ResNet50.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-13 11:43:07.436361: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib/cuda/include:/usr/lib/x86_64-linux-gnu/:/usr/lib/cuda/lib64:/usr/lib/x86_64-linux-gnu/:\n",
      "2023-01-13 11:43:07.436414: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2023-01-13 11:43:10.683714: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib/cuda/include:/usr/lib/x86_64-linux-gnu/:/usr/lib/cuda/lib64:/usr/lib/x86_64-linux-gnu/:\n",
      "2023-01-13 11:43:10.683922: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublas.so.11'; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib/cuda/include:/usr/lib/x86_64-linux-gnu/:/usr/lib/cuda/lib64:/usr/lib/x86_64-linux-gnu/:\n",
      "2023-01-13 11:43:10.684048: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublasLt.so.11'; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib/cuda/include:/usr/lib/x86_64-linux-gnu/:/usr/lib/cuda/lib64:/usr/lib/x86_64-linux-gnu/:\n",
      "2023-01-13 11:43:10.684174: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcufft.so.10'; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib/cuda/include:/usr/lib/x86_64-linux-gnu/:/usr/lib/cuda/lib64:/usr/lib/x86_64-linux-gnu/:\n",
      "2023-01-13 11:43:10.684300: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcurand.so.10'; dlerror: libcurand.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib/cuda/include:/usr/lib/x86_64-linux-gnu/:/usr/lib/cuda/lib64:/usr/lib/x86_64-linux-gnu/:\n",
      "2023-01-13 11:43:10.684403: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusolver.so.11'; dlerror: libcusolver.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib/cuda/include:/usr/lib/x86_64-linux-gnu/:/usr/lib/cuda/lib64:/usr/lib/x86_64-linux-gnu/:\n",
      "2023-01-13 11:43:10.684552: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib/cuda/include:/usr/lib/x86_64-linux-gnu/:/usr/lib/cuda/lib64:/usr/lib/x86_64-linux-gnu/:\n",
      "2023-01-13 11:43:10.684788: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n",
      "2023-01-13 11:43:10.685157: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "94773248/94765736 [==============================] - 1s 0us/step\n",
      "94781440/94765736 [==============================] - 1s 0us/step\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
      "                                                                 \n",
      " resnet50 (Functional)       (None, 1, 1, 2048)        23587712  \n",
      "                                                                 \n",
      " global_average_pooling2d (G  (None, 2048)             0         \n",
      " lobalAveragePooling2D)                                          \n",
      "                                                                 \n",
      " dense (Dense)               (None, 10)                20490     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 23,608,202\n",
      "Trainable params: 20,490\n",
      "Non-trainable params: 23,587,712\n",
      "_________________________________________________________________\n",
      "None\n",
      "Found 17000 images belonging to 10 classes.\n",
      "Found 3000 images belonging to 10 classes.\n",
      "Epoch 1/30\n",
      "531/531 [==============================] - 46s 78ms/step - loss: 1.8841 - accuracy: 0.4074 - val_loss: 1.5373 - val_accuracy: 0.6100 - lr: 1.0000e-04\n",
      "Epoch 2/30\n",
      "531/531 [==============================] - 40s 76ms/step - loss: 1.3548 - accuracy: 0.6492 - val_loss: 1.1967 - val_accuracy: 0.7053 - lr: 1.0000e-04\n",
      "Epoch 3/30\n",
      "531/531 [==============================] - 38s 72ms/step - loss: 1.0999 - accuracy: 0.7205 - val_loss: 0.9973 - val_accuracy: 0.7607 - lr: 1.0000e-04\n",
      "Epoch 4/30\n",
      "531/531 [==============================] - 38s 71ms/step - loss: 0.9412 - accuracy: 0.7612 - val_loss: 0.8662 - val_accuracy: 0.7910 - lr: 1.0000e-04\n",
      "Epoch 5/30\n",
      "531/531 [==============================] - 41s 77ms/step - loss: 0.8308 - accuracy: 0.7888 - val_loss: 0.7726 - val_accuracy: 0.8170 - lr: 1.0000e-04\n",
      "Epoch 6/30\n",
      "531/531 [==============================] - 48s 91ms/step - loss: 0.7648 - accuracy: 0.8055 - val_loss: 0.7320 - val_accuracy: 0.8277 - lr: 5.0000e-05\n",
      "Epoch 7/30\n",
      "531/531 [==============================] - 47s 88ms/step - loss: 0.7291 - accuracy: 0.8132 - val_loss: 0.7024 - val_accuracy: 0.8290 - lr: 5.0000e-05\n",
      "Epoch 8/30\n",
      "531/531 [==============================] - 47s 89ms/step - loss: 0.6969 - accuracy: 0.8216 - val_loss: 0.6702 - val_accuracy: 0.8387 - lr: 5.0000e-05\n",
      "Epoch 9/30\n",
      "531/531 [==============================] - 53s 100ms/step - loss: 0.6694 - accuracy: 0.8269 - val_loss: 0.6443 - val_accuracy: 0.8413 - lr: 5.0000e-05\n",
      "Epoch 10/30\n",
      "531/531 [==============================] - 61s 114ms/step - loss: 0.6437 - accuracy: 0.8310 - val_loss: 0.6195 - val_accuracy: 0.8457 - lr: 5.0000e-05\n",
      "Epoch 11/30\n",
      "531/531 [==============================] - 59s 111ms/step - loss: 0.6202 - accuracy: 0.8378 - val_loss: 0.5988 - val_accuracy: 0.8537 - lr: 5.0000e-05\n",
      "Epoch 12/30\n",
      "531/531 [==============================] - 56s 105ms/step - loss: 0.6005 - accuracy: 0.8420 - val_loss: 0.5819 - val_accuracy: 0.8550 - lr: 5.0000e-05\n",
      "Epoch 13/30\n",
      "531/531 [==============================] - 61s 116ms/step - loss: 0.5809 - accuracy: 0.8475 - val_loss: 0.5622 - val_accuracy: 0.8570 - lr: 5.0000e-05\n",
      "Epoch 14/30\n",
      "531/531 [==============================] - 51s 96ms/step - loss: 0.5639 - accuracy: 0.8508 - val_loss: 0.5477 - val_accuracy: 0.8613 - lr: 5.0000e-05\n",
      "Epoch 15/30\n",
      "531/531 [==============================] - 58s 110ms/step - loss: 0.5481 - accuracy: 0.8546 - val_loss: 0.5316 - val_accuracy: 0.8627 - lr: 5.0000e-05\n",
      "Epoch 16/30\n",
      "531/531 [==============================] - 57s 108ms/step - loss: 0.5334 - accuracy: 0.8580 - val_loss: 0.5193 - val_accuracy: 0.8690 - lr: 5.0000e-05\n",
      "Epoch 17/30\n",
      "531/531 [==============================] - 57s 107ms/step - loss: 0.5194 - accuracy: 0.8616 - val_loss: 0.5041 - val_accuracy: 0.8707 - lr: 5.0000e-05\n",
      "Epoch 18/30\n",
      "531/531 [==============================] - 57s 108ms/step - loss: 0.5066 - accuracy: 0.8647 - val_loss: 0.4940 - val_accuracy: 0.8747 - lr: 5.0000e-05\n",
      "Epoch 19/30\n",
      "531/531 [==============================] - 58s 109ms/step - loss: 0.4952 - accuracy: 0.8670 - val_loss: 0.4821 - val_accuracy: 0.8770 - lr: 5.0000e-05\n",
      "Epoch 20/30\n",
      "531/531 [==============================] - 59s 111ms/step - loss: 0.4838 - accuracy: 0.8702 - val_loss: 0.4706 - val_accuracy: 0.8797 - lr: 5.0000e-05\n",
      "Epoch 21/30\n",
      "531/531 [==============================] - 55s 103ms/step - loss: 0.4734 - accuracy: 0.8731 - val_loss: 0.4612 - val_accuracy: 0.8813 - lr: 5.0000e-05\n",
      "Epoch 22/30\n",
      "531/531 [==============================] - 48s 91ms/step - loss: 0.4633 - accuracy: 0.8754 - val_loss: 0.4532 - val_accuracy: 0.8833 - lr: 5.0000e-05\n",
      "Epoch 23/30\n",
      "531/531 [==============================] - 61s 115ms/step - loss: 0.4545 - accuracy: 0.8775 - val_loss: 0.4442 - val_accuracy: 0.8830 - lr: 5.0000e-05\n",
      "Epoch 24/30\n",
      "531/531 [==============================] - 60s 112ms/step - loss: 0.4455 - accuracy: 0.8800 - val_loss: 0.4358 - val_accuracy: 0.8847 - lr: 5.0000e-05\n",
      "Epoch 25/30\n",
      "531/531 [==============================] - 65s 123ms/step - loss: 0.4367 - accuracy: 0.8828 - val_loss: 0.4274 - val_accuracy: 0.8883 - lr: 5.0000e-05\n",
      "Epoch 26/30\n",
      "531/531 [==============================] - 72s 136ms/step - loss: 0.4293 - accuracy: 0.8853 - val_loss: 0.4204 - val_accuracy: 0.8880 - lr: 5.0000e-05\n",
      "Epoch 27/30\n",
      "531/531 [==============================] - 76s 143ms/step - loss: 0.4217 - accuracy: 0.8874 - val_loss: 0.4136 - val_accuracy: 0.8893 - lr: 5.0000e-05\n",
      "Epoch 28/30\n",
      "531/531 [==============================] - 72s 136ms/step - loss: 0.4142 - accuracy: 0.8887 - val_loss: 0.4070 - val_accuracy: 0.8907 - lr: 5.0000e-05\n",
      "Epoch 29/30\n",
