugSevqZ8r3jyLFgpgwi0tmP9v7/8OuuvOzqHExidmqY9iepZGRtGsWkmv59eFFAnklUHhfDe6DQDDP13B49M25KjgzPmEJEZ/uZqdx2OZMKIFzaqV9NRwPcrPz/DigMYMa1WNd+dG8vbs7eyNjmPw+KV8OH8nN7aoxq9jO2r7AS/rULsMhQL8mLPFO+0njp05z09/H2JIi2raOkQBsih7e4daFHdzEVc5Y1SnUD4Y3pyIQ6cZ+OES9kSl3ZYiOdny+eLd9H1nIduPnmHc0KZ8cmsLypfIfxEdOiH0EV8VP7i+aWVudeW4zIq4NIxzaWQUd361mtrli/HVv1q7veuUmebVS3FLmxp8uWwP692spLlsZzTJNn+2m8hIscIBjL+5BWfOJ/DApHX/tO24kJjMMz9H8MDkdTSuUoLf7+9Iu2yG0pYOLsRNravz07qD7D9x1pPDv8KBk2eZuHQPN1xVhUaVc8/Fr5+foWbZ3LUzmFrjKiF0qF2GiUt2E5+Y/XDLmHMJbDwYQ3svhYumpX1YWf54sDN3dQll2toD9By3gJkbD2f5PIlJyYz5Tpo3jxvaLNe/N/j5GV66oQlDWlTl7dk76DVuIbuj4vhgeHNeHRyeo8gLlT1FCwXQIawMc7Ye9Ur7na+W7iUhOdnxtBClVNZd06QSk0a3IeZcAgPHL72iZ/fe6DiGfbKc53/dTPuwsvz1cBduuKpqrljYdoJOCAug//ZrQNOqITw6df0/zTpX7j7BHV+upmaZYL4Z1cbjYZqP9qlHuWKFefKHjSS6UX1w0Y7jBBfy56rquXMHwEn1KhbnhQFNWLYrmrdnb+fQqXPcOGEZXy7by+hOtfhudNscr06N7lwLPwOfLHJ2l/CNP7ZhkOa8Kmvu6hzG0dPx/LzuULbPsXyXLKx4o6BMRoIC/XmybwN+vq8D5YoV5p5v13LX16vTbAOSluRky+PTNzJ7izRvzisJ/H5+hlcGhTOibQ3ah5Vh5oOd6ReeN0uS5xfdXeHiO48726j67IVEvl6+l94NK+TqxSelCrIWNUrzw70dKB4UwE0fL2dWxBGSky1fLdtDn7cXseXwaV4fHM5nt7WkQj7cFUxNJ4QFUOEAfz64uTl+foZ7vlnL0sgobp+4ksolg/h2tDPV+koEBfLc9Y3YfPi0W42rF0dG0S6sTK4q9uFNg1tU5caWVXlvbiR931lE5LFYPry5Of/t19Aj/yaVQoowqHlVJq/an2ZoqidsPBDDT+sOcUfHWlTOJUU/8pJOdcrSoFIJPlm4K9vV0JZGRlEk0J+rqpfy8Oiyp3GVEH4e04En+tZn/rbj9HxrAZNW7svw72et5eWZW5i+9gAP9cx7zZv9/Qz/G9CYL27PPcVvCrLurkquTlcbnbbmADHnEhjdSRvRK5Wb1SobzA/3tKdBpRLc8+0a+r23mKd/3kTrWqX586HODGlZLd/uCqZWMK+2FVVLFWXc0KZsPnya4Z+uoHyJICaNbktZB3uo9GlckZ4NyjPurx0Zhiruiz7L3uizdPRhmFtu8Hz/xoRXDaFSSBA/j+nANR5udnpXlzASk5L5fPEej54X5CL+xd83Uzq4EHd3DfP4+QsCYwx3dq7FjmOxzN+evZynJTujaVWrNIUCcs9bfaC/H3d3CWPWg51pVLkET/6wkeGfLmd3OjkcHy3YxSeLdnNbuxrc30ObN6ucqVKyCPUrFnc0j1CqEe7mquolaVEjdyzGKKXSV8bVlqJPo4ocOHGWVwc14YvbW1EppOAs4mV4lWCMqWqM+bcx5mdjzCpjzEJjzIfGmH7GmNxzhaGypXv9Cjx6dT0aVynBd6PbOJ4ka4zhuf6NMQae/jki3RyORZHSOLiTjytS+lpQoD8/3NOemQ90IqxcMY+fv1bZYPqFV+ab5XtzVOgjLXO3HmP5rhM80KOOx3JRC6JrwytTOSSIjxZkPbT36OnzRB6LpWPt3Nm2pVbZYCaNbssrA5uw6dBp+ry9kPHzd15SAfn7Vft4ddZWrm9amWeua1QgVmmV83o0KM/qvSc9/r6X4q/NR9h34iyjO4Xqz6xSeUSRQv58eHNz1jzVi6Gtqhe43910J3XGmInA58AF4FXgJuBeYDbQB1hsjOnsjUEq59zXrTa/ju3ktVWQKiWL8Ejveszbdpzf0ikssWh7FJVDggjVvAsC/P0cfVO6t2sYsfGJfLVsj8fOmZiUzMszt1KrbDDD23inrUp+Fejvx7861mLl7hOsc7MgU4olrjYP7cNy7067MYZhrasz++EudK1Xjldnbf2nof2siCM8+cNGOtctxxtDmmprBuUx3etXICnZsmDHcUfO//HCXVQrXYSrG1V05PxKKWcYY3JVRI03ZfS3ftNa29ta+661dqm1NtJaG2Gt/cFaOxboCmS/2oEqsG5rV4PGVUrw3C+biTl36QptYlIyS3dG0alOuQK3OuMLDSqVoEf98ny+ZDdnL2StT2R6pqw+QOSxWB7vU6/A5oB60rDW1SkRFMDHC3dm6XlLIqMpVTSQhpVKODQyz6lQIogJI1pe0tD+/kl/07RaST66pXmB/YBWzmhWrSSlgwsxd4vn8wjX7D3B2n2nuKNDLfx1EUMplUek+ylrrY24/D5jTCljTLjr8QvW2kgnB6fypwB/P14ZGE50bDyvXtYPccPBGE6fT6RjAeo/6Gv3dqvNybMJTFq5P8fniotP5K2/ttOyRildHfeQYoUDuKVtDWZGHEm3V9LlrLUs3SmFmfLSzlqfxpWY/VAXbmxZjauql2TiyFbankF5nL+foVu98szbdtytqtdZ8cnC3YQUCWRIy2oePa9SSjkp02VXY8x8Y0wJY0xpYC3wiTFmnPNDU/lZ4yoh3N6hFt+t2MfqPSf+uX/xjiiMgQ4FvKCMN7WoUYq2oaX5ZOGuHPW8AwmVioqN5z/9GugOrweNbF+TQD8/Pl3sXi7h7qg4Dsecz9XhoukJKRrIywOb8P1d7Tze/kapFD0alCfmXAJr92UtFDsje6Pj+GPzEW5pW91nvYaVUio73InDCbHWngYGAl9Za9sAPZwdlioIHu5Vlyoli/DkDxu5kCirtIt2HKdx5RBHWl+o9N3XrTZHTp/np78PZvscx06f5+OFu+jXpBLNc0mbg/yifIkgBjavwtTVB4iKjc/0+CU7owFdWFEqPZ3qlCXAzzDHg+0nPlu8m0A/P25rV9Nj51RKKW9wZ0IYYIypBNwI/OrweFQBElw4gOf7N2LHsVg+WbSLM+dltbaThot6XcfaZQmvGsL4+TtJymbPu7f+2k5icjKP9dEm9E4Y1SmU+MRkvlq2N9Njl0ZKYaaaZYp6YWRK5T3FgwJpE1qauR5qP3Ey7gJTVx+gf7PKjlfsVkopT3NnQvg88AcQaa1dZYwJBXY4OyxVUPRoUIFrmlTknTk7+H7VfpKSLZ3qFOx2E75gjOHermHsiT7L7+lUf83I9qNnmLJ6P7e0rUGNMlod1gm1yxejZ4MKfL1sT4YFgJKTLct2RdO+dlkN21UqA93rV2DHsVj2RaffFzczh06dY+rq/dw/+W/OJSQxShvRK6XyoEwnhNbaqdbacGvtva7vd1lrBzk/NFVQPHNdIwr7+/Hi71soEuhP8xolfT2kAql3w4qElQvmg3mR6faITM/Lv28huHAA93ev49DoFMDdXUI5eTaBqasPpHvM5sOnOXU2gQ65tP+gUrlFj/rlAZibhbDRk3EX+H3jYf7740a6vTGf9q/M5dFpG9h06DT396hDvYrFnRquUko5JtOsZ2PMa8ALwDlgFhAOPGSt/cbhsakCokKJIB7rW5+nfoqgbWhpCgf4+3pIBZKfn+HerrV5ZOp65m07Rvf6Fdx63tLIKOZtO84TfetTSnM/HdWyZmmaVy/Jp4t3cXOb6gSk0dYjL/QfVCo3qFk2mLBywczZeoyRHWqleUxcfCIr95xgaWQUSyKj2XLkNNZCcCF/2oSW4eY21elQuyz1KhTPUxV9lVIqNXfKYPW21j5mjLkB2IMUl1kI6IRQeczNrasTefQMPRu6NwlRzri+WWXe+ms778+NpFu98pmGHCYnW178fQtVShZhZPua3hlkAXdn5zDu/mYNszYd4drwylc8vmRnNHXKF6OC5jEplakeDSrwxZI9xMYnUqxwABcSk1m3/xRLIqNYujOKdftPkZBkKeTvx1XVS/JQz7p0qF2G8Koltc+qUirfcGdCmHJMP2CqtTZG81KUp/n5GZ7r39jXwyjwAv39uLtLKE/9vIkVu0/QNjTjsMOf1x9k06HTvD20GUGBurPrDb0aViC0bDATFkhF19Tvx/GJSazcHc2wVtV9OEKl8o7u9cvz8cJdPDtjE8fPxLNqzwnOXkjCGGhSJYQ7OobSPqwMrWqWpkghfY9TSuVP7kwIfzXGbEVCRu8xxpQDzjs7LKWUrwxpWY135kTywbzIDCeE5xOSeOOP7TSuUoLrm165U6Wc4e9nGNUplP/8uFGKx6QKDf173ynOJyTTPkzzB5VyR4sapSgTXIhpaw4QVi6YwS2q0j6sLO1CyxBSNNDXw1NKKa/IdEJorX3ClUcYY61NMsbEAf2dH5pSyheCAv0Z1akWr8zcyoYDpwivmnaRn4lL9nDw1DleHxKuuTNeNrB5Fd76axsTFuy6ZEK4NDIKPwNtMtnZVUqJQH8/fr2/IwZDxRANs1ZKFUyZBsAbYwKBW4DvjTHTgDuAaKcHppTynZvbVKdEUAAfztuZ5uMn4i7w4bxIutcvr8VLfCAo0J+R7WuyYPtxth45/c/9S3ZG06RqSUKK6M6GUu6qFFJEJ4NKqQLNnYzo8UAL4EPXV3PXfUqpfKp4UCAj29fkj81HiDx25orH352zg7gLiTzZt74PRqcAbmlbg6KF/Pl44S4AYuMTWb//FB00XFQppZRSWeDOhLCVtfY2a+1c19ftQCunB6aU8q2RHWoRFODP+Pm7Lrl/T1Qc3yzfy9BW1ahTQXtu+UrJooUY2qoaM9Yd4tCpc6zcHU1isqVDbd2xVUoppZT73JkQJhljwlK+McaEAknODUkplRuUDi7E8DbV+WndQfafOPvP/a/9sZVCAX481LOuD0enAO7oWAsLTFyymyWR0RQK8KNFjVK+HpZSSiml8hB3JoSPAvOMMfONMQuAucAjzg5LKZUbjOpUCz8DnyySXcI1e0/y+8Yj3Nk5lPLa587nqpYqSr8mlZi0cj9zthylZY1S2v5DKaWUUlmS6YTQWjsHqAPcD4wF6gGlHR6XUioXqBRShEHNqzJ51X6OnTnPS79voVzxwozuFOrroSmXOzuHEhufyJ7osxouqpRSSqksc2eHEGttvLV2g+srHhjnzvOMMX2MMduMMZHGmCfSeLywMeZ71+MrjDE1L3u8ujEm1hjzb3fPqZTyrLu6hJGYlMzoL1ezZu9JHu5Vl+DC7rQwVd7QuEoIHV0TQe0/qJRSSqmscmtCmIZMm44ZY/yBD4C+QEPgJmNMw8sOuwM4aa2tjUwyX73s8beAmVk8p1LKg2qVDaZfeGXWH4ihTvliDGlR1ddDUpd58pr6jGhbI92ekUoppZRS6cnuhNC6cUxrINJau8taewGYzJUN7fsDX7r+PA3oYYwxAMaYAcBuYFMWz6mU8rAx3WpTPCiAp65tSIB/dt82lFMaVQ7hfwMa4++X6VqdUkoppdQl0o37MsZsJO2JnwEquHHuKsD+VN8fANqkd4y1NtEYEwOUMcacBx4HegH/Tuv4DM6plPKwehWLs/HZq309DKWUUkop5WEZJQJd67VRXOlZYJy1Nta1YZhlxpg7gTsBqlev7rmRKaWUUkoppVQ+ke6E0Fq7N4fnPghUS/V9Vdd9aR1zwBgTAIQA0ciu32BjzGtASSDZtWu4xo1zpoz/Y+BjgJYtW7oT4qqUUkoppZRSBYqTpQJXAXWMMbWQSdswYPhlx8wAbgOWAYOBudZaC3RKOcAY8ywQa6193zVpzOycSimllFJKKaXc4NiE0JUTOAb4A/AHPrfWbjLGPA+sttbOAD4DvjbGRAInkAlels/p1N9BKaWUUkoppfIzIxty+VvLli3t6tWrfT0MpZRSSimllPIJY8waa23Ly+/PbpVRa60N9+D4lFJKKaWUUkp5WW6tMqqUUkoppZRSymFOVhlVSimllFJKKZWL+WV2gDGmrTFmlTEm1hhzwRiTZIw57Y3BKaWUUkoppZRyTqYTQuB94CZgB1AEGAV84OSglFJKKaWUUko5z50JIdbaSMDfWptkrZ0I9HF2WEoppZRSSimlnOZOH8KzxphCwDpjzGvAYdycSCqllFJKKaWUyr3cmdiNcB03BogDqgGDnByUUkoppZRSSinnubNDGAVcsNaeB54zxvgDhZ0dllJKKaWUUkopp7mzQzgHKJrq+yLAbGeGo5RSSimllFLKW9yZEAZZa2NTvnH9uWgGxyullFJKKaWUygPcmRDGGWOap3xjjGkBnHNuSEoppZRSSimlvMGdHMIHganGmEOAASoCQx0dlVJKKaWUUkopx2U6IbTWrjLG1Afque7aZq1NcHZYSimllFJKKaWclumE0BgTCNwDdHbdNd8YM0EnhUoppZRSSimVt7kTMjoeCAQ+dH0/wnXfKKcGpZRSSimllFLKee5MCFtZa5um+n6uMWa9UwNSSimllFJKKeUd7lQZTTLGhKV8Y4wJBZKcG5JSSimllFJKKW9wZ4fwUWCeMWYXUmW0BnC7o6NSSimllFJKKeW4dCeExpgAa22itXaOMaYOl1YZjffO8JRSSimllFJKOSWjkNGVqf78hrV2g+tLJ4NKKaWUUkoplQ9kNCE0qf7cwemBKKWUUkoppZTyrowmhNZro1BKKaWUUkop5XUZFZWpb4zZgOwUhrn+jOt7a60Nd3x0SimllFJKKaUck9GEsIHXRqGUUkoppZRSyuvSnRBaa/d6cyBKKaWUUkoppbzLncb0SimllFJKKaXyIZ0QKqWUUkoppVQBpRNCpZRSSimllCqg0p0QGmM2GmM2pPG1MVXF0QwZY/oYY7YZYyKNMU+k8XhhY8z3rsdXGGNquu5vbYxZ5/pab4y5IdVzHjLGbDLGRBhjJhljgrL+11ZKKaWUUkoplVGV0WtzcmJjjD/wAdALOACsMsbMsNZuTnXYHcBJa21tY8ww4FVgKBABtLTWJhpjKgHrjTG/ABWA+4GG1tpzxpgpwDDgi5yMVSmllFJKKaUKIierjLYGIq21uwCMMZOB/kDqCWF/4FnXn6cB7xtjjLX2bKpjggB72ZiLGGMSgKLAoRyOUymllFJKKaUKpExzCI0xbY0xq4wxscaYC8aYJGPMaTfOXQXYn+r7A6770jzGWpsIxABlXK/bxhizCdgI3G2tTbTWHgTeAPYBh4EYa+2f6Yz7TmPMamPM6uPHj7sxXKWUUkoppZQqWNwpKvM+cBOwAygCjEJCQR1lrV1hrW0EtAKeNMYEGWNKIbuKtYDKQLAx5pZ0nv+xtbaltbZluXLlnB6uUkoppZRSSuU5blUZtdZGAv7W2iRr7USgjxtPOwhUS/V9Vdd9aR5jjAkAQoDoy157CxALNAZ6AruttcettQnAD0B7d/4OSimllE8s+xD+/D9fj0IppZRKkzsTwrPGmELAOmPMa8aYh9x83iqgjjGmluv5w4AZlx0zA7jN9efBwFxrrXU9JwDAGFMDqA/sQUJF2xpjihpjDNAD2OLGWJRSSinvS4yHha/B6omQnOzr0SillFJXcGdiN8J13BggDtnRG5TZk1w5gWOAP5BJ2xRr7