      "531/531 [==============================] - 66s 125ms/step - loss: 0.4075 - accuracy: 0.8907 - val_loss: 0.4007 - val_accuracy: 0.8930 - lr: 5.0000e-05\n",
      "Epoch 30/30\n",
      "531/531 [==============================] - 59s 111ms/step - loss: 0.4015 - accuracy: 0.8930 - val_loss: 0.3963 - val_accuracy: 0.8920 - lr: 5.0000e-05\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
      "                                                                 \n",
      " resnet50 (Functional)       (None, 1, 1, 2048)        23587712  \n",
      "                                                                 \n",
      " global_average_pooling2d (G  (None, 2048)             0         \n",
      " lobalAveragePooling2D)                                          \n",
      "                                                                 \n",
      " dense (Dense)               (None, 10)                20490     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 23,608,202\n",
      "Trainable params: 23,555,082\n",
      "Non-trainable params: 53,120\n",
      "_________________________________________________________________\n",
      "None\n",
      "Epoch 1/20\n",
      "531/531 [==============================] - 1241s 2s/step - loss: 0.3321 - accuracy: 0.9003 - val_loss: 0.2904 - val_accuracy: 0.9110 - lr: 1.0000e-07\n",
      "Epoch 2/20\n",
      "531/531 [==============================] - 1240s 2s/step - loss: 0.2769 - accuracy: 0.9143 - val_loss: 0.2585 - val_accuracy: 0.9190 - lr: 1.0000e-07\n",
      "Epoch 3/20\n",
      "531/531 [==============================] - 1112s 2s/step - loss: 0.2435 - accuracy: 0.9252 - val_loss: 0.2287 - val_accuracy: 0.9287 - lr: 1.0000e-07\n",
      "Epoch 4/20\n",
      "531/531 [==============================] - 1088s 2s/step - loss: 0.2200 - accuracy: 0.9347 - val_loss: 0.2070 - val_accuracy: 0.9367 - lr: 1.0000e-07\n",
      "Epoch 5/20\n",
      "531/531 [==============================] - 1215s 2s/step - loss: 0.2015 - accuracy: 0.9401 - val_loss: 0.1884 - val_accuracy: 0.9443 - lr: 1.0000e-07\n",
      "Epoch 6/20\n",
      "531/531 [==============================] - 1344s 3s/step - loss: 0.3442 - accuracy: 0.8910 - val_loss: 0.2560 - val_accuracy: 0.9067 - lr: 5.0000e-06\n",
      "Epoch 7/20\n",
      "531/531 [==============================] - 1472s 3s/step - loss: 0.1399 - accuracy: 0.9531 - val_loss: 0.0808 - val_accuracy: 0.9750 - lr: 5.0000e-06\n",
      "Epoch 8/20\n",
      "531/531 [==============================] - 1457s 3s/step - loss: 0.0970 - accuracy: 0.9688 - val_loss: 0.0671 - val_accuracy: 0.9790 - lr: 5.0000e-06\n",
      "Epoch 9/20\n",
      "531/531 [==============================] - 1502s 3s/step - loss: 0.0698 - accuracy: 0.9787 - val_loss: 0.0443 - val_accuracy: 0.9867 - lr: 5.0000e-06\n",
      "Epoch 10/20\n",
      "531/531 [==============================] - 1379s 3s/step - loss: 0.0553 - accuracy: 0.9827 - val_loss: 0.0561 - val_accuracy: 0.9840 - lr: 5.0000e-06\n",
      "Epoch 11/20\n",
      "531/531 [==============================] - 1300s 2s/step - loss: 0.0484 - accuracy: 0.9842 - val_loss: 0.0453 - val_accuracy: 0.9853 - lr: 5.0000e-06\n",
      "Epoch 12/20\n",
      "531/531 [==============================] - 1342s 3s/step - loss: 0.0425 - accuracy: 0.9880 - val_loss: 0.0365 - val_accuracy: 0.9887 - lr: 5.0000e-06\n",
      "Epoch 13/20\n",
      "531/531 [==============================] - 1366s 3s/step - loss: 0.0363 - accuracy: 0.9891 - val_loss: 0.0493 - val_accuracy: 0.9860 - lr: 5.0000e-06\n",
      "Epoch 14/20\n",
      "531/531 [==============================] - 1353s 3s/step - loss: 0.0289 - accuracy: 0.9907 - val_loss: 0.0393 - val_accuracy: 0.9873 - lr: 5.0000e-06\n",
      "Epoch 15/20\n",
      "531/531 [==============================] - 1271s 2s/step - loss: 0.0255 - accuracy: 0.9916 - val_loss: 0.0411 - val_accuracy: 0.9903 - lr: 5.0000e-06\n",
      "Epoch 16/20\n",
      "531/531 [==============================] - 1298s 2s/step - loss: 0.0044 - accuracy: 0.9987 - val_loss: 0.0296 - val_accuracy: 0.9950 - lr: 1.0000e-06\n",
      "Epoch 17/20\n",
      "531/531 [==============================] - 1372s 3s/step - loss: 0.0025 - accuracy: 0.9994 - val_loss: 0.0394 - val_accuracy: 0.9937 - lr: 1.0000e-06\n",
      "Epoch 18/20\n",
      "531/531 [==============================] - 1418s 3s/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.0453 - val_accuracy: 0.9923 - lr: 1.0000e-06\n",
      "Epoch 19/20\n",
      "531/531 [==============================] - 1329s 3s/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.0317 - val_accuracy: 0.9937 - lr: 1.0000e-06\n",
      "Epoch 20/20\n",
      "531/531 [==============================] - 1226s 2s/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 0.0427 - val_accuracy: 0.9937 - lr: 1.0000e-06\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 14.581597222222216, 'Predicted Class')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##############################################################################\n",
    "import logging\n",
    "logging.getLogger(\"tensorflow\").disabled = True\n",
    "import numpy as np\n",
    "import math\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score\n",
    "\n",
    "import tensorflow\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dropout, Flatten, Dense\n",
    "from tensorflow.keras.layers import BatchNormalization as BatchNorm\n",
    "\n",
    "# pre-trained models\n",
    "from tensorflow.keras.applications.vgg16 import VGG16\n",
    "from tensorflow.keras.applications import ResNet50, InceptionV3, Xception, DenseNet121\n",
    "\n",
    "# custom learning rates\n",
    "import learning_rate as lr\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "##############################################################################\n",
    "# please change following for your dataset (dataset details and learning params)\n",
    "# dataset details\n",
    "train_data_dir = '/home/engs2426/Downloads/HCR-Net-main/Data/Offline/UCI_DevanagariHandwrittenCharacterDataset/digits/Train'\n",
    "validation_data_dir = '/home/engs2426/Downloads/HCR-Net-main/Data/Offline/UCI_DevanagariHandwrittenCharacterDataset/digits/Test'\n",
    "nb_train_samples = 17000\n",
    "nb_validation_samples = 3000\n",
    "num_classes = 10\n",
    "\n",
    "# learning params\n",
    "epochs1 = 30\n",
    "epochs2 = 20\n",
    "learning_rate1 = lr.decayed_learning_rate\n",
    "learning_rate2 = lr.decayed_learning_rate_tuned20\n",
    "\n",
    "# set seeds for reproduceability\n",
    "np.random.seed(1)\n",
    "tensorflow.random.set_seed(1)\n",
    "##############################################################################\n",
    "batch_size = 32\n",
    "# dimensions of our images.\n",
    "img_width, img_height = 32, 32\n",
    "input_shape = (img_width, img_height, 3)\n",
    "##############################################################################\n",
    "#### TRAINING PHASE - I #########\n",
    "#################################\n",
    "base_model = ResNet50(include_top=False, input_shape=input_shape)\n",
    "base_model.trainable = False\n",
    "inputs = tensorflow.keras.Input(shape=input_shape)\n",
    "x = base_model(inputs, training=False)\n",
    "x = tensorflow.keras.layers.GlobalAveragePooling2D()(x)\n",
    "outputs = tensorflow.keras.layers.Dense(num_classes, activation='softmax')(x)\n",
    "model = tensorflow.keras.Model(inputs, outputs)\n",
    "model.compile(loss='categorical_crossentropy', optimizer=tensorflow.keras.optimizers.RMSprop(), metrics=['accuracy'])\n",
    "print(model.summary())\n",
    "\n",
    "# using ImageDataGenerator to load and scale data\n",
    "train_datagen = ImageDataGenerator(rescale=1.0/255)\n",
    "val_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "                    train_data_dir,\n",
    "                    target_size=(img_width, img_height),\n",
    "                    batch_size=batch_size,\n",
    "                    color_mode=\"rgb\",\n",
    "                    class_mode='categorical',\n",
    "                    shuffle=True,\n",
    "                    seed=13)\n",
    "validation_generator = val_datagen.flow_from_directory(\n",
    "                    validation_data_dir,\n",
    "                    target_size=(img_width, img_height),\n",
    "                    batch_size=nb_validation_samples,\n",
    "                    color_mode=\"rgb\",\n",
    "                    shuffle=False,\n",
    "                    class_mode='categorical')\n",
    "\n",
    "# define callback for custom learning rate\n",
    "callback = tensorflow.keras.callbacks.LearningRateScheduler(learning_rate1)\n",
    "\n",
    "history1 = model.fit(\n",
    "                train_generator,\n",
    "                steps_per_epoch=nb_train_samples // batch_size,\n",
    "                epochs=epochs1,\n",
    "                callbacks=[callback],\n",
    "                validation_data=validation_generator,\n",
    "                validation_steps=1)\n",
    "\n",
    "# save the model\n",
    "# model.save_weights('./results/HCR-Net-I.h5')\n",
    "\n",
    "#################################\n",
    "#### TRAINING PHASE - II #########\n",
    "#################################\n",
    "# fine tuning the model by unfreezing the lower layers (used from VGG16)\n",
    "base_model.trainable = True\n",
    "\n",
    "# reset the image generators\n",
    "train_generator.reset()\n",
    "validation_generator.reset()\n",
    "\n",
    "# recompile the model\n",
    "model.compile(loss='categorical_crossentropy', optimizer=tensorflow.keras.optimizers.RMSprop(), metrics=['accuracy'])\n",
    "print(model.summary())\n",
    "\n",
    "# define callback for custom learning rate\n",
    "callback = tensorflow.keras.callbacks.LearningRateScheduler(learning_rate2)\n",
    "\n",
    "history2 = model.fit(\n",
    "                train_generator,\n",
    "                steps_per_epoch=nb_train_samples // batch_size,\n",
    "                epochs=epochs2,\n",
    "                callbacks=[callback],\n",
    "                validation_data=validation_generator,\n",
    "                validation_steps=1)\n",
    "\n",
    "# save the model\n",
    "model.save_weights('./results/HCR-Net-II.h5')\n",
    "\n",
    "##############################################################################\n",
    "# plot the convergence, in terms of accuracy and loss\n",
    "plt.plot(history1.history['accuracy']+history2.history['accuracy'])\n",
    "plt.plot(history1.history['val_accuracy']+history2.history['val_accuracy'])\n",
    "# plt.title('model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(['Train', 'Test'], loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history1.history['loss']+history2.history['loss'])\n",
    "plt.plot(history1.history['val_loss']+history2.history['val_loss'])\n",
    "# plt.title('model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(['Train', 'Test'], loc='upper right')\n",
    "plt.show()\n",
    "\n",
    "# evaluate model and plot confusion matrix\n",
    "# filenames = validation_generator.filenames\n",
    "predict = model.predict(validation_generator)\n",
    "y_pred = np.argmax(predict,axis=-1)\n",
    "cf_matrix = confusion_matrix(validation_generator.classes,y_pred)\n",
    "\n",
    "# calculate size of confusion matrix figure, and plot it\n",
    "fig, ax = plt.subplots(figsize=(math.ceil(num_classes/3.0), math.ceil(num_classes/3.0)))\n",
    "labels = list(validation_generator.class_indices.keys())\n",
    "sns.heatmap(cf_matrix, annot=True, xticklabels=labels, yticklabels=labels, fmt='', cmap='Blues')\n",
    "plt.ylabel('Actual Class')\n",
    "plt.xlabel('Predicted Class')\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision_score:  0.9937361521417991\n",
      "recall_score   :  0.9936666666666667\n",
      "f1_score       :  0.9936752510211161\n",
      "accuracy_score :  0.9936666666666667\n",
      "\n",
      "Best accuracy  :  0.9950000047683716\n"
     ]
    }
   ],
   "source": [
    "# calculate different metrics for HCR-Net on the test dataset\n",
    "print('precision_score: ', precision_score(validation_generator.classes,y_pred, average=\"macro\"))\n",
    "print('recall_score   : ', recall_score(validation_generator.classes,y_pred, average=\"macro\"))\n",
    "print('f1_score       : ', f1_score(validation_generator.classes,y_pred, average=\"macro\"))\n",
    "print('accuracy_score : ', accuracy_score(validation_generator.classes,y_pred, normalize=True))\n",
    "\n",
    "print(\"\\nBest accuracy  : \", max(max(history1.history['val_accuracy']), max(history2.history['val_accuracy'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_4 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
      "                                                                 \n",
      " resnet50 (Functional)       (None, 1, 1, 2048)        23587712  \n",
      "                                                                 \n",
      " global_average_pooling2d_1   (None, 2048)             0         \n",
      " (GlobalAveragePooling2D)                                        \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                20490     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 23,608,202\n",
      "Trainable params: 20,490\n",
      "Non-trainable params: 23,587,712\n",
      "_________________________________________________________________\n",
      "None\n",
      "Found 17000 images belonging to 10 classes.\n",
      "Found 3000 images belonging to 10 classes.\n",
      "Epoch 1/30\n",
      "531/531 [==============================] - 50s 83ms/step - loss: 1.9086 - accuracy: 0.4252 - val_loss: 1.5300 - val_accuracy: 0.5940 - lr: 1.0000e-04\n",
      "Epoch 2/30\n",
      "531/531 [==============================] - 58s 109ms/step - loss: 1.3563 - accuracy: 0.6554 - val_loss: 1.1900 - val_accuracy: 0.7070 - lr: 1.0000e-04\n",
      "Epoch 3/30\n",
      "531/531 [==============================] - 69s 130ms/step - loss: 1.0991 - accuracy: 0.7198 - val_loss: 0.9901 - val_accuracy: 0.7617 - lr: 1.0000e-04\n",
      "Epoch 4/30\n",
      "531/531 [==============================] - 70s 132ms/step - loss: 0.9401 - accuracy: 0.7642 - val_loss: 0.8642 - val_accuracy: 0.7897 - lr: 1.0000e-04\n",
      "Epoch 5/30\n",
      "531/531 [==============================] - 70s 131ms/step - loss: 0.8305 - accuracy: 0.7907 - val_loss: 0.7709 - val_accuracy: 0.8153 - lr: 1.0000e-04\n",
      "Epoch 6/30\n",
      "531/531 [==============================] - 70s 132ms/step - loss: 0.7640 - accuracy: 0.8067 - val_loss: 0.7301 - val_accuracy: 0.8260 - lr: 5.0000e-05\n",
      "Epoch 7/30\n",
      "531/531 [==============================] - 67s 125ms/step - loss: 0.7285 - accuracy: 0.8151 - val_loss: 0.6972 - val_accuracy: 0.8337 - lr: 5.0000e-05\n",
      "Epoch 8/30\n",
      "531/531 [==============================] - 70s 132ms/step - loss: 0.6967 - accuracy: 0.8205 - val_loss: 0.6693 - val_accuracy: 0.8403 - lr: 5.0000e-05\n",
      "Epoch 9/30\n",
      "531/531 [==============================] - 63s 119ms/step - loss: 0.6685 - accuracy: 0.8289 - val_loss: 0.6419 - val_accuracy: 0.8440 - lr: 5.0000e-05\n",
      "Epoch 10/30\n",
      "531/531 [==============================] - 70s 131ms/step - loss: 0.6429 - accuracy: 0.8349 - val_loss: 0.6178 - val_accuracy: 0.8480 - lr: 5.0000e-05\n",
      "Epoch 11/30\n",
      "531/531 [==============================] - 67s 127ms/step - loss: 0.6207 - accuracy: 0.8409 - val_loss: 0.5985 - val_accuracy: 0.8510 - lr: 5.0000e-05\n",
      "Epoch 12/30\n",
      "531/531 [==============================] - 64s 120ms/step - loss: 0.5996 - accuracy: 0.8451 - val_loss: 0.5767 - val_accuracy: 0.8553 - lr: 5.0000e-05\n",
      "Epoch 13/30\n",
      "531/531 [==============================] - 70s 131ms/step - loss: 0.5802 - accuracy: 0.8502 - val_loss: 0.5593 - val_accuracy: 0.8587 - lr: 5.0000e-05\n",
      "Epoch 14/30\n",
      "531/531 [==============================] - 62s 117ms/step - loss: 0.5634 - accuracy: 0.8537 - val_loss: 0.5428 - val_accuracy: 0.8617 - lr: 5.0000e-05\n",
      "Epoch 15/30\n",
      "531/531 [==============================] - 63s 119ms/step - loss: 0.5474 - accuracy: 0.8564 - val_loss: 0.5287 - val_accuracy: 0.8673 - lr: 5.0000e-05\n",
      "Epoch 16/30\n",
      "531/531 [==============================] - 61s 114ms/step - loss: 0.5325 - accuracy: 0.8612 - val_loss: 0.5149 - val_accuracy: 0.8680 - lr: 5.0000e-05\n",
      "Epoch 17/30\n",
      "531/531 [==============================] - 70s 132ms/step - loss: 0.5192 - accuracy: 0.8637 - val_loss: 0.5020 - val_accuracy: 0.8723 - lr: 5.0000e-05\n",