SZjzPPGmOtdh30GlDHGRAIPAymtKToilUXXAT8C91pro6y1K5DiM2uR3EI/4GO3/qZKKaWUt+34E86dhAuxcGKXr0ejlFJKXcFYazM+wJhg4Jy1Ntn1vT9Q+LJKoLlay5Yt7erVq309DKWUUgXNpOEQ+RckXYBBn0GTwb4ekVJKqQLKGLPGWtvy8vvd2SGcg7R3SFEEmO2pgSmllFL5Ulw07PgDWv4L/ALhyAZfj0gppZS6gjsTwiBrbWzKN64/F83geKWUUkpFTIfkRGh+K5RvAIfX+3pESiml1BXcmRDGGWOap3xjjGkBnHNuSEoppVQ+sH4SVGwCFRpBpaZweANkkqahlFJKeZs7E8IHganGmEXGmMXA90ixGKWUUkql5fg2OLQWmg6X7ys1hXMn4PTl3ZeUUkop3wrI7ABr7SpjTH2gnuuuba4egEoppZRKy/pJYPwvFpGp1FRuD6+HkKq+G5dSSil1mXQnhMaY7tbaucaYgZc9VNcYg7X2B4fHppRSSuU9yUmwYQrU7gnFyst9FRoBRsJG6/fz6fCUUkqp1DLaIewCzAWuS+MxC+iEUCmllLrcnkUSGtr7hYv3FQqGsnW1sIxSSqlcJ90JobX2GWOMHzDTWjvFi2NSSiml8q71k6FwCNS75tL7K4XD3qW+GZNSSimVjgyLyria0T/mpbEopZRSeVt8LGyeAY1vgMCgSx+rGC47h3FRvhmbUkoplQZ3qozONsb82xhTzRhTOuXL8ZEppZRSec2WXyAhDpredOVjqQvLKKWUUrlEplVGgaGu2/tS3WeBUM8PRymllMrD1k+CUjWhWpsrH6vYRG6PbIDaPbw6LKWUUio9me4QWmtrpfGlk0GllFIqtZgDsHuh7A4ac+XjRUtDyeq+2yE8HwNv1odNP/rm9ZVSSuVKmU4IjTGBxpj7jTHTXF9jjDGB3hicUkoplWdsmAJYCB+a/jEVw6X1hC/sWw5nDsP8V8Ba34xBKaVUruNODuF4oAXwoeurhes+pZRSSoFMsNZPhurtoXSt9I+r1BRO7ITzp703thT7lsvt8a0QOdv7r6+UUipXcmdC2Mpae5u1dq7r63agldMDU0oppfKMQ2shahs0HZbxcSmFZY5GOD+my+1fIXmMxSvB0ve8//pKKaVyJXeKyiQZY8KstTsBjDGhQJKzw1L/sBYuxEHccTgbLeXKz0a5bqOhTm+o1cnXo1RKqYJt/WTwLwyNBmR8XMVwuT28AWq0d35cKRIvwME10PJfUKwCzH5GxlAp3HtjUEqptETvlGJcfv6+HkmB5c6E8FFgnjFmF2CAGsDtjo4qv0tKgJj9Mqm7fIIXdzzVfdFym3g+/XNtmAL3/w2Finpv/EplV2I8+AWCnzvBCUrlEYkXYOM0qN8PgkIyPrZ4RQgu7/3CMofXy2dJ9bZQqwssfB2WvQ8DP/buOJRSKrWoSPigFVzzBrS6w9ejybmYgzAwGcroAAAgAElEQVTvJej9PykklkdkOiG01s4xxtQB6rnu2matjXd2WPncqX3wXvMr7w8sCkXLQnAZWcEt30j+XLQsBJeF4HIXHy9aVkqXT+wLKz+Gjg96/++hVFZERcIX10CdXtD/A1+PRinPifwLzp1Iu/fg5YyRXbkjXi4ss9+VP1itLRQpCVeNgFWfQI9nIKSKd8eilFIpIqaBTYYdf+XtCWFSIqz4COa/DMmJEi1Sp5evR+W2TCeExpiBl91V2xgTA2y01h5zZlj5XInKMGB8qoleWflzVnf5arSXkNHFb0GL26BIKWfGq1ROxRyErwfIDvjf30Czm70bLqeUk9ZPkl2/sO7uHV+pKeycBwnnITDI2bGl2LdcQrKKV5Dv294DKyfIBUzv/3lnDEoplZq1EDFd/rxnsUTQ+efBRgb7V8KvD0lueJ2r4ZrX5P02D3EnbusO4FPgZtfXJ8DjwBJjzAgHx5Z/BRaBZsOhbm+o0lz6UmU35LPH09Jbasm7nh2jUp4SFw1f3wDnTsHI3yGkGvz+GCRrKjIgYbQJGYSFq9zt7AnYNgvCbwR/d7IwkDxCmwTHNjs7thTWSkGZam0v3leqBjTsD2u+8E3FU6WUOrIRorZDaDe4cEbynPOSsydgxv3wWS84dxKGfgPDv89zk0Fwb0IYADSw1g6y1g4CGgIWaINMDJUvVWwCTYbIKu+ZI74ejVKXij8D3w6GU3th+GSo0Q56vwBHN8Lqz309utzh+xHw1fXaF87TEs7DhqmQcM7Z14mYDskJmVcXTS2lkIu3wkZP7JLd+eptL72//ViIPw1/f+2dcSilVGoR08EvAPq9CRiJnMgLrIV138H7LSXqqd0YuG8lNLhO0gLyIHcmhNWstUdTfX/Mdd8JIMGZYaks6fYfSLogRQKUyi0S42HyzVLMYsgXULOj3N+wP9TqDHNfkN3Dguz0Ydjxp+ze7F3q69HkL4vHwQ+j4Pd/O/s66ydDhcayOOeuUrWgcIj3Csuk9B+8fEJYpYX0TVw+XvJflFLKW6yFiB9kd7BMGFS+CnbN9/WoMndsC3zRD366B0qHwV0L4eoXoXAxX48sR9yZEM43xvxqjLnNGHMbMMN1XzBwytnhKbeUDoXmt0noz4ldvh6NUnJxOf0O2L1ACsjU63vxMWOg72uyezi3gOcubfoRsFComFR8VJ5x5qj02StaRlZv//7GmdeJ2gEHV2dtdxDkd6BiE2n74A37l0v107L1rnys/Viper35J++MRSmlAA6sgph90HiQfB/aVe7LrSHsF87C7Gfho44S7n/du/CvP6BiY1+PzCPcmRDeB0wEmrm+vgTus9bGWWu7OTk4lQVdHpNy/vNe9vVIVEFnLfz6IGz5Ba5+GZqlUXmxfANoc5csYhxa5/Uh5hoR02Ri0PZe2DZTKrGqnFvwKiTFw+2zZDf6t0ckV8XT1k8G4wdNbsz6cys1lQIE3tiZ27cCqrVJu91L3T5QprZMoDVsWSnlLRHTpXdr/X7yfVg3ya3eu8S340rLtpnwQRuJPAkfBmNWSzHHfNRCK9O/ibXWAquB36y1DwG/A3l7XzQ/Kl4R2t4NG6fCkQhfj0YVZLOfkZykzo9Cu3vTP67rE1Jh9/dHITnZe+PLLaJ3SgJ9kyHQerRUVlv+oa9HlfdFRcpCQ4uRUK4uDPoMgkrClFulAJenJCfDhu8hrMfFyp1ZUSlc+gJG7/DcmNJy9gREbbsyXDSFn58sSBxelzsvxJRS+U9ykkTI1O0NQSXkvmptIKBI7gobPbVfUl8mDYNCwXD7TBjwgVy75DOZTgiNMaOBacAE111VAI0tyY06PCC/WAU9DE/5zuK3Yck70GoUdPtvxscGhUDP5+DASrmwLmgifpDbRgOhWHmpUrnuO7mAV9k35zmp5NzFVfOsWHkY/Dmc3As/j/HcLtjexRJqmdVw0RSVmsqt02Gj+1fIbbV0JoQg/ROLloGlGraslPKCPYsh9ujFcFGAgMLSjio3FJZJSpBrmQ9aw865cq1y96J83S7L3ZDRDsBpAGvtDqC8k4NS2VSkFHR4ELbPgr3LfD0aVdCs+VJ2BxsPgr6vu1dpq+lNULUV/PV07s0bcIK1sptfvR2UrCb3tRsDiedg9We+HVtetn8VbJkheXHFUn1M1ewAPZ+Rx1Z85JnXWj8ZCpe4GO6UVWXqQECQ84Vl9i2XdIIqzdM/plBRWcTZPlPyIpVSykkR0yV3vs7Vl94f2lUiGk4f8sWoxN5lMKGzXJeEdoX7VkDHB/Nmf8QscGdCGG+tvZDyjTEmAGk7oXKjNndDsYqySq75IMpbNv8seYO1e8KAj9yPq/fzkwIzcccl76ugOBohH3pNBl+8r3wDCT9c+YlUaFVZY618gAeXl8n15drfD/X6wZ//J02Ec+JCnPzMN+wvu5HZ4R8AFRo533pi/wrZjcxsnK1GSz6PFjdSSjkp8YK8f9a75soe3KFd5XbXAm+PSqyfDBP7SNG7YZPgpknSK7wAcOeqbYEx5j9AEWNML2Aq8Iuzw1LZVqioFJjZtwx2/OXr0aiCYOc8mD5Kdvpu/AoCCmXt+VWaQ/MRsnNzfJszY8xtNk4D4w8NB1x6f/sxEkazcZpvxpWXbZ8F+5ZC18fTLv9tDAz4EEpUgakjc9byZOtvcCEWmg3P/jlAJmqHNzi3eJcYDwfXpp8/mFqxchL+un4yxB53ZjxKFXQn98r7T0GuCL9rHpw/demCaIoKjaFoWTnGF1Z9BuUbyq5g/Wt8MwYfcWdC+ARwHNgI3AX8bq3NJDlIGGP6GGO2GWMijTFPpPF4YWPM967HVxhjarrub22MWef6Wm+MuSHVc0oaY6YZY7YaY7YYY9q5M5YCpfmt0udqznMFs1iH8p4DayThukwdGP69JF1nR49n5LkzH8v/O9vJyRIuE9btysT00G5QvhEs+yD//zt4UlKilAMvU1ta8KSnSElZtIiLkh6FyUnZe731k6BkjYzz8txRMRziY+DknpydJz2H1km1VXcmhOAKWz4Pqz51ZjxKFXTzXpRiKpNvkUiDgmjjNCn0FZpGowI/PwjtIoVlvP0ZePqQ1DRoPCj71zJ5mDsTwrHW2k+stUOstYOttZ8YYx7I7EnGGH/gA6Av0BC4yRjT8LLD7gBOWmtrA+OAlJixCKCltbYZ0AeY4ApVBXgHmGWtrQ80Bba48XcoWPwDofv/SVhaxHRfj0blV8e2wreDZGdhxA+Sw5pdwWWlCM2u+dKuIj87sFKKkTQZcuVjxkC7++DYJt+tkOZF67+D41uhx9OZ53lUbgZ9X5VCAQvfyPprnT4kP6dNh+W85Pg/hWUcyiPc72pIX62Ne8eXqyttKFZ9AgnnnBmTUr6QnOz7RbaoSMkdr9UZjm+BGff7fkzeduEsbPsdGl6ffjRRaDeJlDnm5cv7Lb/KbYPrvfu6uYQ7n2ZpLbeOdON5rYFIa+0uVw7iZKD/Zcf0R/oaglQy7WGMMdbas9balOZMQbhyFo0xIUBn4DMAa+0Fa+0pN8ZS8DQaCBWawLwXJF5bKU86tQ++vgH8C8GIn6TtSU61vEN2x/74r3xo5Fcbp0kxkfSKkTQZDMUqaMVHd104C/NekpBldz/IW4yE8KEw/2WZGGbFhilgk+X5OVW+oYQOO5VHuG8FlA69tMBOZtqPhbPRsguqVH5w7iSMbw+/PezbcSx8XfJ0B30G3Z+SPrQFrdXQjj8l3L5xGuGiKUK7yq23209smQHl6svCWAGU7oTQGHOTMeYXoJYxZkaqr3mAO3XRqwD7U31/wHVfmse4JoAxQBnX67cxxmxCQlXvdj1eCwlfnWiM+dsY86kxJs19XWPMncaY1caY1cePF8B8CD8/WS0/uQf+/srXo1H5Sewx+GoAJMTBLT9A6VqeOa9/AFzzGsTsk3LP+VFSoqv3Uh8oXDztYwIKS1/CnXPg6Gbvji8vWjEezhyGXs+7V9kW5Lhrx8mH//RREHPQvedZKxOlam2gTFj2x5wiMEiKCTnResJa2SHMalhrjQ5QqZmELWvKgcrrkpPhhztlR271585X9U1PVCRsnAKt7pAFmo4PQYPr4M+nYPdC34zJFyKmyYJnzY7pH1OyGpQO8+6EMC5K+rAW0N1ByHiHcCnwJrDVdZvy9QhwdQbP8whr7QprbSOgFfCkMSYICACaA+OttVcBcUiOY1rP/9ha29Ja27JcuXJODzd3qtMLqreHBa/l7x0X5T3nY+CbQRI2N3wKVGzs2fPX7Cjx+0veluT7/Gb3fDgblXYyfWot75AGvcs/8Mqw/pHXwpfioqX3Zd2+We8PVShY8gkTzsO026XvVGYOr5fQ1KY3ZW+8aakY7sxFanSk7PS5mz+YwhjZJYyOlEI9SuVlC1+XXakeT0taw+xnfTOORW/I7mAHV8aVMTBgvCwsTb0dYg74ZlzedP40bP8TGt0Afv4ZHxvWTXoVeivCbeuvEvnRUCeEV7DW7rXWzrfWtrPWLkj1tTZVOGdGDgLVUn1f1XVfmse4cgRDgEtKv1lrtwCxQGNkl/GAtdbVaZdpyARRpcUY6b0Ve9RzvbdUwZVwDibdBMc2w9Bvsn6h6a5e/wPjB3/8x5nz+9LG6dK7rnavjI8rWloqWG6YAmeOemdsh9fDm/VhWx6aBCx6Q8KPej6bveeXqwvXvyutGdy5UFw/WS7qGg3I/Fh3VQqHuGNw5ojnzgnSfxCy93vasD+EVNMWFCpv2/GXhIWHD4OOD0PnRyVE3NuNz6N3wobvL+4OpihcHIZ9J9WAvx8hi1P52dbfpMhV6mb06QntKlFIB1c7PSqxeYYUY6zg4UXuPCTTHEJjTFtjzCpjTKwx5oIxJskY404H6VVAHWNMLWNMIWAYMOOyY2ZwMUdxMDDXWmtdzwlwvX4NoD6wx1p7BNhvjKnnek4PQGOqMlK9rYSnLXlb4uiVyo7kJFnF3LsUbpgAdXo691ohVaDzv2XFLnKOc6/jbQnnpGBOg+slVDAzbe+VXStvVHy8cFZCJ2OP5J3qxCf3SM/GZjdD+frZP0+TwdKDb9n7clGQnqQEKQhRr2/OCihdzqnCMvuXyzjL1Mn6c/0Dpaft3iVwcI1nx6WUN5zYDdPvkAv8a8fJAnmrURBSHWY/4933uIWu3cH291/5WNk6cMNHcGgtzHzUe2PyhYjp8u9ftVXmx9bsJAvD3pi8nzsJuxfI7qC7aQf5kDtFZd4HbgJ2AEWAUUj10Ay5dhHHAH8glUCnWGs3GWOeN8ak7Ml+BpQxxkQCD3Mx/LMjsN4Ysw74EbjXWhvlemws8K0xZgPQDHjJjb9Dwdb9Kdmqz695Wcp5u+bD9plw9YuZhzt6QrsxUgxj5uP5pyjSjj/hwhlo4sbqKEDZ2jL5WPWp8yHfs5+BqO3SsubYZqkCl9vNfQH8AqCbB3aSr34RKjeHn++T1fy0RM6WcF9PhovCxRVpT+cR7lshuY7ZrYTa/FbZzdbiRiqvuXAWpoyQPw/96mLz84DCUoH98HrY9IN3xpKyO9jyX1C8QtrHNLgWOv0b1n4Fqyd6Z1zeFhctlbMbD3Rv0lWkpLwneyOPcNssSE6EBpfXvSxY3PqksNZGAv7W2iRr7USkFYQ7z/vdWlvXWhtmrX3Rdd/T1toZrj+fd7WzqG2tbW2t3eW6/2trbSNrbTNrbXNr7U+pzrnOlRsYbq0dYK3Vba/MVGwsJe6XfwSnD/t6NCovipwjK5wtbvfO6wUUhj6vQvSO/BPuvHEaBJeDmp3df067MXDuBGyY7Ny4dvwFKz+GtvdBv3ESNrPwtdydT3job9mta3sPlKic8/MFFIYbv5QV6Sm3pd1yYf0kaZhcu0fOXy+1oBJSQOGIB3cI46LkdycnYd1BJaDFbbD55/yZz6vyJ2ulmuiRjTDwU1lYTK3JEKnAPud57yw2LnxDdtw7ZNKtrdt/oHZP+P1R2L/K+XF525afZdLlTrhoirBuEqFwPsa5cYFUFy1RFaoU7Aw0dyaEZ10hn+uMMa8ZYx5y83kqN+n2H0hOkAs9pbJq5xwp2pGy0uoNdXtLuPOCVz2fX+Vt52Ng+x/SDsY/IPPjU9RoD5Wvcq7iY1wU/HSvtPvo8bSMrdMjsoIeOdvzr+cJ1sJfz0CR0tDxQc+dt2R1GPgJHN0oF2WpnTsJ22ZC+I2Z9znMjkoeLiyz35Vmn9UKo5drc7es5ueXRRmV/63+TBZvujwhnyGX8/ODXs/Cqb1SddRJ7uwO/jMuf3n/CakCU26Vat75ScQPULYuVGzi/nNCu4JNkuIyTok/IwveDa4r0OGi4N7EboTruDFIVc9qQBam+CpXKF1Lem+t/Sr9kCgltvwKn/aCNV/kn3DFnIg5IJUVazuYN5ieq1+CpAsyAcjLUpLpsxpua4zsEkZHwo4/PDsma2HGWDh/CgZ9cjGvMXyoFBRZkEt3CXfOkXyPLo9BUIhnz123t0yI//4a/v724v2bfpSfw6bDPPt6KSo1ld6ensrz3rdceoRWvipn5wmpKosYa7+Cc9ryV+Vy+1fBzCegTm/o8nj6x4X1kObwC1+TdBqnLHrTvd3BFEVLS8G2cydh6kj3Kh/nBacPyaSu8aCsTbqqtoLAos6Gje74Uz6bC3B10RQZ9SEsZ4xp6Ko2et5ae9pa+xyS9+fw/q1yROdH5SJhnqZdpitiuqzOHd8KvzwA714lhSt8Uf3r0DoZw9qvvf/aqaUUdvF0qJw7yoRJCfwNky9WTcyLNk6THSh3kukv17C/hLMs83ALirVfSq5gz2ehQqOL9wcUkp23Aytl4pWbJCfDX89CyRqy6u6Erv+Rgga/PQJHIuS+dZOkiXzFcGdeM+W8nsoj3L9Cegm6U7woM+3HSCXXNV/k/FxKOSX2mHx2l6gshc8yyp01Bno+J21Zlr7rzHhO7JKqxC3/BcUruv+8ik3g+vekoNOfTzkzNm/b9BNgsxYuChLKX6ODs4VlNs+A4PKSb13AZbRD+B5QNo37SwNanSQvKl5RQoAipjnTCDmvW/+9VFqs1hoe2gS3TJfwjd//De82g+XjnS/ukXhBJg+f9YaPu8hF2NwXfFv1ceccKF5Zmnj7QqdHoITr/yE5yTdjyInY47LC2Xhw9kJS/AOhzV2wZ5EsEnhCVCTMelJCctrcc+XjzW6B4pUk/yU32ThFQjp7PC0XC07wD4BBn8nu45RbJV/xwErZHXQqpCil0ugRD7wvJ5yXMVf30AVOpaaym7JigkZMqNwpKRGm/UvyrYd+IzttmanSXHa/l33gTErCwizuDqYWPkSqTK8YL9cleV3EdFn0KpuNisehXSUf2ok+jQnnJIe+wbWZ90UsADKaENa21i68/E5r7SLAoWVS5bgOD0BQSZj7P1+PJHf5+xv48S5ZjbpluhRUqN0T/vUH3DoDytSGWU/AO+FSrTU+1rOvf+YozH8F3m4ipbJjj8HVL0Pf16QVwGEPTQSyKilRJjO1e/guvr5QMPR+QYoE5MVdis0/SR5ETqqztrgNChXzzC5hUgL8MFomVAPGp72SHhgkJdL3LIK9y3L+mp6QcF4WRyo1kws5JxWvAIM/l9YWX/WXYjNNbnTu9YLLyqKHJxbqDv0t4a3V2+X8XCnajYUzh7xXmTEvsjZvLljlB3Oelfeqa9+WfFx39XhKflfmv+LZ8ZzYJXmMLW7P2u5gar2ehxodJUooLy/gn9gtvQSzujuYIqyb3DoRNho5R3odNtBwUch4Qlg8g8ccyKpXXlGkJHR8SOKm9y719Whyh9WfS7n5sG4wfIpMQFIYA6FdYOSvcPtMKRH/19MycVv4Rs7zDw6sll3JcY2kgW7FxjB8KoxdC+3ude0q+UlBC19IqfDli3DR1BrdIGF8c/8HZ0/4dixZtXGqhBumDsvMqqAQaQOw6QeIOZiz8Sx4TXpeXft2xhU6W4yUqpoLX8/Z63nKqk8gZj/0ei77rRSyomYH2Yk8HwOh3aBEJWdfr6KHCsvsd4VWezIEqnZPiRBY+n7uzCvNDf74D3zU0fOLhSpjm36Cpe9Jj8FmWWwJUzpUQjrXfgVROzw3pkVvSkuc7OwOpvAPhCETpZfo97fkvc+9FCmLSI2zuYhXvqGEdDoxIdwyQ/59a3b0/LnzoIw+VSONMddcfqcxpi+wy7khKce1vhOKVYTZz+Xsw91aKUe+6UdY8bFUa8prVkyAXx+COlfDsEkZV9Gs0R5u/QnumC25YHP/B283hnkvZ60YRGK85CR93A0+7SE9cFrdAWPWyO5k3d4XL3iDy8iF3XYfTQh3zpEJaWhX37x+CmNkt/T8adklyitO7ZN8ruyujqbW5m6wybByQvbPsW85LHpDmrk3GpDxsYWKSv7YzjlwwMfNyc+dlAWYsB7e/Vns8IDsTvf2ws9cpaYSGnUhLmfn2bdCIhqC08r4yCY/P2h3n4TreqMvWF5zar+0bjm2WXp6+pK1knYQe9y34/CG49tkMbdqK4moyY7Oj0FgEZjznGfGdGK3fL63vD3ni0jFysPQr+HMYVk4zos70BunyzVMyerZe74x8p6/a75nF6MSL8i1V71+zlSOzoMymhA+BLxtjPnCGDPW9fUlkj+Yg2UP5XOFikLXx2UleXsWKhfGHpNfoHkvwTeD4fXaEkI5dSTMfFRi+JMSHRu2xy19D2Y+BvWvlbwDdwswVGsFN0+BO+fLrtWCV2Ccq69RXHT6zzt9COb8D95qCD/dLYUarnkDHtkCfV+VRuRpqdtHwiWdiKHPTOQcqNJCVtF8rUJDaD0a1kzMOyE0EdPl1hMTwlI1pMDM6i+yt/hy/jT8cKd8MPd91b3ntBol//e+3iVcPE526np56KLNXcZIUaMKDZ1/rUrhMuE/uin750hOlgWInLabSEuTG2Wlfpk2qr9Cyr9J40Gw6lPY5cNiTGu/lLSDv/JJQZL0nD8Nk2+WydyQL6UYVnYUKyfh8Vt+8Uz/v392Bz3UEqdqS7jmdVmYm/eiZ87pLce2wLFNOf/8C+0Kccdz9t54ud0LID5G2k0oIIMJobV2O9AEWADUdH0tAMJdj6m87KoREi4x5/m0C5acj5EPtcXj4PsRMK4xvFEHJg2Vi8PTB2Wi0u9NGD1PJjY7/oRZj+eNkKKFb8Cf/wcNB8CQL7L3YVL5Khj2Ldy9REIqF70loaR//t/FHkLWSmjulNvk33DRm7KaOeJHuG+lTHAKZxSdDdTrK7fbZ2V9jDlx9oSEjPqi3UR6uj4p/ed+ugeO54G3oY3T5P+7dC3PnK/dGPkQ+/ubrD935uMScnnDx5n/zKUoXFyKG2yf6btJeMwBWP6RtMPISg+rvCalsExOwkajd0hhjZw0pE9PYJBEl0TOhqObPX/+vCouCtZ8KRPm69+H0mHw8xjfRMxE7ZBiUX6BErnjqTYmuY21sjN4YhcMnijF33Ki3X2y2PHX0zm7fjmx25U7ONKzIeYtRkLz2+T6Ycsvnjuv0yJ+kAijhplEo2QmtKvcejI6YfPPUKj4xRxFRbodko0xxlobD0zM5Jg8cPWvruAfCN3+KyuJ676Bcg3k4v/QWji4Vi4sUpSqKRe1be6WylwVw6FwsUvPV6W5hMctfVcmmu3u8+pfx23WSgL5glfkA3zA+Kw1Ck9LxcZw45dwbKuE4y37QFpVhN8IB/+WMKugEGh7j+y4ZHVyULYulKrlCi0dlbOxZsWueYCVML3cokhJGPChFEUZ317yYTs94pny+p52bCscjYA+bu7GuaNqS9n9Wf6hXJy7Wxlt04+w/jsJj8pq9cnWd8pu+qI34Mavsj7mnJr3EmCh+3+9/9reVKKKLHbkZEKY0prFiQkhSGj7ojflPW6Ah9ug5FUrJkDiOWnVUqiovD993kcmF9eO8944Ei9IWGFAYVnk/O5G2DBFKhTnN0vflfyv3i9ArU45P1/hYhI19dsjEjVVr0/2zrPoTTD+8rPgade8Lp8nP94DZetBubqefw1PslYq2tfsJEW6ciKkilwH7ZonaQw5lZQovYHrXu1cteo8KKOQ0XmuMNFLAn+NMYWMMd1d4aO3OTs85ahGA2XFfcZY+Kyn7O7tWiClgbv9n+SzPbYbHlgvyc3tx0ge3eWTwRQ9n5NqTX/8V5q75zbWyo7oglckh+qGj3I+GUytfH0Y9Cnct0pCJP7+VqpLXvs2PLwFrn4xeztFxsgu4e6FOc8vyorIuVKRtkpz772mO+peLfmWjQdKY+Hx7WDnXF+P6koR02R1tNENnj1vu/tk8cXdleKYg/DLgxL62+WxrL9ekZIyKdw8Qya53nR0E6z7Tl4/uzkoeYUxEjaak9YT+1dA0TKSQ+iEoqXhqpul/YcTpfrzmvgzktNb/1ooV0/uq95WdtVXf+7dfMv5L0k16uvfk/fISs2kKnN+W7PftQBmPyvh8+08MDlI0fw22d2d/Wz2cvVO7nHtDt6WcbGu7AooDDd+LbeTh+e8oJ3TDq+THdycVNdOLbSrRFslxuf8XHuXSCSFNqO/REYTwj5AEjDJGHPIGLPZGLMb2AHcBLxtrf3CC2NUTvHzgwEfQZcnYOi3Mmn59za4aRJ0eVRCBd3p55P6fDdMkAnE9FGy05hbWCuhnIvfkvCL6993ru9M2dqySvyfQ3DPUkkuT125NDvq9oGkeGcbtKZmreQshHbNnf15ipWDgR/DrT8DBr6+AabdIe07cgNrpbporc45Xx29XP1+smvvTguK5GQJr01KgIGfZD95vu29EFhUVsC9afaz0gKm0yPefV1fqdRU8m6y2+9v33Ip4OBki5i298rP06wn4fRh514nL1g9UdIrOj586f3d/88VOjrWO6GjuxfB4rdlUpOSE9VipBS5ObDa+df3lpgDUqugTB3o/4Fnf879A6Wq8PEtMrHLqkVvygJgx4c8N6bLhVSRaKQTu+R93Zf9iTMTMV1Cl+tf65nzhXaDhO59LBsAACAASURBVLOwf2XOz7XlFwgokrvSYXKBjHIIz1trP7TWdgBqAD2Aq6y1Nay1o621f3ttlMo5FRtDtyelMacnVrUKFYWbJkNwOZg0THYyfM1ayZ9a9r7sNFz7tnfK1gcGee4Dq0Z7KBzivWqjxzZLZbPc/oYZ2lUm3V2ekBCi91vJyryvPygPrpUV48YeWh1Nzc9fLsoPrMz8w3HFeEme7/MylAnL/msGl4FW/5Jdz+id2T9PVuxeJHnJHR/O2sJUXlYxXPqiHc/GTmzscTix07lw0RRlwiRaZNOPUmV5+mhJNyhoEuNlUaZWZ6ja4tLHUkJHY/bDnw4Xdzl3UnrolgmT3/MUTQZDYHDe7N2alsR4mHIrJJ6XInDu5kFnRcP+Ekkx7yVpWu6uk3slkqG5Q7uDqdXsKKGyW3+Fv30Qwu+O5GTJH6zdw3Pv3TU7SDhuTnfdk5NlQlinZ84X6vMZt66KrbUJ1trD1tpTTg9I5QPFysPNU6WR9HdDZQXVV5KTpa3EygkSXtL3Nd81WM8J/0B5c93+p3cmO5Fz5Dasu/OvlVOBQbKocc9SqNxU/r8/7w1HInw3pohp4F/IuQpmzW6WvNSMKj4eiZAdtnr9pIdhTrUbK3+nxW/l/FyZsVZysEpUyZ85UOmp1ExusxM2+k//QYcnhCAXpGPXQKvR0iP1k+7waS+5CExKcP71c4P1kyD2yJW7gymqt5Xw7jUTnYvssFbCwWOPSgRA6gvcwsVlUhgx3befwZ4y6wlZeBjwoXP5c8ZIQ/jTByU31F3e2B1Mre09sgO9zcuF5ty1f4X8G3pyQTQoRCbrOZ0QHlglv7cN+ntkWPmJF7ZJVIFUvr6ENkRtlwqbvrhISE6S/Mg1E+VDu/cLeXMymKJeX4g7JoV/nLZzjhQaymn1Nm8qWwdunSFhyyd2w4TOEibszbxLkJ+7iOlQp7fk3zmhcDFocbusdJ7cc+XjCeel8E6RUpJT5Imf++IVZAV8/WRZEXfS+snyc97tv1JWvqAoHQqFimWvsMy+5eBfGCo38/y40lImDPq+Ag9vhj6vSFn4abfDO02l4nJebaTtjuQkWPKOVJoO7Zr+cd3/T/I5Z4x1Judr/STY/JP8nqSV691ipBS82TDF86/tjthjUpBq2YfSjmPt1zKWTT/JQsLOubBniYS1Ht4gfQVP7JYWTXHREm6beEHy8Vd/Ln1BGzp8IV+zo/QlXuzmz/DJvbDuW3lv9NbnpTFQvZ1MvHJjjmjENAnJTKmQ7ilh3eRzISfVc7fMkIXNuld7blz5hAcraih1mbBuUmVtxlip3nXdO96bkCUlSoz9xinSqqDL43l7MggSvmn85YO0akvnXudCnCRvt77TuddwijHQdJhMxmY/Ixcjm36SCm2e/nBKz57FsmLvid6DGWlzl+wQLh9/ZV/BOc9J2O/N0yXc01M6PCAXZkvegWsd2inc+pu8Z1RrI/+XBYmfnxT6yk6Lj/0rZILi7ap5QSVkx6L1nRLiu3y8/PwteA2aDpXq1OUbeHdMTtv8k+Rx3fhVxp8rgUWg/4fw+dXSF/C6dzw3hhO74PdHoUZH+b1MS+WrJAx5zRdSodrbn4F//Fc+gz2hZifo/rRnzpWZns/A+A4yKez9QsbHLn7Lu7uDKaq1lgrx0ZGyGJpbJCXKZ269PukXIMyu0K6w4FX5jM1O9I21UhwttJu8b6lLZDohNMYEA+estcnGmLpAfWCmtbaAxIWoHGl+q6z4LX5LVpTT++DypKQE2R3Z9CN0fwo6/9v51/SGoqUlDGn7LOjhYF7KniWSx1Q7F7WbyKqipWVnrOlwCSGdNEyS2/u+5vwqbsQ02eWpm83S5e4qUVlCctZ+7erP6NqN3Dn3YluKOh7OAQ2pIlUm//5afq88nS+z5ReYOlJCJ2+emjsLGjmtYrj0mUxOdj/XOeEcHFoH7e51dmwZ8fOXRZd6faU67IqPZKd3zRdyAdb2Hqjdyzv5206yFhaNk8Im9d24KK3eRnIul74nu1ueCMNPSpDcTT9/GDgh/d8TY2SX8LeHJa/58lxHJ0XvlPfCtvdJkbrEC1IY7Z/b82ncFy+fPZfcxkuj9+a3ebYqeEYqNIJmw2HFx9D6LihZLe3jTu2T39UWI70fTZOSK7x/Re6aEO5ZCGejnFkQrdpKPlt3zsvehPDwOojZJy1G1BXceWdeCAQZY6oAfwIjgC+cHJTKZ7o/JS0u/npaVo6clHhBLig3/Sgre/llMpiibh/pReRksZ6dcyTco3p7517DW2q0g7sWQs9nJS/yg9YSvpSU6MzrJcZLw9v6/aSwhNPa3QcJcRcLR5w9AT/dK32qej3vzGt2fMgVMveuZ8+7eYb87lZuDiN+lJyRgqhSuPyfnshC8Z6DayE5QcLIcoMKjWRB5qHN8v5/fKv0xXu/peRm+aJpu6dEzpbesh0fdH9y2+2/MoH82UOhowtfh4OrpUBaSNWMj20yRCoEr0m3pbQzFr0loXkdHpDQ9eIVpHVM2dry81Glhbw/h3aFur3lAr/JYJmItbxdIiA63A+dH5X3HG8Xlur6pNzOeyn9Yxa9BRjv7w6C/DwFlbzYezS32DgdCpeQxR9P8w+EGh2yn0e4eYZEWdW7xqPDyi/ceTcz1tqzwEDgQ2vtEKCRs8NS+YqfnzSAr9ZGqqHtX+X517BWEqw/6ynVt/q+Bu3Hev51fC0l7NHJZPLIOVLRKzc2e8+OgELygX3fcqnW+seT8Ek3ZyojRs6RAg5OVBdNS6VwqXK4YoLsGvxyP8RFST9Mp3LvStWUUM41X0iOkCds+kkmg1VaSP/TghzOU6mp3GYlj/CfgjJtPD+enAguI4tyD26EQZ/JRf3Mx+CthjDrP2nnv+Z2i8dJsaMmN7r/nMAi8hl45pDkNefEvuUyIWw6XHqxZiaohOzWREz3Xu+6k3thw2TZOfN02x1vKVlNJqXrJ6VdoCxld7D5rZlPyp3g5ye/7/tXeP+105MYL1Ee9a917vohrJsslmV1UdxayR+s1angVK3OIrcmhMaYdsDNwG+u+wpgHI/KkcAgGPYdFK8o4XueuhCwFrb+Dh93hUlD4dwpGDwx/1YmLFtHqos51X7i5F6I3pH7201kR6maMHwKDPlSJjKf9oS5L3p