      "Epoch 18/30\n",
      "531/531 [==============================] - 69s 129ms/step - loss: 0.5061 - accuracy: 0.8662 - val_loss: 0.4908 - val_accuracy: 0.8763 - lr: 5.0000e-05\n",
      "Epoch 19/30\n",
      "531/531 [==============================] - 73s 138ms/step - loss: 0.4939 - accuracy: 0.8696 - val_loss: 0.4824 - val_accuracy: 0.8740 - lr: 5.0000e-05\n",
      "Epoch 20/30\n",
      "531/531 [==============================] - 60s 113ms/step - loss: 0.4830 - accuracy: 0.8728 - val_loss: 0.4683 - val_accuracy: 0.8767 - lr: 5.0000e-05\n",
      "Epoch 21/30\n",
      "531/531 [==============================] - 75s 141ms/step - loss: 0.4724 - accuracy: 0.8748 - val_loss: 0.4592 - val_accuracy: 0.8797 - lr: 5.0000e-05\n",
      "Epoch 22/30\n",
      "531/531 [==============================] - 74s 140ms/step - loss: 0.4625 - accuracy: 0.8775 - val_loss: 0.4497 - val_accuracy: 0.8813 - lr: 5.0000e-05\n",
      "Epoch 23/30\n",
      "531/531 [==============================] - 72s 135ms/step - loss: 0.4534 - accuracy: 0.8798 - val_loss: 0.4413 - val_accuracy: 0.8820 - lr: 5.0000e-05\n",
      "Epoch 24/30\n",
      "531/531 [==============================] - 68s 128ms/step - loss: 0.4444 - accuracy: 0.8817 - val_loss: 0.4361 - val_accuracy: 0.8867 - lr: 5.0000e-05\n",
      "Epoch 25/30\n",
      "531/531 [==============================] - 76s 143ms/step - loss: 0.4365 - accuracy: 0.8845 - val_loss: 0.4264 - val_accuracy: 0.8867 - lr: 5.0000e-05\n",
      "Epoch 26/30\n",
      "531/531 [==============================] - 74s 139ms/step - loss: 0.4284 - accuracy: 0.8860 - val_loss: 0.4192 - val_accuracy: 0.8900 - lr: 5.0000e-05\n",
      "Epoch 27/30\n",
      "531/531 [==============================] - 62s 116ms/step - loss: 0.4207 - accuracy: 0.8878 - val_loss: 0.4117 - val_accuracy: 0.8907 - lr: 5.0000e-05\n",
      "Epoch 28/30\n",
      "531/531 [==============================] - 67s 126ms/step - loss: 0.4139 - accuracy: 0.8912 - val_loss: 0.4053 - val_accuracy: 0.8930 - lr: 5.0000e-05\n",
      "Epoch 29/30\n",
      "531/531 [==============================] - 75s 140ms/step - loss: 0.4073 - accuracy: 0.8930 - val_loss: 0.3989 - val_accuracy: 0.8927 - lr: 5.0000e-05\n",
      "Epoch 30/30\n",
      "531/531 [==============================] - 70s 131ms/step - loss: 0.4008 - accuracy: 0.8941 - val_loss: 0.3932 - val_accuracy: 0.8947 - lr: 5.0000e-05\n",
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_4 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
      "                                                                 \n",
      " resnet50 (Functional)       (None, 1, 1, 2048)        23587712  \n",
      "                                                                 \n",
      " global_average_pooling2d_1   (None, 2048)             0         \n",
      " (GlobalAveragePooling2D)                                        \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                20490     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 23,608,202\n",
      "Trainable params: 23,555,082\n",
      "Non-trainable params: 53,120\n",
      "_________________________________________________________________\n",
      "None\n",
      "Epoch 1/20\n",
      "531/531 [==============================] - 1441s 3s/step - loss: 0.3319 - accuracy: 0.9009 - val_loss: 0.2925 - val_accuracy: 0.9093 - lr: 1.0000e-07\n",
      "Epoch 2/20\n",
      "531/531 [==============================] - 1353s 3s/step - loss: 0.2773 - accuracy: 0.9148 - val_loss: 0.2587 - val_accuracy: 0.9187 - lr: 1.0000e-07\n",
      "Epoch 3/20\n",
      "531/531 [==============================] - 1181s 2s/step - loss: 0.2450 - accuracy: 0.9259 - val_loss: 0.2265 - val_accuracy: 0.9293 - lr: 1.0000e-07\n",
      "Epoch 4/20\n",
      "531/531 [==============================] - 1076s 2s/step - loss: 0.2209 - accuracy: 0.9325 - val_loss: 0.2126 - val_accuracy: 0.9373 - lr: 1.0000e-07\n",
      "Epoch 5/20\n",
      "531/531 [==============================] - 1076s 2s/step - loss: 0.2010 - accuracy: 0.9405 - val_loss: 0.1953 - val_accuracy: 0.9430 - lr: 1.0000e-07\n",
      "Epoch 6/20\n",
      "531/531 [==============================] - 1082s 2s/step - loss: 0.3652 - accuracy: 0.8865 - val_loss: 0.1087 - val_accuracy: 0.9633 - lr: 5.0000e-06\n",
      "Epoch 7/20\n",
      "531/531 [==============================] - 1122s 2s/step - loss: 0.1352 - accuracy: 0.9537 - val_loss: 0.0666 - val_accuracy: 0.9803 - lr: 5.0000e-06\n",
      "Epoch 8/20\n",
      "531/531 [==============================] - 1128s 2s/step - loss: 0.0904 - accuracy: 0.9707 - val_loss: 0.1441 - val_accuracy: 0.9573 - lr: 5.0000e-06\n",
      "Epoch 9/20\n",
      "531/531 [==============================] - 1110s 2s/step - loss: 0.0708 - accuracy: 0.9770 - val_loss: 0.0530 - val_accuracy: 0.9817 - lr: 5.0000e-06\n",
      "Epoch 10/20\n",
      "531/531 [==============================] - 1111s 2s/step - loss: 0.0583 - accuracy: 0.9811 - val_loss: 0.0607 - val_accuracy: 0.9817 - lr: 5.0000e-06\n",
      "Epoch 11/20\n",
      "531/531 [==============================] - 1079s 2s/step - loss: 0.0471 - accuracy: 0.9862 - val_loss: 0.0743 - val_accuracy: 0.9763 - lr: 5.0000e-06\n",
      "Epoch 12/20\n",
      "531/531 [==============================] - 1028s 2s/step - loss: 0.0390 - accuracy: 0.9882 - val_loss: 0.0615 - val_accuracy: 0.9830 - lr: 5.0000e-06\n",
      "Epoch 13/20\n",
      "531/531 [==============================] - 1053s 2s/step - loss: 0.0343 - accuracy: 0.9907 - val_loss: 0.0372 - val_accuracy: 0.9903 - lr: 5.0000e-06\n",
      "Epoch 14/20\n",
      "531/531 [==============================] - 1053s 2s/step - loss: 0.0305 - accuracy: 0.9910 - val_loss: 0.0424 - val_accuracy: 0.9883 - lr: 5.0000e-06\n",
      "Epoch 15/20\n",
      "531/531 [==============================] - 1093s 2s/step - loss: 0.0259 - accuracy: 0.9919 - val_loss: 0.0603 - val_accuracy: 0.9833 - lr: 5.0000e-06\n",
      "Epoch 16/20\n",
      "531/531 [==============================] - 1079s 2s/step - loss: 0.0044 - accuracy: 0.9984 - val_loss: 0.0179 - val_accuracy: 0.9963 - lr: 1.0000e-06\n",
      "Epoch 17/20\n",
      "531/531 [==============================] - 1105s 2s/step - loss: 0.0032 - accuracy: 0.9992 - val_loss: 0.0233 - val_accuracy: 0.9943 - lr: 1.0000e-06\n",
      "Epoch 18/20\n",
      "531/531 [==============================] - 1091s 2s/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 0.0263 - val_accuracy: 0.9943 - lr: 1.0000e-06\n",
      "Epoch 19/20\n",
      "531/531 [==============================] - 1099s 2s/step - loss: 0.0018 - accuracy: 0.9994 - val_loss: 0.0262 - val_accuracy: 0.9937 - lr: 1.0000e-06\n",
      "Epoch 20/20\n",
      "531/531 [==============================] - 1119s 2s/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.0228 - val_accuracy: 0.9963 - lr: 1.0000e-06\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 14.581597222222216, 'Predicted Class')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##############################################################################\n",
    "import logging\n",
    "logging.getLogger(\"tensorflow\").disabled = True\n",
    "import numpy as np\n",
    "import math\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score\n",
    "\n",
    "import tensorflow\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dropout, Flatten, Dense\n",
    "from tensorflow.keras.layers import BatchNormalization as BatchNorm\n",
    "\n",
    "# pre-trained models\n",
    "from tensorflow.keras.applications.vgg16 import VGG16\n",
    "from tensorflow.keras.applications import ResNet50, InceptionV3, Xception, DenseNet121\n",
    "\n",
    "# custom learning rates\n",
    "import learning_rate as lr\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "##############################################################################\n",
    "# please change following for your dataset (dataset details and learning params)\n",
    "# dataset details\n",
    "train_data_dir = '/home/engs2426/Downloads/HCR-Net-main/Data/Offline/UCI_DevanagariHandwrittenCharacterDataset/digits/Train'\n",
    "validation_data_dir = '/home/engs2426/Downloads/HCR-Net-main/Data/Offline/UCI_DevanagariHandwrittenCharacterDataset/digits/Test'\n",
    "nb_train_samples = 17000\n",
    "nb_validation_samples = 3000\n",
    "num_classes = 10\n",
    "\n",
    "# learning params\n",
    "epochs1 = 30\n",
    "epochs2 = 20\n",
    "learning_rate1 = lr.decayed_learning_rate\n",
    "learning_rate2 = lr.decayed_learning_rate_tuned20\n",
    "\n",
    "# set seeds for reproduceability\n",
    "np.random.seed(2)\n",
    "tensorflow.random.set_seed(2)\n",
    "##############################################################################\n",
    "batch_size = 32\n",
    "# dimensions of our images.\n",
    "img_width, img_height = 32, 32\n",
    "input_shape = (img_width, img_height, 3)\n",
    "##############################################################################\n",
    "#### TRAINING PHASE - I #########\n",
    "#################################\n",
    "base_model = ResNet50(include_top=False, input_shape=input_shape)\n",
    "base_model.trainable = False\n",
    "inputs = tensorflow.keras.Input(shape=input_shape)\n",
    "x = base_model(inputs, training=False)\n",
    "x = tensorflow.keras.layers.GlobalAveragePooling2D()(x)\n",
    "outputs = tensorflow.keras.layers.Dense(num_classes, activation='softmax')(x)\n",
    "model = tensorflow.keras.Model(inputs, outputs)\n",
    "model.compile(loss='categorical_crossentropy', optimizer=tensorflow.keras.optimizers.RMSprop(), metrics=['accuracy'])\n",
    "print(model.summary())\n",
    "\n",
    "# using ImageDataGenerator to load and scale data\n",
    "train_datagen = ImageDataGenerator(rescale=1.0/255)\n",
    "val_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "                    train_data_dir,\n",
    "                    target_size=(img_width, img_height),\n",
    "                    batch_size=batch_size,\n",
    "                    color_mode=\"rgb\",\n",
    "                    class_mode='categorical',\n",
    "                    shuffle=True,\n",
    "                    seed=13)\n",
    "validation_generator = val_datagen.flow_from_directory(\n",
    "                    validation_data_dir,\n",
    "                    target_size=(img_width, img_height),\n",
    "                    batch_size=nb_validation_samples,\n",
    "                    color_mode=\"rgb\",\n",
    "                    shuffle=False,\n",
    "                    class_mode='categorical')\n",
    "\n",
    "# define callback for custom learning rate\n",
    "callback = tensorflow.keras.callbacks.LearningRateScheduler(learning_rate1)\n",
    "\n",
    "history1 = model.fit(\n",
    "                train_generator,\n",
    "                steps_per_epoch=nb_train_samples // batch_size,\n",
    "                epochs=epochs1,\n",
    "                callbacks=[callback],\n",
    "                validation_data=validation_generator,\n",
    "                validation_steps=1)\n",
    "\n",
    "# save the model\n",
    "# model.save_weights('./results/HCR-Net-I.h5')\n",
    "\n",
    "#################################\n",
    "#### TRAINING PHASE - II #########\n",
    "#################################\n",
    "# fine tuning the model by unfreezing the lower layers (used from VGG16)\n",
    "base_model.trainable = True\n",
    "\n",
    "# reset the image generators\n",
    "train_generator.reset()\n",
    "validation_generator.reset()\n",
    "\n",
    "# recompile the model\n",
    "model.compile(loss='categorical_crossentropy', optimizer=tensorflow.keras.optimizers.RMSprop(), metrics=['accuracy'])\n",
    "print(model.summary())\n",
    "\n",
    "# define callback for custom learning rate\n",
    "callback = tensorflow.keras.callbacks.LearningRateScheduler(learning_rate2)\n",
    "\n",
    "history2 = model.fit(\n",
    "                train_generator,\n",
    "                steps_per_epoch=nb_train_samples // batch_size,\n",
    "                epochs=epochs2,\n",
    "                callbacks=[callback],\n",
    "                validation_data=validation_generator,\n",
    "                validation_steps=1)\n",
    "\n",
    "# save the model\n",
    "model.save_weights('./results/HCR-Net-II.h5')\n",
    "\n",
    "##############################################################################\n",
    "# plot the convergence, in terms of accuracy and loss\n",
    "plt.plot(history1.history['accuracy']+history2.history['accuracy'])\n",
    "plt.plot(history1.history['val_accuracy']+history2.history['val_accuracy'])\n",
    "# plt.title('model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(['Train', 'Test'], loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history1.history['loss']+history2.history['loss'])\n",
    "plt.plot(history1.history['val_loss']+history2.history['val_loss'])\n",
    "# plt.title('model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(['Train', 'Test'], loc='upper right')\n",
    "plt.show()\n",
    "\n",
    "# evaluate model and plot confusion matrix\n",
    "# filenames = validation_generator.filenames\n",
    "predict = model.predict(validation_generator)\n",
    "y_pred = np.argmax(predict,axis=-1)\n",
    "cf_matrix = confusion_matrix(validation_generator.classes,y_pred)\n",
    "\n",
    "# calculate size of confusion matrix figure, and plot it\n",
    "fig, ax = plt.subplots(figsize=(math.ceil(num_classes/3.0), math.ceil(num_classes/3.0)))\n",
    "labels = list(validation_generator.class_indices.keys())\n",
    "sns.heatmap(cf_matrix, annot=True, xticklabels=labels, yticklabels=labels, fmt='', cmap='Blues')\n",
    "plt.ylabel('Actual Class')\n",
    "plt.xlabel('Predicted Class')\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision_score:  0.99633664818498\n",
      "recall_score   :  0.9963333333333333\n",
      "f1_score       :  0.9963338842607896\n",
      "accuracy_score :  0.9963333333333333\n",
      "\n",
      "Best accuracy  :  0.9963333606719971\n"
     ]
    }
   ],
   "source": [
    "# calculate different metrics for HCR-Net on the test dataset\n",
    "print('precision_score: ', precision_score(validation_generator.classes,y_pred, average=\"macro\"))\n",
    "print('recall_score   : ', recall_score(validation_generator.classes,y_pred, average=\"macro\"))\n",
    "print('f1_score       : ', f1_score(validation_generator.classes,y_pred, average=\"macro\"))\n",
    "print('accuracy_score : ', accuracy_score(validation_generator.classes,y_pred, normalize=True))\n",
    "\n",
    "print(\"\\nBest accuracy  : \", max(max(history1.history['val_accuracy']), max(history2.history['val_accuracy'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_6 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
      "                                                                 \n",
      " resnet50 (Functional)       (None, 1, 1, 2048)        23587712  \n",
      "                                                                 \n",
      " global_average_pooling2d_2   (None, 2048)             0         \n",
      " (GlobalAveragePooling2D)                                        \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 10)                20490     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 23,608,202\n",
      "Trainable params: 20,490\n",
      "Non-trainable params: 23,587,712\n",
      "_________________________________________________________________\n",
      "None\n",
      "Found 17000 images belonging to 10 classes.\n",
      "Found 3000 images belonging to 10 classes.\n",
      "Epoch 1/30\n",
      "531/531 [==============================] - 70s 120ms/step - loss: 1.9279 - accuracy: 0.4105 - val_loss: 1.5265 - val_accuracy: 0.6127 - lr: 1.0000e-04\n",
      "Epoch 2/30\n",
      "531/531 [==============================] - 46s 86ms/step - loss: 1.3532 - accuracy: 0.6450 - val_loss: 1.1885 - val_accuracy: 0.7000 - lr: 1.0000e-04\n",
      "Epoch 3/30\n",
      "531/531 [==============================] - 60s 112ms/step - loss: 1.1037 - accuracy: 0.7090 - val_loss: 0.9973 - val_accuracy: 0.7483 - lr: 1.0000e-04\n",
      "Epoch 4/30\n",
      "531/531 [==============================] - 60s 114ms/step - loss: 0.9461 - accuracy: 0.7518 - val_loss: 0.8687 - val_accuracy: 0.7863 - lr: 1.0000e-04\n",
      "Epoch 5/30\n",
      "531/531 [==============================] - 61s 114ms/step - loss: 0.8365 - accuracy: 0.7827 - val_loss: 0.7758 - val_accuracy: 0.8063 - lr: 1.0000e-04\n",
      "Epoch 6/30\n",
      "531/531 [==============================] - 61s 115ms/step - loss: 0.7708 - accuracy: 0.7989 - val_loss: 0.7359 - val_accuracy: 0.8230 - lr: 5.0000e-05\n",