2tzBiGhQpLR9a3tJujFxoTrlVPox7PCUTRSd1fFhCuTKqcuquiB+kt1jVVjoZBChXX3ZWsjIh3LcCytbNvRc6/oGy+zNqNoyaIzm+KyfAey2kJVBeKUCzb4U0tW4/VhaasqJaK/ldXfvlxSrOWXU+RtIhSla/Mm84Iy1ulx5uG6dm73WzavE4yavzRoqIkzo+JO9Hc5678rFFb108xleqtZbCfbnl9ydyNsTHOJs/H9pVbrO6S3hsi+RbNtBm9OlxZ0L4IPAk8KO1dpMxJhTwUndsla8El4Wbp0FyInw7JGeVoqyV4hMTOsPkm+D8KWlUO3aNe6umeVm9vpJU7UTY1c6UdhN5OH8wI8ZAowEwZhU0vQkWvgZfXOOZqpnxsbI40WhA9hvAZ0ftXjIZ2Pa7FF5oN8b51yxbW8LAV32Ws4uRjdNg+ihZ6b5lmjO9xfIa/0Ao39D91hPJybJLkNt2B9NTtSUM/kx2Da8aASs/hnevklDu/2/vzuOkqs78j3+eptllkUVAaETWiAqoCEYBAU0Ek9EYTdTEjGZz1BjNYjKa/H6ZSWbym8mYxExWNWpi1MR9wcRECO5GQVRWURZFQEVAZRORpc/vj+e2Fm1V9b23q7qqq77v16teVXWr7lOnqk9X3XPPOc/ZvbPUpcvvsZ/6CZ+0y7hM+a7/r864KN1SEPd9Cza/4ktMJDlx0v9w6HOoDxstdlbKzWu95+yws4q/Jl+xdeoBE7/pyZJeevT97ZvWRL2Dn8s9v7AlvDePsACLtRfC4jugU08YfGzxXqP3h2CfvskbhEtnAOa9l5JVkw3CEMLDIYSTQgg/MrMaYGMI4aIWKJtUol7DfFHZN1+KFplNeADQsKjoVRPh5s94o+jkX8OF8/wHqCUPxEtlxHSfcL/ygcLHXjEbutWV1yT1YujQ1dezOvVaP3N45UT/MWuOF/7qKd5barhog5oaT2/fawSccmXLJWKZ+E3Yuc2zSqax8Dbv7Rh4lCeQUWPwff1GeQ9hnIP3jcv8hFi5zB+Mq+v+8E8/g/Me82yY918Gvz7KT/SVYyr915d4Qq/x56Vf0Lpth/RDRxfeBgtv8YzZdeOS7WsGR5wN6xbBq88m2zepx/8XCKXtOSukcef6EOFZ33u/XjasxZprDcqWsv/hnnCnHIaN7nzbfwNHnlzc4zAz7yV88aFkazI/N8O/I1vrEOYW0GSD0Mz+aGZdo2yji4HnzOxbxS+aVKwDJ3rCgZce8QyQcX786+s9WcdVE+GWs2DndvjElVFD8LPV0RBsUHeUTyYv9DzCPbv8bzJkautfoiOuQ0+D8x71hY5v/wLc/RXv6Utj0W1+4FCKA/ORJ8OFc1t2LkufkZ4IYs5VyXs7Ft4Kd53rCQI+e1vh05O3dv1G+wiKzWubfu7qJ/y6obegtelzsCcR+kyUVfbmz8D1/5Ru6Y1ieuxnnuFw3JebF2fAWB9y+swffIhdHG+97JlC68b7iZg0Rn3ak4U9c326/ePYug6evt7nGHcfWLzXaUltO8KU7/j6d8/d7f+Tz9zgJ6BL2TsInris76jyaBC+8Fcfllzs5ZbAG4Tb3/AEe3G8sRLWL4GRGi6aT5whoyNDCFuATwB/BQ7EM42KpDfmTD/TOf9GePQnuZ9XX+8JJ66cEPUo7oBTroavzPUYLZWGupy0qYVhH4Hl93u2x0JZ+xS8u6Uy5w/ms+8g+PxfYeIlvsDwVZM8hX8S29/04baHfLL1p9VPYtK3fM7I3Kvj77PgZk8uNWiCz+lM29tSyfpGiWXiDBtdMwc69fJF7VsrM880ef4/4MQfe2/cVZPgnq94I6PU3lrlIwiOOKcw8zQnf8d79OMMHd2zG+4810+cfvLq9L95Hbr5wfqi24uX5fUfv/Bst6XuOSu00WdC74Ng9g98HTyAiWXyHuvGe4K0PSVeCW7xndClX8tkJx882a/jDht97h6/1nDRvOIcubQ1s7Z4g3BGtP5gGY7nkFZn8mWeEvuB//AfqUz19f4Fc+UxcNvZPkTyk7/1huDo06uzIZhp+DQ/Q7Z2XuFirpjtKZmLOf6/XLVp68lYzvmzn3S45vjo4CbmkJTn7vG5sS09XLTU+o2GYSf4/K84Pavz/wh3nedzHc+8pWWW5miN+hzsSTniJJZZ/aT3DlZCr36btt4Dd9GzvqTKglvg54fDw5f7qJBSefzn3ntZqPm57w0dfc0Xb8/nsSs8i+zHfuInr5rjiLN9mHdzh8dn8/ZGmHedZ1/tOaTw8Uuppo0vXfTmi96ze9hny6cHdOB4/80qZY/6O5tgxSyfV94SJ0S79vO5hC/GTGeydIYPry11j26Zi/OXuwpYBXQGHjGzA4AWyl0sFc3ME8EM/LCvnbb6Se/xWnQ7/ObDcPvn/SD71GvhK3N8yEs1LlSdzdDjfe5AIbONrpztmR6rdf038F6r8x6D4Sf4HJ+bToWtrze936LbfV2ohiUDqsmkb8E7b/rBYD7P3uj/54Mnw2fUGMyrXSdPPtLUQd7W1+Gtl1rvcNFcOnaHE37o3/tDp8KD/+lrGC68Ndm8oULY+rrX3dFn+IFooQw4Ao6+CJ69AZbnGDq6dh489F9+omlUgmUucr7mkZ6wqGHd0kJ64pew6530Q1rL3fATfIh7TW15vce6jAXqS+X5P/tJ+0NbYLhog8GT4eUnYNeO/M/btNrnzWq4aJPiJJX5eQihfwjhxOBeBlowp7pUtNr2vhxFtwHwpzPh1x+GO77oj516LVzwpM/zUkNwbx27e0O6UPMI397owySHVmh20SQ69fDERx+/Al7+h/dSL5+V+/lbXvVU9IeeVhm9NEnVHek/zv/4hR8QZvPMH+CeC305jjP/VLw1EitJ31FN9xC+t/5ghTUIG/Qc4v+L5/zFs1Tf+WVfa7YlF+Oe8xsfBnnM1wofe/Jl3tNxb5aho+9u9Qy8Xft772AhvlvMfNjrq88mHxafz/Y3Ye5v4eBTfD52JTLzY5Jz/lI+vYPgJym6DXz/u6AUFt/hvdf7H95yrzl4iidxa6ohvPRev9ZyE02Kk1Smm5n91MzmRZef4L2FIoXRqYcnlqhp48OkTvsdnP+EGoJNGTEdNiwtzJqOKx8EghqEDcxg7Bfg3Ieg835w02nwt8t84d3GFt8JhOobLppp0rfh7fWeUKKxeb+DGV/1unWGGoOx9Rvt2Sjf3pj7OavnQG2Hyu+ZHjQBvvyQD7Pc8ipcdwLcenbxF7bfsdmXVhl5cnGGQbbt4Fmyt74G939n78f+eilsehk+eZWfACyUUZ/2OlPI5DJzrvShqJMuKVzMctS1X3n2xg8c70tPlCI777YN8OLDPj+1JU+IDop6a5uaR/jcDOhzSOUNYy6COENGrwO2Ap+OLluA38UJbmbTzOwFM1thZpdmeby9md0SPT7HzAZF28eZ2fzossDMTmm0Xxsze9bM/hynHNIK9BwCX18CFzxRfYk50ho+za8L0Uu4cravr9VvTPNjVZL9DoIvP+Cpx5/8NVxzHGxYtvdzFt3mn1uvoaUpYzkYdIwPp3r8f/duNM+7Dv78NV+I/PSb/ABY4uk3yq/z9RKuedLPyiddJL01qqmBMZ/xtWaPvRSW3Q+/PNKXAyjW/MKnrvFEW8VcQmHAEb6A+7M3vj8SYcldnnBt4jfhgAIn6ei4r/fkLbwtfUblTDs2w5NXesKOPgc3P54kVzfeTypsWt3yr/38vRD2tEx20Uztu/gQ6HzzCLeu8x5E9Q7GEueoe0gI4d9CCC9Gl+8DTaYzM7M2wK+A6cBI4EwzG9noaV8E3gohDAWuAKL0TSwGxoYQxgDTgKvMLDOLyMXA0hhll9aktn11DrlLq+cQn2fU3HmE9fWeUGbIFPXIZtO2A5x4OZx5sy8KffWxPgQyBE9n/dp8782udpMu8R6t+Tf5/aeu8WVlhp3gw/7UGEymbxMNwp3b/bGBrWRB+kJp1xmmXOYNw0NO9ZMQv50K658v7OvsesfX2Bx6fPF7YBuGjs64CF5/Du69GPof4Zm4i+GIc2DnVlhyZ/NjzbnaMw1P0mpkJVMXfQeUYoH6ZTOh+wE+N7WlDZ7sQ5+3v5n98aX3AkHzB2OK0yB8x8wmNNwxs2OAHBNF9jIOWBE1IncCNwMnN3rOyUDDuIXbgePMzEII20MIu6PtHcjIampmA4CPAdfEKINIZRs+DVY9nnwduEyvL/bhfkM0XDSvEdM9Lf6AI30I5G3n+HAyzLOrVbvBU6D/WM+K+ORv4C/fhOHT4fQb/GSPJNOxux9o5Vp64tVnPOlWa1uQvlC69YdTroSz7oS3N8Bvp8D8PxUu/rM3etyWWGC9tj184tew7XV/H3t2e1btYq2vWzfeG6DNTS7z7lZ48ld+0md/jS4pmf1G+hqZLT2PcNcOeOlhHwFSipP5g6cAAVY9mv3xpTM82VvvD7VosVqrOA3C84BfmdkqM1sF/BL4lxj79QfWZNxfG23L+pyoAbgZ6AlgZuPNbAmwCDgvo4H4M+DbQAunGhMpQyOme8KDFbPTx1gZ7av5g03r2g8+d7enIH/+z34wdMAxfnBa7cy8l2DTavjbpTDiY/DpP6gx2Bz98iSWaViQfsCRLVeecjT0OM8MvP/hcPd5vnZhc4eQ7tnlS00MGOf/3y2hfzR0dPcOmP6j4s55akgu88rTsG5R+jhPXQvvvAXHfrtgRZMU2tTCgLE+p7glvfy4L0Y/7KMt+7oN+h8O7bpEORAaefsNP1k+8iSNPIspTpbRBSGE0cAoYFQI4TBgarELFkKYE0I4GDgSuMzMOpjZx4H1IYSnm9rfzM5tSISzYcOGYhdXpDQGjPM5IcuaMY9wxWyfdN2lb+HKVclqarzX4Asz/Uz70V8tdYnKx/ATYOhHYNTp8KnfV8fctmLqN9rXPtuRZaWn1XP8zHchFkpv7br2g3++x09IPHuTDyHd8EL6eIvvhM2rffHxljyYnPp/PaHaYWcV/7VGnQ5t2mdPBBXHzu2+1MTgKd4YkdKqGw/rl2T/riiW5bM8QdGgCU0/txjatPXXzpZY5oX7fG6j5g/GFjtzRwhhSwihoaZ9I8YurwCZq0AOiLZlfU40R7Ab8Eaj110KbAMOAY4BTop6Km8GpprZjTnKe3UIYWwIYWzv3r1jFFekFWpT62fnls/0YUZJvbvNU7gPKfo5nsoz4Aj44kwYMa3UJSkfZnDW7fDJq9UYLIS+0dy11xfvvb2+HtbOfX/ukPh34dT/A2fd4UM9r56cbghpfb0Pe95vpA+FbEk1NdBnZMs0Qjv1gIM/AQtvSdej+vTv/XNW72B5qBsPoR5emddyr7liFgyaWNo1ZYdM8bVYG2ccXjrDlwep9AzMBZQ2lWOcb6ungGFmdqCZtQPOAGY0es4M4Ozo9mnAAyGEEO1TC2BmBwAfAlaFEC4LIQwIIQyK4j0QQmiBU2kiZWz4NB+2szbFhPJVj/qQUw0XFSk/DQczjYeNbnje5w1X6/zBfJo7hHTZ33w5nwlfr/xs10ec41lUl9yVbL9dOzyZzwETCp8FVdIZMBawlkss88ZKeGNF6YaLNhg82a8zewl3bPZhpAdpuGgSab/tmlzsJJrzdyFwP54R9NYQwhIz+4GZNfThXgv0NLMVeK9jw9IUE4AFZjYfuAu4IISQZzEmkSo29DioaQsvpMg2umI2tO2kA0uRctSlD+zTB15rlFimYf5gtWUYjSvtENIQ4LGfes9CNSSKGvhhz1SdNLnMszfAtnXqHSwnHbr5sh+rWyixzIq/+/Wwj7TM6+XSazh02X/vBuGy+/1E98jGeSwln5wNQjPbamZbsly2AvvHCR5CuC+EMDyEMCSE8MNo2/dCCDOi2ztCCJ8KIQwNIYwLIbwYbb8hhHBwCGFMCOHwEMLdWWI/FEL4eKp3LVJJOnTzdeDSzCNcOduHfCjxh0h56pslscyaOdB5P9j3wNKUqTVIM4T05cdh7VNw9EW+f6VrSC6zdi68viTePrt3wmM/8yGKB04qavEkobpxsHYe1O8p/mstn+kZPHuU+DvIzHsJX3zYh3sDPHcPdOnnWa8ltpwNwhBClxBC1yyXLiGEKvimFGlFhk+Hjct8GEdcb77oFw0XFSlf/Ub7ENFdO97ftvpJ7x3UcKimJRlC+uhPoXPvlknqUi5Gnwlt2sVPLrPgj7BlLUz6tupfuak7yteXXP9ccV9n53Z46dHSDxdtMGQKvPOmL9Gz820f+XTQP1X+kO8C06clUgkaEpsk6SVsWKpi6PGFL4+IFEa/UZ4tb33Ug7N1HWx62Q/+JJ44Q0hfne8jJo66ANp2LE05S6FTDx9at/Dmpuda7tnljeb9D9OJxHLUMIR8TZGXn1j1KOx5t/TDRRsceKxfv/igZz7d/Y6yi6agBqFIJdh3EPQ+KNk8wpUP+MLXPQYXrVgi0kzvJZaJ5hE2zBHSvN9ksg0hXXDz+48/dgW07wpHfrFkRSyZw8/2RBzP3ZP/eYtu85MR6h0sT90P8DnHxV6PcPlMaNu5fBIKdenjWYFffMizi3bqqe/HFNQgFKkUI6Z5sol3NjX93N074aVH/CyvfthFylf3A3ye8LqMBmFtR+85lOQyh5De9S8+hHTdIm8MHfkl/6yrzaAJ0GNI/uQy9Xvg0Z9An0NhxPQWK5okYOZzO4vZQxiCNwgHTy6v3AODp8DLT3hCmQ99rDrmABeYGoQilWL4dKjf/X72r3zWzIGd22CIhv2IlDWzvRPLrHkS+h/hizJLOtmGkNa2h6POL3XJSqMhucyaJ2H90uzPWXKXLzMw6RKdRCxndeO9F3fruuLE37gMNq0un+GiDQZP9mGsO7fBQcoumoYahCKVYsBYHyoRZx7hytlQU6sscSKtQb/RngVyx2YfOqrlJpqvYQjp5+6EjvvC+H+BffYrdalKZ8xnfPmiZ/7wwcfq6+GRH0PvD2luVrkbGM0tLtbyE8tn+nW5NQgPONrrb/tuOq5JSQ1CkUpR0waGneBf2Ht25X/uitl+JrFD15Ypm4ik13cU7N7hc97CHs2PKaQhU+Ebz8Px3y91SUqrcy/PzDj/j3tntAV4/l7YsBQmXqLMjeWu7yio7VC8BeqXz/T5et0GFCd+Wu33gTFnwvhzobZdqUvTKuk/W6SSjJjmvQj5zg5uW+/zkYZMbblyiUh6DYll5l4NGAw4sqTFqTg1NRoGCT5sdMcmT8zRIAR45HKfY3jIJ0tWNImptp3Pj11ThB7CHVt8nl659Q42OOkX3usvqahBKFJJhkz1NaXyDRtd+YBfK224SOvQa5gnknljBex3EHTsXuoSSSUaNNGzTmcml1n2N0+6M+kSH4Ui5a9unM853vVOYeO+9DDU7yqf9QeloNQgFKkk7bt4xrh8y0+smA2dekHf0S1XLhFJr6YN9D3Eb9dp/qAUSU2NL0Hx8uOwYZn3Dj78I890e+inSl06iWvgUZ5g7pVnCht3+UxfmkXfQRVJDUKRSjN8Ory5EjYu/+Bj9fXeQzhkquaCiLQmfaNlJjR/UIppzGej5DLX+8nDV5+Fid9QVtvWZMA4vy7k8hMh+KLvQ6aoLlQoHRGKVJoR0/w6Wy/huoWwfaOGi4q0NgM/7Afq5bIYtFSmfXr7Om7z/wgP/Rd0HQCjP1PqUkkSnXtCz2GFbRC+vhi2vqbhohVMDUKRStN9IPQ5JPs8woY1CpVQRqR1OeRUuHgBdK8rdUmk0h1xDrzzJrwyDyZ8TVkbW6OB0QL1IRQmXsNyE0OPL0w8KTtqEIpUouHTPNPo9jf33r7yAR96Vs3rd55eSQAAFr9JREFUbYm0RjU10K1/qUsh1eDAY2HfQbBPXzjsc6UujaRRNx7eeSv71JE0lv/dsx136VuYeFJ21CAUqUQjpvt6ZQ09guApo9fM0XBRERHJraYGzvgTnHUHtO1Q6tJIGnXRAvWFGDb6zlseR8NFK5oahCKVaP/DofN+e88jfOkRzzw2RA1CERHJo8/I9zPbSuvTcyh03Lcw6xGufNBPMKtBWNHUIBSpRDU1MPyjniVuzy7ftnI2tNtHKaNFREQqWU2N/9avmdv8WMtneeOy/xHNjyVlSw1CkUo1fDq8uxle/odPLF8xGw6cpAQBIiIila5uHGxc9sFcAknU18OKWZ5MpqZN4comZUcNQpFKNWQKtGnv2UbffBE2vazsoiIiItWgEPMIX5sPb2/QcNEqoAahSKVq19l7BF+47/3kMkooIyIiUvn2PwxqapvXIFw+CzDlHqgCahCKVLIR0+CtVTD3t9BjsF9ERESksrXr5EtFrG5Og3AmDBjri91LRVODUKSSDZ/m128s1xk+ERGRalI3Hl59BnbvTL7v2xvhlac1XLRKqEEoUsm6DYC+h/ptDRcVERGpHnXjYfcOWLcw+b4rZgMBhn2k4MWS8qMGoUilO/gUaN8VBk0sdUlERESkpTQsM5VmHuHymb6ecd/RhS2TlCU1CEUq3dEXw8ULoP0+pS6JiIiItJSu/aD7QFidcIH6+j2ejG7YR3xNQ6l4+iuLVLo2tdCpR6lLISIiIi2t7ijvIQwh/j5r58GOTRouWkXUIBQRERERqUR142Db674WcVzLZ4K1gcFTilcuKStqEIqIiIiIVKKBDQvUz42/z/KZPv+wY/filEnKjhqEIiIiIiKVaL+R0K5L/HmEW9d5VlINF60qRW0Qmtk0M3vBzFaY2aVZHm9vZrdEj88xs0HR9nFmNj+6LDCzU6LtdWb2oJk9Z2ZLzOziYpZfRERERKTVqmnji8vH7SFc8Xe/1vqDVaVoDUIzawP8CpgOjATONLORjZ72ReCtEMJQ4ArgR9H2xcDYEMIYYBpwlZnVAruBb4YQRgJHAV/JElNERERERMCHf65fAju2NP3c5TOhy/7Q5+Dil0vKRjF7CMcBK0IIL4YQdgI3Ayc3es7JwPXR7duB48zMQgjbQwi7o+0dgAAQQngthPBMdHsrsBToX8T3ICIiIiLSeg0cD6Ee1j6V/3l7dsHKB324qFnLlE3KQjEbhP2BNRn31/LBxtt7z4kagJuBngBmNt7MlgCLgPMyGohEjw8CDgNSrLYpIiIiIlIF+o8Fq2l62OiaOfDuFg0XrUJlm1QmhDAnhHAwcCRwmZl1aHjMzPYB7gC+FkLI2v9tZuea2Twzm7dhw4aWKbSIiIiISDnp0BX2OxjWNJFYZvlMqGkLg49tmXJJ2Shmg/AVoC7j/oBoW9bnRHMEuwFvZD4hhLAU2AYcEj2vLd4YvCmEcGeuFw8hXB1CGBtCGNu7d+9mvhURERERkVaqbpwvOF+/J/dzls+CA46G9l1arlxSForZIHwKGGZmB5pZO+AMYEaj58wAzo5unwY8EEII0T61AGZ2APAhYJWZGXAtsDSE8NMill1EREREpDIMPAp2boPXl2R/fNMaWP+chotWqaI1CKM5fxcC9+PJX24NISwxsx+Y2UnR064FeprZCuAbQMPSFBOABWY2H7gLuCCEsBE4BvgcMDVjWYoTi/UeRERERERavbpxfr0mR+qNFbP8Wg3CqlRbzOAhhPuA+xpt+17G7R3Ap7LsdwNwQ5btjwFKeyQiIiIiElf3A2Cfvt4gHPflDz6+fJY/p9ewli+blFzZJpUREREREZECMPPlJ7L1EO5+F158yHsHtdxEVVKDUERERESk0tWNh02rYctre29/+XHYtV3DRauYGoQiIiIiIpWu7ii/btxLuHwW1HaAQRNavkxSFtQgFBERERGpdH0P9YbfBxqEM2HQRGjXqTTlkpJTg1BEREREpNLVtoP+R8DqjAXq31gJb6zQcNEqpwahiIiIiEg1qBsH6xbCzu1+f8Xf/XrYR0pXJik5NQhFRERERKpB3VFQvxtefcbvL58JPYdBjwNLWy4pKTUIRURERESqQeYC9Tu3w6rHNFxUirswvYiIiIiIlIlOPaDXcFg9B/ocCrt3wLDjS10qKTE1CEVEREREqkXdOHj+L9BtALTtBAccU+oSSYlpyKiIiIiISLWoOwreeQsW3gKDJ0Nt+1KXSEpMDUIRERERkWoxMFqgfuc2ZRcVQA1CEREREZHq0XModOzht4eqQSiaQygiIiIiUj3MYMgUePNF6F5X6tJIGVCDUERERESkmpz0S1+PUAQ1CEVEREREqku7TqUugZQRzSEUERERERGpUmoQioiIiIiIVCk1CEVERERERKqUGoQiIiIiIiJVSg1CERERERGRKqUGoYiIiIiISJVSg1BERERERKRKqUEoIiIiIiJSpdQgFBERERERqVJqEIqIiIiIiFQpCyGUugxFZ2YbgJdLXY4i6QVsLIMY5VSWcolRTmXR+ylOjHIqi95PcWKUU1n0fsq7LOUSo5zKovdTnBjlVBa9n/IvS6YDQgi9P7A1hKBLK74A88ohRjmVpVxilFNZ9H70mbS2spRLjHIqi95PeZelXGKUU1n0fvSZtLayVNr7iXvRkFEREREREZEqpQahiIiIiIhIlVKDsPW7ukxiFCpOJcUoVJxyiVGoOJUUo1BxyiVGoeJUUoxCxSmXGIWKUy4xChWnkmIUKk65xChUnEqKUag45RKjUHHKJUah4hSqLE2qiqQyIiIiIiIi8kHqIRQREREREalSahC2UmZ2nZmtN7PFzYhRZ2YPmtlzZrbEzC5OEaODmc01swVRjO83ozxtzOxZM/tzM2KsMrNFZjbfzOaljNHdzG43s+fNbKmZfTjh/iOi12+4bDGzr6Uox9ejz3Sxmf3JzDokjRHFuTiKsSRuObLVLzPrYWazzGx5dL1vyjifispSb2ZjU8a4PPr7LDSzu8yse4oY/xHtP9/MZprZ/mnKkvHYN80smFmvFGX5dzN7JaPOnJimHGb21ehzWWJm/5OiHLdklGGVmc3PFyNPnDFm9mTD/6GZjUsRY7SZPRH9P99rZl2biJH1+yxJvc0TI3adzRMjaZ3NFSd2vc0VI+PxJutsnnIkrbM5yxK33uYpS+x6mydG0jqbK07sems5fj/N7EAzm2NmK6L31i5FjAuj/eN8J+WKcZOZvWD++3GdmbVNGefaaNtC89/WfZLGyHj852a2LWU5fm9mL2XUlTEp45iZ/dDMlpkfI1yUIsajGeV41czuThHjODN7JorxmJkNTRFjahRjsZldb2a1+T6TjHh7Ha8lqbN5YsSus3liJKqzOWLErq/54mRsb7LO5ilLojrbLC2VzlSXwl6AScDhwOJmxOgHHB7d7gIsA0YmjGHAPtHttsAc4KiU5fkG8Efgz814T6uAXs38bK8HvhTdbgd0b0asNsA6fN2XJPv1B14COkb3bwXOSfH6hwCLgU5ALfB3YGia+gX8D3BpdPtS4Ecp4xwEjAAeAsamjPFRoDa6/aOmypIjRteM2xcBV6YpS7S9DrgfX+80b/3LUZZ/By5J8HfNFmNK9PdtH93fL817yXj8J8D3UpZlJjA9un0i8FCKGE8Bx0a3vwD8RxMxsn6fJam3eWLErrN5YiSts7nixK63uWIkqbN5ypG0zuaKE7ve5ns/cettnnIkrbO54sSut+T4/cS/78+Itl8JnJ8ixmHAIGL8JuaJcWL0mAF/yleOJuJk1tmfEv0/JokR3R8L3ABsS1mO3wOnJaizueJ8HvgDUBOjzjZ5jATcAfxzinIsAw6Ktl8A/D5hjKOBNcDwaPsPgC/G/Gz2Ol5LUmfzxIhdZ/PESFRnc8SIXV/zxUlSZ/OUJVGdbc5FPYStVAjhEeDNZsZ4LYTwTHR7K7AUb4gkiRFCCA1nPtpGl8QTU81sAPAx4Jqk+xaSmXXDD06vBQgh7AwhbGpGyOOAlSGEl1PsWwt0jM7YdQJeTRHjIGBOCGF7CGE38DDwyaZ2ylG/TsYby0TXn0gTJ4SwNITwQoyy54sxM3o/AE8CA1LE2JJxtzMx6m2e/7srgG83M0ZsOWKcD/x3COHd6Dnr05bDzAz4NP6DmqYsAWjoGelGE3U3R4zhwCPR7VnAqU3EyPV9Frve5oqRpM7miZG0zuaKE7veNvEdH6vOFuJ3ook4settU2WJU2/zxEhaZ3PFiV1v8/x+TgVuj7Y3VWezxgghPBtCWJXvPcSIcV/0WADm0nSdzRVnC7z39+lI/jqbNYaZtQEux+tsqvfT1H4J4pwP/CCEUB89L1+dzVsW8x7kqUDOHsI8MWLX2Rwx9gA7QwjLou1Nfs9GZd7reC36u8aus9liRGWMXWfzxEhUZ3PEiF1f88VJUmdzxWhJahAKAGY2CD87MyfFvm3Mh+isB2aFEBLHAH6G/9PUp9g3UwBmmtnTZnZuiv0PBDYAv4u67a8xs87NKM8ZxDiobiyE8ArwY2A18BqwOYQwM8XrLwYmmllPM+uEnz2rSxEHoE8I4bXo9jqgT8o4hfYF4K9pdoyG/awBPgt8L2WMk4FXQggL0uyf4cJoiMp1FmM4bhbD8b/1HDN72MyObEZZJgKvhxCWp9z/a8Dl0Wf7Y+CyFDGW4I05gE+RoN42+j5LVW+b850YI0aiOts4Tpp6mxkjbZ3N8n5S1dlGcVLV2xyfbaJ62yhG6jrbKE6ietv49xNYCWzKOHmwliYa4IX4Dc4XIxp29zngb2njmNnv8P+/DwG/SBHjQmBGxv9y2vfzw6jOXmFm7VPGGQKcbj60+K9mNixlWcAbTrMbneiJG+NLwH1mthb/+/x3khh4g6nW3h8GfxrxvmcbH6/1JGGdzRIjjZwxEtTZrDGS1Nc8cRLV2VxlIWGdTUsNQiEaH30H8LWmvpSyCSHsCSGMwc/EjDOzQxK+/seB9SGEp5O+dhYTQgiHA9OBr5jZpIT71+JD134TQjgMeBsfZpaY+Rj6k4DbUuy7L35QcSCwP9DZzM5KGieEsBQfnjYT/2Kcj58VbJbo7FviM66FZmbfBXYDN6XZP4Tw3RBCXbT/hSlevxPwHVI2JjP8Bj/IGIOfAPhJihi1QA98KNG3gFujM5xpnEmKExkZzge+Hn22XyfqcU/oC8AFZvY0PiRvZ5yd8n2fxa23zf1OzBcjaZ3NFidpvc2MEb124jqbpRyp6myWOInrbZ6/T+x6myVGqjqbJU6ietv49xM/AE2kub/BMWL8GngkhPBo2jghhM/jv2VLgdMTxpiEN67jHJjnK8dl+Od7JF7n/jVlnPbAjhDCWOC3wHUpYjSIVWdzxPg6cGIIYQDwO3x4Y+wYwMH4SesrzGwusJUmjg8KcbzWQjGarLP5YiSpr9nimM/tjl1n85QlcZ1NLbTAuFRdinPBx1qnnkMYxWiLzyP5RoHK9D0SzCuJ9vkv/IzSKvyMzHbgxgKU5d9TlKUvsCrj/kTgLylf/2RgZsp9PwVcm3H/n4FfF+Az+X/ABWnqF/AC0C+63Q94IU2cjO0PEWMOYa4YwDnAE0Cn5pQjemxg3P+lzDjAofjZ1lXRZTfeq9u3GWWJ9X+d5e/zN2BKxv2VQO8Un2st8DowIEG9alyWzfDeskYGbGnm32c4MDdGjA98nyWtt9liJK2zuWKkqLN5v5/j1NvGMdLU2RjliFtns/19EtXbPJ9t7Hqboxxp6mxTn0usepvx/O/hjeKNvD/f9MPA/QljXJJxfxUJ59VnxgD+DR/OWJMkRrayRNsmkSBPQBTj3/Bjg4Y6Ww+saGY5JicpR2Yc4HngwIy6sjnlZ9sLeAPokKIc38KnozRsGwg818zP5KPArU3sl+147aYkdTZHjBszHm+yzuaLEbfONlWOuPU1R5y3ktTZmGVJXGeTXNRDWMWis7DXAktDCHnPLOWJ0duibHlm1hH4CP5lGVsI4bIQwoAQwiD8bNUDIYTEvWFm1tnMujTcxr/cEmVhDSGsA9aY2Yho03HAc0nLEmlOL8tq4Cgz6xT9nY7Dz1QlZmb7RdcD8fmDf0xZphnA2dHts4F7UsZpNjObhg+tOCmEsD1ljMxhPieTsN4ChBAWhRD2CyEMiurvWjzRxLqEZemXcfcUEtbbyN14gg7MbDieEGljijjHA8+HENam2LfBq8Cx0e2pQOKhpxn1tgb4P3iignzPz/V9FrveFug7MWuMpHU2T5zY9TZbjKR1Nk85EtXZPJ9t7HrbxN8nVr3NEyNRnc3zucSutzl+P5cCD+LD96DpOtvs3+BcMczsS8AJwJkhmi+XIs4LFmW/jD6zk/KVL0eMp0MIfTPq7PYQQr6MmrneT7+McnyCputsrs/2vTqL15ll2SM0+fc5DT/A35GiHEuBbtH/DBnbEpUjo762x3uf8n7P5jhe+ywJ6mwhjvlyxUhSZ7PFAD6XpL7mKcu+SepsnveTqM42S7FamroU94I3NF4DduE/6LEyQzWKMQEfPrUQH0o4Hx9+kCTGKODZKMZiYmQlbCLeZFKeAQEGAwuiyxLguynjjAHmRe/pbmDfFDE642f+ujXjs/g+/kW0GM9S1T5lnEfxRu0C4Li09QufJzAbP1D6O9AjZZxTotvv4mf08579zhFjBZ4draHe5s0QmiPGHdFnuxC4F0/Ykfj9NHp8FU2f2cxWlhuARVFZZhD1aCWM0Q64MXpPzwBT07wXPKvZeQnqV7ayTACejurcHOCIFDEuxg+0luFzY6yJGFm/z5LU2zwxYtfZPDGS1tlccWLX21wxktTZPOVIWmdzxYldb/O9n7j1Nk85ktbZXHFi11ty/H7iv2VzozpzG3m++/PEuCiqs7vxxu41KWLsxntsG95f3t/3bHHwqUmPR3VlMd6b1DVpWRo9p6kso7nezwMZ5biRKOtmijjdgb9EsZ4ARqd5P/iIg2kx6myucpwSlWFBFGtwihiX4w3JF/Bhz3nL0ijmZN7Phhm7zuaJEbvO5omRqM42jpG0vuYrS5I6m+f9JKqzzbk0DI8QERERERGRKqMhoyIiIiIiIlVKDUIREREREZEqpQahiIiIiIhIlVKDUEREREREpEqpQSgiIiIiIlKl1CAUERGJycz2mNn8jMulBYw9yMyKt86UiIhIFrWlLoCIiEgr8k4IYUypCyEiIlIo6iEUERFpJjNbZWb/Y2aLzGyumQ2Ntg8yswfMbKGZzTazgdH2PmZ2l5ktiC5HR6HamNlvzWyJmc00s44le1MiIlIV1CAUERGJr2OjIaOnZzy2OYRwKPBL4GfRtl8A14cQRgE3AT+Ptv8ceDiEMBo4HFgSbR8G/CqEcDCwCTi1yO9HRESqnIUQSl0GERGRVsHMtoUQ9smyfRUwNYTwopm1BdaFEHqa2UagXwhhV7T9tRBCLzPbAAwIIbybEWMQMCuEMCy6/69A2xDCfxb/nYmISLVSD6GIiEhhhBy3k3g34/YeNNdfRESKTA1CERGRwjg94/qJ6PY/gDOi258FHo1uzwbOBzCzNmbWraUKKSIikklnHkVEROLraGbzM+7/LYTQsPTEvma2EO/lOzPa9lXgd2b2LWAD8Plo+8XA1Wb2Rbwn8HzgtaKXXkREpBHNIRQREWmmaA7h2BDCxlKXRUREJAkNGRUREREREalS6iEUERERERGpUuohFBERERERqVJqEIqIiIiIiFQpNQhFRERERESqlBqEIiIiIiIiVUoNQhERERERkSqlBqGIiIiIiEiV+v/QYfmHDJF2twAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(history['classification_output_loss'])\n",