      "Epoch 7/30\n",
      "531/531 [==============================] - 60s 113ms/step - loss: 0.7349 - accuracy: 0.8083 - val_loss: 0.7019 - val_accuracy: 0.8293 - lr: 5.0000e-05\n",
      "Epoch 8/30\n",
      "531/531 [==============================] - 60s 113ms/step - loss: 0.7035 - accuracy: 0.8153 - val_loss: 0.6752 - val_accuracy: 0.8337 - lr: 5.0000e-05\n",
      "Epoch 9/30\n",
      "531/531 [==============================] - 60s 114ms/step - loss: 0.6747 - accuracy: 0.8231 - val_loss: 0.6474 - val_accuracy: 0.8360 - lr: 5.0000e-05\n",
      "Epoch 10/30\n",
      "531/531 [==============================] - 60s 113ms/step - loss: 0.6494 - accuracy: 0.8300 - val_loss: 0.6240 - val_accuracy: 0.8413 - lr: 5.0000e-05\n",
      "Epoch 11/30\n",
      "531/531 [==============================] - 60s 112ms/step - loss: 0.6256 - accuracy: 0.8359 - val_loss: 0.6030 - val_accuracy: 0.8467 - lr: 5.0000e-05\n",
      "Epoch 12/30\n",
      "531/531 [==============================] - 60s 113ms/step - loss: 0.6050 - accuracy: 0.8406 - val_loss: 0.5815 - val_accuracy: 0.8507 - lr: 5.0000e-05\n",
      "Epoch 13/30\n",
      "531/531 [==============================] - 60s 113ms/step - loss: 0.5861 - accuracy: 0.8446 - val_loss: 0.5649 - val_accuracy: 0.8537 - lr: 5.0000e-05\n",
      "Epoch 14/30\n",
      "531/531 [==============================] - 48s 91ms/step - loss: 0.5685 - accuracy: 0.8490 - val_loss: 0.5477 - val_accuracy: 0.8583 - lr: 5.0000e-05\n",
      "Epoch 15/30\n",
      "531/531 [==============================] - 58s 109ms/step - loss: 0.5521 - accuracy: 0.8537 - val_loss: 0.5328 - val_accuracy: 0.8590 - lr: 5.0000e-05\n",
      "Epoch 16/30\n",
      "531/531 [==============================] - 57s 106ms/step - loss: 0.5372 - accuracy: 0.8563 - val_loss: 0.5187 - val_accuracy: 0.8647 - lr: 5.0000e-05\n",
      "Epoch 17/30\n",
      "531/531 [==============================] - 59s 111ms/step - loss: 0.5232 - accuracy: 0.8604 - val_loss: 0.5059 - val_accuracy: 0.8683 - lr: 5.0000e-05\n",
      "Epoch 18/30\n",
      "531/531 [==============================] - 59s 112ms/step - loss: 0.5103 - accuracy: 0.8633 - val_loss: 0.4957 - val_accuracy: 0.8710 - lr: 5.0000e-05\n",
      "Epoch 19/30\n",
      "531/531 [==============================] - 48s 91ms/step - loss: 0.4984 - accuracy: 0.8676 - val_loss: 0.4845 - val_accuracy: 0.8733 - lr: 5.0000e-05\n",
      "Epoch 20/30\n",
      "531/531 [==============================] - 53s 101ms/step - loss: 0.4863 - accuracy: 0.8698 - val_loss: 0.4728 - val_accuracy: 0.8783 - lr: 5.0000e-05\n",
      "Epoch 21/30\n",
      "531/531 [==============================] - 59s 111ms/step - loss: 0.4759 - accuracy: 0.8732 - val_loss: 0.4650 - val_accuracy: 0.8793 - lr: 5.0000e-05\n",
      "Epoch 22/30\n",
      "531/531 [==============================] - 59s 112ms/step - loss: 0.4662 - accuracy: 0.8747 - val_loss: 0.4531 - val_accuracy: 0.8833 - lr: 5.0000e-05\n",
      "Epoch 23/30\n",
      "531/531 [==============================] - 64s 120ms/step - loss: 0.4571 - accuracy: 0.8781 - val_loss: 0.4461 - val_accuracy: 0.8817 - lr: 5.0000e-05\n",
      "Epoch 24/30\n",
      "531/531 [==============================] - 59s 111ms/step - loss: 0.4478 - accuracy: 0.8794 - val_loss: 0.4380 - val_accuracy: 0.8827 - lr: 5.0000e-05\n",
      "Epoch 25/30\n",
      "531/531 [==============================] - 61s 114ms/step - loss: 0.4394 - accuracy: 0.8817 - val_loss: 0.4299 - val_accuracy: 0.8860 - lr: 5.0000e-05\n",
      "Epoch 26/30\n",
      "531/531 [==============================] - 60s 112ms/step - loss: 0.4317 - accuracy: 0.8851 - val_loss: 0.4221 - val_accuracy: 0.8897 - lr: 5.0000e-05\n",
      "Epoch 27/30\n",
      "531/531 [==============================] - 60s 112ms/step - loss: 0.4243 - accuracy: 0.8866 - val_loss: 0.4157 - val_accuracy: 0.8913 - lr: 5.0000e-05\n",
      "Epoch 28/30\n",
      "531/531 [==============================] - 60s 113ms/step - loss: 0.4167 - accuracy: 0.8887 - val_loss: 0.4088 - val_accuracy: 0.8917 - lr: 5.0000e-05\n",
      "Epoch 29/30\n",
      "531/531 [==============================] - 60s 113ms/step - loss: 0.4100 - accuracy: 0.8907 - val_loss: 0.4025 - val_accuracy: 0.8913 - lr: 5.0000e-05\n",
      "Epoch 30/30\n",
      "531/531 [==============================] - 59s 112ms/step - loss: 0.4030 - accuracy: 0.8921 - val_loss: 0.3967 - val_accuracy: 0.8927 - lr: 5.0000e-05\n",
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_6 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
      "                                                                 \n",
      " resnet50 (Functional)       (None, 1, 1, 2048)        23587712  \n",
      "                                                                 \n",
      " global_average_pooling2d_2   (None, 2048)             0         \n",
      " (GlobalAveragePooling2D)                                        \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 10)                20490     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 23,608,202\n",
      "Trainable params: 23,555,082\n",
      "Non-trainable params: 53,120\n",
      "_________________________________________________________________\n",
      "None\n",
      "Epoch 1/20\n",
      "531/531 [==============================] - 1115s 2s/step - loss: 0.3341 - accuracy: 0.8994 - val_loss: 0.2914 - val_accuracy: 0.9127 - lr: 1.0000e-07\n",
      "Epoch 2/20\n",
      "531/531 [==============================] - 1113s 2s/step - loss: 0.2787 - accuracy: 0.9144 - val_loss: 0.2536 - val_accuracy: 0.9223 - lr: 1.0000e-07\n",
      "Epoch 3/20\n",
      "531/531 [==============================] - 1073s 2s/step - loss: 0.2457 - accuracy: 0.9257 - val_loss: 0.2267 - val_accuracy: 0.9303 - lr: 1.0000e-07\n",
      "Epoch 4/20\n",
      "531/531 [==============================] - 1011s 2s/step - loss: 0.2208 - accuracy: 0.9346 - val_loss: 0.2039 - val_accuracy: 0.9373 - lr: 1.0000e-07\n",
      "Epoch 5/20\n",
      "531/531 [==============================] - 1026s 2s/step - loss: 0.2017 - accuracy: 0.9389 - val_loss: 0.1872 - val_accuracy: 0.9463 - lr: 1.0000e-07\n",
      "Epoch 6/20\n",
      "531/531 [==============================] - 1031s 2s/step - loss: 0.3549 - accuracy: 0.8899 - val_loss: 0.1123 - val_accuracy: 0.9720 - lr: 5.0000e-06\n",
      "Epoch 7/20\n",
      "531/531 [==============================] - 1103s 2s/step - loss: 0.1420 - accuracy: 0.9532 - val_loss: 0.1278 - val_accuracy: 0.9537 - lr: 5.0000e-06\n",
      "Epoch 8/20\n",
      "531/531 [==============================] - 1054s 2s/step - loss: 0.0964 - accuracy: 0.9694 - val_loss: 0.0729 - val_accuracy: 0.9783 - lr: 5.0000e-06\n",
      "Epoch 9/20\n",
      "531/531 [==============================] - 1112s 2s/step - loss: 0.0720 - accuracy: 0.9778 - val_loss: 0.0646 - val_accuracy: 0.9817 - lr: 5.0000e-06\n",
      "Epoch 10/20\n",
      "531/531 [==============================] - 1111s 2s/step - loss: 0.0586 - accuracy: 0.9827 - val_loss: 0.0438 - val_accuracy: 0.9850 - lr: 5.0000e-06\n",
      "Epoch 11/20\n",
      "531/531 [==============================] - 1125s 2s/step - loss: 0.0488 - accuracy: 0.9850 - val_loss: 0.0356 - val_accuracy: 0.9890 - lr: 5.0000e-06\n",
      "Epoch 12/20\n",
      "531/531 [==============================] - 1117s 2s/step - loss: 0.0377 - accuracy: 0.9876 - val_loss: 0.0470 - val_accuracy: 0.9860 - lr: 5.0000e-06\n",
      "Epoch 13/20\n",
      "531/531 [==============================] - 1115s 2s/step - loss: 0.0333 - accuracy: 0.9890 - val_loss: 0.0244 - val_accuracy: 0.9933 - lr: 5.0000e-06\n",
      "Epoch 14/20\n",
      "531/531 [==============================] - 1121s 2s/step - loss: 0.0292 - accuracy: 0.9912 - val_loss: 0.0245 - val_accuracy: 0.9920 - lr: 5.0000e-06\n",
      "Epoch 15/20\n",
      "531/531 [==============================] - 1111s 2s/step - loss: 0.0278 - accuracy: 0.9923 - val_loss: 0.0247 - val_accuracy: 0.9923 - lr: 5.0000e-06\n",
      "Epoch 16/20\n",
      "531/531 [==============================] - 1119s 2s/step - loss: 0.0045 - accuracy: 0.9984 - val_loss: 0.0252 - val_accuracy: 0.9940 - lr: 1.0000e-06\n",
      "Epoch 17/20\n",
      "531/531 [==============================] - 1001s 2s/step - loss: 0.0021 - accuracy: 0.9994 - val_loss: 0.0256 - val_accuracy: 0.9937 - lr: 1.0000e-06\n",
      "Epoch 18/20\n",