    "plt.plot(history['val_classification_output_loss'])\n",
    "plt.title('Classification Model Loss - Main Training')\n",
    "plt.ylabel('Loss (Categorical Focal Loss)')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='best')\n",
    "plt.xticks(range(1,46))\n",
    "plt.gca().ticklabel_format(axis='both', style='plain', useOffset=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "colab_type": "code",
    "id": "tgYxfJGxNDqi",
    "outputId": "c7aba244-c445-4034-9615-4a9f537c6bfb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(history['ordinal_regression_output_loss'])\n",
    "plt.plot(history['val_ordinal_regression_output_loss'])\n",
    "plt.title('Ordinal Regression Model Loss - Main Training')\n",
    "plt.ylabel('Loss (Binary Focal Loss)')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='best')\n",
    "plt.xticks(range(1,46))\n",
    "plt.gca().ticklabel_format(axis='both', style='plain', useOffset=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pOZFyljJRnMS"
   },
   "source": [
    "### <font color='red'> 15.6 Stage 3 (Post Training) </font>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 104
    },
    "colab_type": "code",
    "id": "TPItySzSb_de",
    "outputId": "ffbae475-c00f-4c3c-ffdb-a68ad41aad5d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:337: UserWarning: This ImageDataGenerator specifies `zca_whitening`, which overrides setting of `featurewise_center`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2929 validated image filenames.\n",
      "2929\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Get the Outputs from the 3 Heads - (Classification,Regression,Ordinal Regression) to pass the outputs to a new Model\n",
    "'''\n",
    "\n",
    "complete_datagen = ImageDataGenerator(rescale=1./255, rotation_range=360,brightness_range=[0.5, 1.5],\n",
    "                                     zoom_range=[1, 1.2],zca_whitening=True,horizontal_flip=True,\n",
    "                                     vertical_flip=True,fill_mode='constant')\n",
    "complete_generator = complete_datagen.flow_from_dataframe(dataframe=df_train_train,\n",
    "                                                          directory = \"./train_images_resized_preprocessed/\",\n",
    "                                                          x_col=\"file_name\",\n",
    "                                                          target_size=(HEIGHT, WIDTH),\n",
    "                                                          batch_size=1,\n",
    "                                                          shuffle=False,\n",
    "                                                          class_mode=None)\n",
    "STEP_SIZE_COMPLETE = complete_generator.n//complete_generator.batch_size\n",
    "print(complete_generator.n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "5S3C2x2Hwo8-",
    "outputId": "6179394a-08aa-4b4f-fcfa-ac1e2b3eb77e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 733 validated image filenames.\n",
      "733\n"
     ]
    }
   ],
   "source": [
    "test_generator = complete_datagen.flow_from_dataframe(dataframe=df_train_test,\n",
    "                                                          directory = \"./test_images_resized_preprocessed/\",\n",
    "                                                          x_col=\"file_name\",\n",
    "                                                          target_size=(HEIGHT, WIDTH),\n",
    "                                                          batch_size=1,\n",
    "                                                          shuffle=False,\n",
    "                                                          class_mode=None)\n",
    "STEP_SIZE_TEST = test_generator.n//test_generator.batch_size\n",
    "print(test_generator.n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 121
    },
    "colab_type": "code",
    "id": "jPydELR1b_gF",
    "outputId": "7addea96-6e1c-46a3-c9ec-c1acd3e890fe"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:720: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n",
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:739: UserWarning: This ImageDataGenerator specifies `zca_whitening`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2929/2929 [==============================] - 1006s 344ms/step\n"
     ]
    }
   ],
   "source": [
    "model = load_model(\"model_main_training.h5\",custom_objects={'categorical_focal_loss_fixed':categorical_focal_loss(alpha=.25, gamma=2),\n",
    "                                                            'binary_focal_loss_fixed' : binary_focal_loss(alpha=.25, gamma=2)})\n",
    "train_preds = model.predict_generator(complete_generator, steps=STEP_SIZE_COMPLETE,verbose = 1)\n",
    "\n",
    "f = open(\"train_preds\",\"wb\")\n",
    "pickle.dump(train_preds,f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 121
    },
    "colab_type": "code",
    "id": "Snsubop3wzXg",
    "outputId": "f30b7332-888c-4f10-808a-4419f32f56ff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/733 [..............................] - ETA: 5:50"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:720: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n",
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:739: UserWarning: This ImageDataGenerator specifies `zca_whitening`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "733/733 [==============================] - 256s 350ms/step\n"
     ]
    }
   ],
   "source": [
    "test_preds = model.predict_generator(test_generator, steps=STEP_SIZE_TEST,verbose = 1)\n",
    "\n",
    "f = open(\"test_preds\",\"wb\")\n",
    "pickle.dump(test_preds,f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "id": "mCPQGqWJqrIX",
    "outputId": "66cc6979-049d-4f49-d614-9897096570bb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2929, 1) (2929, 5) (2929, 5)\n",
      "(2929, 1) (2929, 1) (2929, 1)\n",
      "(2929, 3)\n"
     ]
    }
   ],
   "source": [
    "print(train_preds[0].shape,train_preds[1].shape,train_preds[2].shape)\n",
    "\n",
    "train_output_regression = np.array(train_preds[0]).reshape(-1,1)\n",
    "train_output_classification = np.array(np.argmax(train_preds[1],axis = -1)).reshape(-1,1)\n",
    "train_output_ordinal_regression = np.array(np.sum(train_preds[2],axis = -1)).reshape(-1,1)\n",
    "\n",
    "print(train_output_regression.shape,train_output_classification.shape,train_output_ordinal_regression.shape)\n",
    "X_train = np.hstack((train_output_regression,train_output_classification,train_output_ordinal_regression))\n",
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "id": "MDQmh3iaw68C",
    "outputId": "9c963b7d-26f7-43f1-f5fb-9d311325997e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(733, 1) (733, 5) (733, 5)\n",
      "(733, 1) (733, 1) (733, 1)\n",
      "(733, 3)\n"
     ]
    }
   ],
   "source": [
    "print(test_preds[0].shape,test_preds[1].shape,test_preds[2].shape)\n",
    "\n",
    "test_output_regression = np.array(test_preds[0]).reshape(-1,1)\n",
    "test_output_classification = np.array(np.argmax(test_preds[1],axis = -1)).reshape(-1,1)\n",
    "test_output_ordinal_regression = np.array(np.sum(test_preds[2],axis = -1)).reshape(-1,1)\n",
    "\n",
    "print(test_output_regression.shape,test_output_classification.shape,test_output_ordinal_regression.shape)\n",
    "X_test = np.hstack((test_output_regression,test_output_classification,test_output_ordinal_regression))\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 185
    },
    "colab_type": "code",
    "id": "woVkwW_2Oicp",
    "outputId": "c2271671-2e11-4f2b-e5c4-55ee47957af0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_4 (Dense)              (None, 1)                 4         \n",