      "531/531 [==============================] - 865s 2s/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.0368 - val_accuracy: 0.9927 - lr: 1.0000e-06\n",
      "Epoch 19/20\n",
      "531/531 [==============================] - 767s 1s/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.0284 - val_accuracy: 0.9937 - lr: 1.0000e-06\n",
      "Epoch 20/20\n",
      "531/531 [==============================] - 758s 1s/step - loss: 8.6180e-04 - accuracy: 0.9998 - val_loss: 0.0272 - val_accuracy: 0.9940 - lr: 1.0000e-06\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 14.581597222222216, 'Predicted Class')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##############################################################################\n",
    "import logging\n",
    "logging.getLogger(\"tensorflow\").disabled = True\n",
    "import numpy as np\n",
    "import math\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score\n",
    "\n",
    "import tensorflow\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dropout, Flatten, Dense\n",
    "from tensorflow.keras.layers import BatchNormalization as BatchNorm\n",
    "\n",
    "# pre-trained models\n",
    "from tensorflow.keras.applications.vgg16 import VGG16\n",
    "from tensorflow.keras.applications import ResNet50, InceptionV3, Xception, DenseNet121\n",
    "\n",
    "# custom learning rates\n",
    "import learning_rate as lr\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "##############################################################################\n",
    "# please change following for your dataset (dataset details and learning params)\n",
    "# dataset details\n",
    "train_data_dir = '/home/engs2426/Downloads/HCR-Net-main/Data/Offline/UCI_DevanagariHandwrittenCharacterDataset/digits/Train'\n",
    "validation_data_dir = '/home/engs2426/Downloads/HCR-Net-main/Data/Offline/UCI_DevanagariHandwrittenCharacterDataset/digits/Test'\n",
    "nb_train_samples = 17000\n",
    "nb_validation_samples = 3000\n",
    "num_classes = 10\n",
    "\n",
    "# learning params\n",
    "epochs1 = 30\n",
    "epochs2 = 20\n",
    "learning_rate1 = lr.decayed_learning_rate\n",
    "learning_rate2 = lr.decayed_learning_rate_tuned20\n",
    "\n",
    "# set seeds for reproduceability\n",
    "np.random.seed(3)\n",
    "tensorflow.random.set_seed(3)\n",
    "##############################################################################\n",
    "batch_size = 32\n",
    "# dimensions of our images.\n",
    "img_width, img_height = 32, 32\n",
    "input_shape = (img_width, img_height, 3)\n",
    "##############################################################################\n",
    "#### TRAINING PHASE - I #########\n",
    "#################################\n",
    "base_model = ResNet50(include_top=False, input_shape=input_shape)\n",
    "base_model.trainable = False\n",
    "inputs = tensorflow.keras.Input(shape=input_shape)\n",
    "x = base_model(inputs, training=False)\n",
    "x = tensorflow.keras.layers.GlobalAveragePooling2D()(x)\n",
    "outputs = tensorflow.keras.layers.Dense(num_classes, activation='softmax')(x)\n",
    "model = tensorflow.keras.Model(inputs, outputs)\n",
    "model.compile(loss='categorical_crossentropy', optimizer=tensorflow.keras.optimizers.RMSprop(), metrics=['accuracy'])\n",
    "print(model.summary())\n",
    "\n",
    "# using ImageDataGenerator to load and scale data\n",
    "train_datagen = ImageDataGenerator(rescale=1.0/255)\n",
    "val_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "                    train_data_dir,\n",
    "                    target_size=(img_width, img_height),\n",
    "                    batch_size=batch_size,\n",
    "                    color_mode=\"rgb\",\n",
    "                    class_mode='categorical',\n",
    "                    shuffle=True,\n",
    "                    seed=13)\n",
    "validation_generator = val_datagen.flow_from_directory(\n",
    "                    validation_data_dir,\n",
    "                    target_size=(img_width, img_height),\n",
    "                    batch_size=nb_validation_samples,\n",
    "                    color_mode=\"rgb\",\n",
    "                    shuffle=False,\n",
    "                    class_mode='categorical')\n",
    "\n",
    "# define callback for custom learning rate\n",
    "callback = tensorflow.keras.callbacks.LearningRateScheduler(learning_rate1)\n",
    "\n",
    "history1 = model.fit(\n",
    "                train_generator,\n",
    "                steps_per_epoch=nb_train_samples // batch_size,\n",
    "                epochs=epochs1,\n",
    "                callbacks=[callback],\n",
    "                validation_data=validation_generator,\n",
    "                validation_steps=1)\n",
    "\n",
    "# save the model\n",
    "# model.save_weights('./results/HCR-Net-I.h5')\n",
    "\n",
    "#################################\n",
    "#### TRAINING PHASE - II #########\n",
    "#################################\n",
    "# fine tuning the model by unfreezing the lower layers (used from VGG16)\n",
    "base_model.trainable = True\n",
    "\n",
    "# reset the image generators\n",
    "train_generator.reset()\n",
    "validation_generator.reset()\n",
    "\n",
    "# recompile the model\n",
    "model.compile(loss='categorical_crossentropy', optimizer=tensorflow.keras.optimizers.RMSprop(), metrics=['accuracy'])\n",
    "print(model.summary())\n",
    "\n",
    "# define callback for custom learning rate\n",
    "callback = tensorflow.keras.callbacks.LearningRateScheduler(learning_rate2)\n",
    "\n",
    "history2 = model.fit(\n",
    "                train_generator,\n",
    "                steps_per_epoch=nb_train_samples // batch_size,\n",
    "                epochs=epochs2,\n",
    "                callbacks=[callback],\n",
    "                validation_data=validation_generator,\n",
    "                validation_steps=1)\n",
    "\n",
    "# save the model\n",
    "model.save_weights('./results/HCR-Net-II.h5')\n",
    "\n",
    "##############################################################################\n",
    "# plot the convergence, in terms of accuracy and loss\n",
    "plt.plot(history1.history['accuracy']+history2.history['accuracy'])\n",
    "plt.plot(history1.history['val_accuracy']+history2.history['val_accuracy'])\n",
    "# plt.title('model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(['Train', 'Test'], loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history1.history['loss']+history2.history['loss'])\n",
    "plt.plot(history1.history['val_loss']+history2.history['val_loss'])\n",
    "# plt.title('model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(['Train', 'Test'], loc='upper right')\n",
    "plt.show()\n",
    "\n",
    "# evaluate model and plot confusion matrix\n",
    "# filenames = validation_generator.filenames\n",
    "predict = model.predict(validation_generator)\n",
    "y_pred = np.argmax(predict,axis=-1)\n",
    "cf_matrix = confusion_matrix(validation_generator.classes,y_pred)\n",
    "\n",
    "# calculate size of confusion matrix figure, and plot it\n",
    "fig, ax = plt.subplots(figsize=(math.ceil(num_classes/3.0), math.ceil(num_classes/3.0)))\n",
    "labels = list(validation_generator.class_indices.keys())\n",
    "sns.heatmap(cf_matrix, annot=True, xticklabels=labels, yticklabels=labels, fmt='', cmap='Blues')\n",
    "plt.ylabel('Actual Class')\n",
    "plt.xlabel('Predicted Class')\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision_score:  0.9940098484733711\n",
      "recall_score   :  0.9940000000000001\n",
      "f1_score       :  0.9939994008773464\n",
      "accuracy_score :  0.994\n",
      "\n",
      "Best accuracy  :  0.9940000176429749\n"
     ]
    }
   ],
   "source": [
    "# calculate different metrics for HCR-Net on the test dataset\n",
    "print('precision_score: ', precision_score(validation_generator.classes,y_pred, average=\"macro\"))\n",
    "print('recall_score   : ', recall_score(validation_generator.classes,y_pred, average=\"macro\"))\n",
    "print('f1_score       : ', f1_score(validation_generator.classes,y_pred, average=\"macro\"))\n",
    "print('accuracy_score : ', accuracy_score(validation_generator.classes,y_pred, normalize=True))\n",
    "\n",
    "print(\"\\nBest accuracy  : \", max(max(history1.history['val_accuracy']), max(history2.history['val_accuracy'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_8 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