      "=================================================================\n",
      "Total params: 4\n",
      "Trainable params: 4\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_post = Sequential()\n",
    "model_post.add(Dense(1, activation='linear', input_shape=(3,)))\n",
    "model_post.compile(optimizer=optimizers.SGD(lr=LEARNING_RATE), loss='mean_squared_error', metrics=['mean_squared_error'])\n",
    "model_post.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "c8Tnmh0FVWKV",
    "outputId": "904b600f-e4a1-4565-da2e-50b0348d9d5b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 2929 samples, validate on 733 samples\n",
      "Epoch 1/50\n",
      "2929/2929 [==============================] - 0s 72us/step - loss: 2.1613 - mean_squared_error: 2.1613 - val_loss: 1.7903 - val_mean_squared_error: 1.7903\n",
      "Epoch 2/50\n",
      "2929/2929 [==============================] - 0s 62us/step - loss: 1.5696 - mean_squared_error: 1.5696 - val_loss: 1.3935 - val_mean_squared_error: 1.3935\n",
      "Epoch 3/50\n",
      "2929/2929 [==============================] - 0s 58us/step - loss: 1.2575 - mean_squared_error: 1.2575 - val_loss: 1.1798 - val_mean_squared_error: 1.1798\n",
      "Epoch 4/50\n",
      "2929/2929 [==============================] - 0s 58us/step - loss: 1.0864 - mean_squared_error: 1.0864 - val_loss: 1.0614 - val_mean_squared_error: 1.0614\n",
      "Epoch 5/50\n",
      "2929/2929 [==============================] - 0s 62us/step - loss: 0.9895 - mean_squared_error: 0.9895 - val_loss: 0.9911 - val_mean_squared_error: 0.9911\n",
      "Epoch 6/50\n",
      "2929/2929 [==============================] - 0s 60us/step - loss: 0.9303 - mean_squared_error: 0.9303 - val_loss: 0.9470 - val_mean_squared_error: 0.9470\n",
      "Epoch 7/50\n",
      "2929/2929 [==============================] - 0s 56us/step - loss: 0.8920 - mean_squared_error: 0.8920 - val_loss: 0.9165 - val_mean_squared_error: 0.9165\n",
      "Epoch 8/50\n",
      "2929/2929 [==============================] - 0s 58us/step - loss: 0.8649 - mean_squared_error: 0.8649 - val_loss: 0.8937 - val_mean_squared_error: 0.8937\n",
      "Epoch 9/50\n",
      "2929/2929 [==============================] - 0s 59us/step - loss: 0.8444 - mean_squared_error: 0.8444 - val_loss: 0.8757 - val_mean_squared_error: 0.8757\n",
      "Epoch 10/50\n",
      "2929/2929 [==============================] - 0s 57us/step - loss: 0.8280 - mean_squared_error: 0.8280 - val_loss: 0.8605 - val_mean_squared_error: 0.8605\n",
      "Epoch 11/50\n",
      "2929/2929 [==============================] - 0s 59us/step - loss: 0.8142 - mean_squared_error: 0.8142 - val_loss: 0.8477 - val_mean_squared_error: 0.8477\n",
      "Epoch 12/50\n",
      "2929/2929 [==============================] - 0s 64us/step - loss: 0.8027 - mean_squared_error: 0.8027 - val_loss: 0.8365 - val_mean_squared_error: 0.8365\n",
      "Epoch 13/50\n",
      "2929/2929 [==============================] - 0s 57us/step - loss: 0.7927 - mean_squared_error: 0.7927 - val_loss: 0.8266 - val_mean_squared_error: 0.8266\n",
      "Epoch 14/50\n",
      "2929/2929 [==============================] - 0s 59us/step - loss: 0.7839 - mean_squared_error: 0.7839 - val_loss: 0.8178 - val_mean_squared_error: 0.8178\n",
      "Epoch 15/50\n",
      "2929/2929 [==============================] - 0s 57us/step - loss: 0.7762 - mean_squared_error: 0.7762 - val_loss: 0.8102 - val_mean_squared_error: 0.8102\n",
      "Epoch 16/50\n",
      "2929/2929 [==============================] - 0s 58us/step - loss: 0.7693 - mean_squared_error: 0.7693 - val_loss: 0.8033 - val_mean_squared_error: 0.8033\n",
      "Epoch 17/50\n",
      "2929/2929 [==============================] - 0s 60us/step - loss: 0.7632 - mean_squared_error: 0.7632 - val_loss: 0.7971 - val_mean_squared_error: 0.7971\n",
      "Epoch 18/50\n",
      "2929/2929 [==============================] - 0s 66us/step - loss: 0.7579 - mean_squared_error: 0.7579 - val_loss: 0.7917 - val_mean_squared_error: 0.7917\n",
      "Epoch 19/50\n",
      "2929/2929 [==============================] - 0s 59us/step - loss: 0.7532 - mean_squared_error: 0.7532 - val_loss: 0.7869 - val_mean_squared_error: 0.7869\n",
      "Epoch 20/50\n",
      "2929/2929 [==============================] - 0s 67us/step - loss: 0.7489 - mean_squared_error: 0.7489 - val_loss: 0.7826 - val_mean_squared_error: 0.7826\n",
      "Epoch 21/50\n",
      "2929/2929 [==============================] - 0s 58us/step - loss: 0.7452 - mean_squared_error: 0.7452 - val_loss: 0.7787 - val_mean_squared_error: 0.7787\n",
      "Epoch 22/50\n",
      "2929/2929 [==============================] - 0s 58us/step - loss: 0.7419 - mean_squared_error: 0.7419 - val_loss: 0.7753 - val_mean_squared_error: 0.7753\n",
      "Epoch 23/50\n",
      "2929/2929 [==============================] - 0s 60us/step - loss: 0.7389 - mean_squared_error: 0.7389 - val_loss: 0.7723 - val_mean_squared_error: 0.7723\n",
      "Epoch 24/50\n",
      "2929/2929 [==============================] - 0s 59us/step - loss: 0.7362 - mean_squared_error: 0.7362 - val_loss: 0.7695 - val_mean_squared_error: 0.7695\n",
      "Epoch 25/50\n",
      "2929/2929 [==============================] - 0s 57us/step - loss: 0.7338 - mean_squared_error: 0.7338 - val_loss: 0.7671 - val_mean_squared_error: 0.7671\n",
      "Epoch 26/50\n",
      "2929/2929 [==============================] - 0s 57us/step - loss: 0.7317 - mean_squared_error: 0.7317 - val_loss: 0.7648 - val_mean_squared_error: 0.7648\n",
      "Epoch 27/50\n",
      "2929/2929 [==============================] - 0s 57us/step - loss: 0.7298 - mean_squared_error: 0.7298 - val_loss: 0.7629 - val_mean_squared_error: 0.7629\n",
      "Epoch 28/50\n",
      "2929/2929 [==============================] - 0s 61us/step - loss: 0.7280 - mean_squared_error: 0.7280 - val_loss: 0.7612 - val_mean_squared_error: 0.7612\n",
      "Epoch 29/50\n",
      "2929/2929 [==============================] - 0s 60us/step - loss: 0.7265 - mean_squared_error: 0.7265 - val_loss: 0.7597 - val_mean_squared_error: 0.7597\n",
      "Epoch 30/50\n",
      "2929/2929 [==============================] - 0s 58us/step - loss: 0.7251 - mean_squared_error: 0.7251 - val_loss: 0.7583 - val_mean_squared_error: 0.7583\n",
      "Epoch 31/50\n",
      "2929/2929 [==============================] - 0s 57us/step - loss: 0.7239 - mean_squared_error: 0.7239 - val_loss: 0.7571 - val_mean_squared_error: 0.7571\n",
      "Epoch 32/50\n",
      "2929/2929 [==============================] - 0s 58us/step - loss: 0.7227 - mean_squared_error: 0.7227 - val_loss: 0.7560 - val_mean_squared_error: 0.7560\n",
      "Epoch 33/50\n",
      "2929/2929 [==============================] - 0s 58us/step - loss: 0.7217 - mean_squared_error: 0.7217 - val_loss: 0.7551 - val_mean_squared_error: 0.7551\n",
      "Epoch 34/50\n",
      "2929/2929 [==============================] - 0s 61us/step - loss: 0.7208 - mean_squared_error: 0.7208 - val_loss: 0.7543 - val_mean_squared_error: 0.7543\n",
      "Epoch 35/50\n",
      "2929/2929 [==============================] - 0s 64us/step - loss: 0.7200 - mean_squared_error: 0.7200 - val_loss: 0.7536 - val_mean_squared_error: 0.7536\n",
      "Epoch 36/50\n",
      "2929/2929 [==============================] - 0s 58us/step - loss: 0.7192 - mean_squared_error: 0.7192 - val_loss: 0.7529 - val_mean_squared_error: 0.7529\n",
      "Epoch 37/50\n",
      "2929/2929 [==============================] - 0s 59us/step - loss: 0.7185 - mean_squared_error: 0.7185 - val_loss: 0.7523 - val_mean_squared_error: 0.7523\n",
      "Epoch 38/50\n",
      "2929/2929 [==============================] - 0s 58us/step - loss: 0.7179 - mean_squared_error: 0.7179 - val_loss: 0.7518 - val_mean_squared_error: 0.7518\n",
      "Epoch 39/50\n",
      "2929/2929 [==============================] - 0s 62us/step - loss: 0.7173 - mean_squared_error: 0.7173 - val_loss: 0.7514 - val_mean_squared_error: 0.7514\n",
      "Epoch 40/50\n",
      "2929/2929 [==============================] - 0s 58us/step - loss: 0.7168 - mean_squared_error: 0.7168 - val_loss: 0.7509 - val_mean_squared_error: 0.7509\n",
      "Epoch 41/50\n",
      "2929/2929 [==============================] - 0s 60us/step - loss: 0.7163 - mean_squared_error: 0.7163 - val_loss: 0.7507 - val_mean_squared_error: 0.7507\n",
      "Epoch 42/50\n",
      "2929/2929 [==============================] - 0s 58us/step - loss: 0.7159 - mean_squared_error: 0.7159 - val_loss: 0.7504 - val_mean_squared_error: 0.7504\n",
      "Epoch 43/50\n",
      "2929/2929 [==============================] - 0s 57us/step - loss: 0.7155 - mean_squared_error: 0.7155 - val_loss: 0.7500 - val_mean_squared_error: 0.7500\n",
      "Epoch 44/50\n",
      "2929/2929 [==============================] - 0s 57us/step - loss: 0.7151 - mean_squared_error: 0.7151 - val_loss: 0.7499 - val_mean_squared_error: 0.7499\n",
      "Epoch 45/50\n",
      "2929/2929 [==============================] - 0s 59us/step - loss: 0.7148 - mean_squared_error: 0.7148 - val_loss: 0.7498 - val_mean_squared_error: 0.7498\n",
      "Epoch 46/50\n",
      "2929/2929 [==============================] - 0s 58us/step - loss: 0.7145 - mean_squared_error: 0.7145 - val_loss: 0.7497 - val_mean_squared_error: 0.7497\n",
      "Epoch 47/50\n",
      "2929/2929 [==============================] - 0s 60us/step - loss: 0.7142 - mean_squared_error: 0.7142 - val_loss: 0.7494 - val_mean_squared_error: 0.7494\n",
      "Epoch 48/50\n",
      "2929/2929 [==============================] - 0s 57us/step - loss: 0.7139 - mean_squared_error: 0.7139 - val_loss: 0.7493 - val_mean_squared_error: 0.7493\n",
      "Epoch 49/50\n",
      "2929/2929 [==============================] - 0s 61us/step - loss: 0.7137 - mean_squared_error: 0.7137 - val_loss: 0.7492 - val_mean_squared_error: 0.7492\n",
      "Epoch 50/50\n",
      "2929/2929 [==============================] - 0s 64us/step - loss: 0.7134 - mean_squared_error: 0.7134 - val_loss: 0.7491 - val_mean_squared_error: 0.7491\n"
     ]
    }
   ],
   "source": [
    "history = model_post.fit(X_train,np.array(df_train_train.diagnosis.values),\n",
    "                         batch_size=BATCH_SIZE,\n",
    "                         epochs=50,\n",
    "                         verbose=1,\n",
    "                         validation_data = (X_test,np.array(df_train_test.diagnosis.values)))\n",
    "\n",
    "model_post.save(\"model_post_training.h5\")\n",
    "f = open(\"history_post_training\",\"wb\")\n",
    "pickle.dump(history,f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 342
    },
    "colab_type": "code",
    "id": "FtrJDNeCVWMo",
    "outputId": "6dd9acb8-458d-44ea-b087-c48915338f21"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,5))\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Linear Regression Model Loss - Post Training (50 epochs)')\n",
    "plt.ylabel('Loss (MSE)')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='best')\n",
    "plt.xticks(range(1,51))\n",
    "plt.gca().ticklabel_format(axis='both', style='plain', useOffset=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UnpWnUyW4nuA"
   },
   "source": [
    "### <font color='red'> 15.7 Evaluate Model performance (Test data)</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Tb0PBdUbVWPJ"
   },
   "outputs": [],
   "source": [
    "# ref- https://github.com/dimitreOliveira/APTOS2019BlindnessDetection/blob/master/Model%20backlog/ResNet50/63%20-%20ResNet50%20-%20Regression%20-%20RGB%20scale.ipynb\n",
    "'''This Function does nearest integer rounding for regression output from the post training model'''\n",
    "\n",
    "def classify(x):\n",
    "    if x < 0.5:\n",
    "        return 0\n",
    "    elif x < 1.5:\n",
    "        return 1\n",
    "    elif x < 2.5:\n",
    "        return 2\n",
    "    elif x < 3.5:\n",
    "        return 3\n",
    "    return 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "qySwX-6dVWRT",
    "outputId": "901fa894-e1b5-4e37-fece-ae2414fdf9e9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2929/2929 [==============================] - 0s 27us/step\n"
     ]
    }
   ],
   "source": [
    "train_labels = model_post.predict(X_train,batch_size=BATCH_SIZE,verbose = 1)\n",
    "train_labels = np.apply_along_axis(classify, 1, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "JGVHhia6VWT2",
    "outputId": "41d1be1b-33b0-4346-a520-442ec3653e83"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "733/733 [==============================] - 0s 32us/step\n"
     ]
    }
   ],
   "source": [
    "test_labels = model_post.predict(X_test,batch_size=BATCH_SIZE,verbose = 1)\n",
    "test_labels = np.apply_along_axis(classify, 1, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iiQE5CcB4LSF"
   },
   "outputs": [],
   "source": [
    "# Plot Confusion Matrix..\n",
    "\n",
    "def plot_conf_matrix(true,pred,classes):\n",
    "    cf = confusion_matrix(true, pred)\n",
    "    \n",
    "    df_cm = pd.DataFrame(cf, range(len(classes)), range(len(classes)))\n",
    "    plt.figure(figsize=(8,5.5))\n",
    "    sns.set(font_scale=1.4)\n",
    "    sns.heatmap(df_cm, annot=True, annot_kws={\"size\": 16},xticklabels = classes ,yticklabels = classes,fmt='g')\n",
    "    #sns.heatmap(df_cm, annot=True, annot_kws={\"size\": 16})\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 485
    },
    "colab_type": "code",
    "id": "G-BTE9Cn4LVy",
    "outputId": "503e6077-479a-4840-fb31-ff4fa55f8da4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['0 - No DR', '1 - Mild', '2 - Moderate', '3 - Severe', '4 - Proliferative DR']\n",
    "plot_conf_matrix(list(df_train_test['diagnosis'].astype(int)),test_labels,labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 436
    },
    "colab_type": "code",
    "id": "d70ssg2u4sZ2",
    "outputId": "636ea908-970c-43d8-e4fd-51bc0ff1c934"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnf_matrix = confusion_matrix(df_train_test['diagnosis'].astype('int'), test_labels)\n",
    "cnf_matrix_norm = cnf_matrix.astype('float') / cnf_matrix.sum(axis=1)[:, np.newaxis]\n",
    "df_cm = pd.DataFrame(cnf_matrix_norm, index=labels, columns=labels)\n",
    "plt.figure(figsize=(16, 7))\n",
    "sns.heatmap(df_cm, annot=True, fmt='.2f', cmap=\"Blues\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "l7c3uhJK432J",
    "outputId": "8919bd78-879f-41e5-db7c-5e292fcf503d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Cohen Kappa score: 0.715\n",
      "Train Accuracy score : 0.576\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Cohen Kappa score: %.3f\" % cohen_kappa_score(train_labels, df_train_train['diagnosis'].astype('int'), weights='quadratic'))\n",
    "print(\"Train Accuracy score : %.3f\" % accuracy_score(df_train_train['diagnosis'].astype('int'),train_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "KjckvWfv4sc-",
    "outputId": "bbab7e29-0a52-4938-c2d1-d65ec1fd915a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Cohen Kappa score: 0.704\n",
      "Test Accuracy score : 0.576\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Cohen Kappa score: %.3f\" % cohen_kappa_score(test_labels, df_train_test['diagnosis'].astype('int'), weights='quadratic'))\n",
    "print(\"Test Accuracy score : %.3f\" % accuracy_score(df_train_test['diagnosis'].astype('int'),test_labels))"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "research_paper_implementation.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}