      "                                                                 \n",
      " resnet50 (Functional)       (None, 1, 1, 2048)        23587712  \n",
      "                                                                 \n",
      " global_average_pooling2d_3   (None, 2048)             0         \n",
      " (GlobalAveragePooling2D)                                        \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 10)                20490     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 23,608,202\n",
      "Trainable params: 20,490\n",
      "Non-trainable params: 23,587,712\n",
      "_________________________________________________________________\n",
      "None\n",
      "Found 17000 images belonging to 10 classes.\n",
      "Found 3000 images belonging to 10 classes.\n",
      "Epoch 1/30\n",
      "531/531 [==============================] - 42s 72ms/step - loss: 1.9089 - accuracy: 0.3999 - val_loss: 1.5528 - val_accuracy: 0.5990 - lr: 1.0000e-04\n",
      "Epoch 2/30\n",
      "531/531 [==============================] - 34s 65ms/step - loss: 1.3701 - accuracy: 0.6435 - val_loss: 1.2105 - val_accuracy: 0.6917 - lr: 1.0000e-04\n",
      "Epoch 3/30\n",
      "531/531 [==============================] - 35s 66ms/step - loss: 1.1109 - accuracy: 0.7129 - val_loss: 1.0001 - val_accuracy: 0.7517 - lr: 1.0000e-04\n",
      "Epoch 4/30\n",
      "531/531 [==============================] - 32s 61ms/step - loss: 0.9461 - accuracy: 0.7545 - val_loss: 0.8706 - val_accuracy: 0.7860 - lr: 1.0000e-04\n",
      "Epoch 5/30\n",
      "531/531 [==============================] - 35s 65ms/step - loss: 0.8338 - accuracy: 0.7865 - val_loss: 0.7784 - val_accuracy: 0.8103 - lr: 1.0000e-04\n",
      "Epoch 6/30\n",
      "531/531 [==============================] - 31s 57ms/step - loss: 0.7668 - accuracy: 0.8030 - val_loss: 0.7352 - val_accuracy: 0.8183 - lr: 5.0000e-05\n",
      "Epoch 7/30\n",
      "531/531 [==============================] - 20s 38ms/step - loss: 0.7307 - accuracy: 0.8121 - val_loss: 0.7007 - val_accuracy: 0.8270 - lr: 5.0000e-05\n",
      "Epoch 8/30\n",
      "531/531 [==============================] - 21s 39ms/step - loss: 0.6986 - accuracy: 0.8184 - val_loss: 0.6741 - val_accuracy: 0.8313 - lr: 5.0000e-05\n",
      "Epoch 9/30\n",
      "531/531 [==============================] - 35s 65ms/step - loss: 0.6706 - accuracy: 0.8266 - val_loss: 0.6448 - val_accuracy: 0.8417 - lr: 5.0000e-05\n",
      "Epoch 10/30\n",
      "531/531 [==============================] - 30s 56ms/step - loss: 0.6445 - accuracy: 0.8331 - val_loss: 0.6203 - val_accuracy: 0.8457 - lr: 5.0000e-05\n",
      "Epoch 11/30\n",
      "531/531 [==============================] - 20s 37ms/step - loss: 0.6220 - accuracy: 0.8374 - val_loss: 0.5991 - val_accuracy: 0.8480 - lr: 5.0000e-05\n",
      "Epoch 12/30\n",
      "531/531 [==============================] - 20s 37ms/step - loss: 0.6011 - accuracy: 0.8426 - val_loss: 0.5800 - val_accuracy: 0.8523 - lr: 5.0000e-05\n",
      "Epoch 13/30\n",
      "531/531 [==============================] - 27s 51ms/step - loss: 0.5821 - accuracy: 0.8478 - val_loss: 0.5611 - val_accuracy: 0.8573 - lr: 5.0000e-05\n",
      "Epoch 14/30\n",
      "531/531 [==============================] - 34s 65ms/step - loss: 0.5636 - accuracy: 0.8521 - val_loss: 0.5475 - val_accuracy: 0.8577 - lr: 5.0000e-05\n",
      "Epoch 15/30\n",
      "531/531 [==============================] - 36s 67ms/step - loss: 0.5480 - accuracy: 0.8551 - val_loss: 0.5309 - val_accuracy: 0.8630 - lr: 5.0000e-05\n",
      "Epoch 16/30\n",
      "531/531 [==============================] - 34s 65ms/step - loss: 0.5328 - accuracy: 0.8600 - val_loss: 0.5171 - val_accuracy: 0.8653 - lr: 5.0000e-05\n",
      "Epoch 17/30\n",
      "531/531 [==============================] - 34s 65ms/step - loss: 0.5193 - accuracy: 0.8629 - val_loss: 0.5063 - val_accuracy: 0.8700 - lr: 5.0000e-05\n",
      "Epoch 18/30\n",
      "531/531 [==============================] - 36s 68ms/step - loss: 0.5064 - accuracy: 0.8665 - val_loss: 0.4929 - val_accuracy: 0.8697 - lr: 5.0000e-05\n",
      "Epoch 19/30\n",
      "531/531 [==============================] - 34s 65ms/step - loss: 0.4938 - accuracy: 0.8690 - val_loss: 0.4820 - val_accuracy: 0.8763 - lr: 5.0000e-05\n",
      "Epoch 20/30\n",
      "531/531 [==============================] - 32s 61ms/step - loss: 0.4832 - accuracy: 0.8710 - val_loss: 0.4701 - val_accuracy: 0.8767 - lr: 5.0000e-05\n",
      "Epoch 21/30\n",
      "531/531 [==============================] - 20s 37ms/step - loss: 0.4731 - accuracy: 0.8741 - val_loss: 0.4601 - val_accuracy: 0.8783 - lr: 5.0000e-05\n",
      "Epoch 22/30\n",
      "531/531 [==============================] - 20s 37ms/step - loss: 0.4627 - accuracy: 0.8756 - val_loss: 0.4524 - val_accuracy: 0.8817 - lr: 5.0000e-05\n",
      "Epoch 23/30\n",
      "531/531 [==============================] - 32s 60ms/step - loss: 0.4537 - accuracy: 0.8781 - val_loss: 0.4432 - val_accuracy: 0.8830 - lr: 5.0000e-05\n",
      "Epoch 24/30\n",
      "531/531 [==============================] - 35s 65ms/step - loss: 0.4449 - accuracy: 0.8812 - val_loss: 0.4355 - val_accuracy: 0.8853 - lr: 5.0000e-05\n",
      "Epoch 25/30\n",
      "531/531 [==============================] - 36s 67ms/step - loss: 0.4368 - accuracy: 0.8846 - val_loss: 0.4288 - val_accuracy: 0.8887 - lr: 5.0000e-05\n",
      "Epoch 26/30\n",
      "531/531 [==============================] - 35s 65ms/step - loss: 0.4288 - accuracy: 0.8854 - val_loss: 0.4206 - val_accuracy: 0.8873 - lr: 5.0000e-05\n",
      "Epoch 27/30\n",
      "531/531 [==============================] - 35s 66ms/step - loss: 0.4215 - accuracy: 0.8874 - val_loss: 0.4138 - val_accuracy: 0.8873 - lr: 5.0000e-05\n",
      "Epoch 28/30\n",
      "531/531 [==============================] - 26s 50ms/step - loss: 0.4141 - accuracy: 0.8896 - val_loss: 0.4091 - val_accuracy: 0.8920 - lr: 5.0000e-05\n",
      "Epoch 29/30\n",
      "531/531 [==============================] - 20s 37ms/step - loss: 0.4071 - accuracy: 0.8914 - val_loss: 0.4020 - val_accuracy: 0.8927 - lr: 5.0000e-05\n",
      "Epoch 30/30\n",
      "531/531 [==============================] - 22s 42ms/step - loss: 0.4009 - accuracy: 0.8933 - val_loss: 0.3944 - val_accuracy: 0.8953 - lr: 5.0000e-05\n",
      "Model: \"model_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_8 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
      "                                                                 \n",
      " resnet50 (Functional)       (None, 1, 1, 2048)        23587712  \n",
      "                                                                 \n",
      " global_average_pooling2d_3   (None, 2048)             0         \n",
      " (GlobalAveragePooling2D)                                        \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 10)                20490     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 23,608,202\n",
      "Trainable params: 23,555,082\n",
      "Non-trainable params: 53,120\n",
      "_________________________________________________________________\n",
      "None\n",
      "Epoch 1/20\n",
      "531/531 [==============================] - 781s 1s/step - loss: 0.3311 - accuracy: 0.9015 - val_loss: 0.2933 - val_accuracy: 0.9057 - lr: 1.0000e-07\n",
      "Epoch 2/20\n",
      "531/531 [==============================] - 802s 2s/step - loss: 0.2762 - accuracy: 0.9146 - val_loss: 0.2586 - val_accuracy: 0.9180 - lr: 1.0000e-07\n",
      "Epoch 3/20\n",
      "531/531 [==============================] - 810s 2s/step - loss: 0.2435 - accuracy: 0.9266 - val_loss: 0.2285 - val_accuracy: 0.9290 - lr: 1.0000e-07\n",
      "Epoch 4/20\n",
      "531/531 [==============================] - 809s 2s/step - loss: 0.2196 - accuracy: 0.9337 - val_loss: 0.2109 - val_accuracy: 0.9353 - lr: 1.0000e-07\n",
      "Epoch 5/20\n",
      "531/531 [==============================] - 812s 2s/step - loss: 0.2006 - accuracy: 0.9398 - val_loss: 0.1934 - val_accuracy: 0.9410 - lr: 1.0000e-07\n",
      "Epoch 6/20\n",
      "531/531 [==============================] - 812s 2s/step - loss: 0.3701 - accuracy: 0.8878 - val_loss: 0.3205 - val_accuracy: 0.8983 - lr: 5.0000e-06\n",
      "Epoch 7/20\n",
      "531/531 [==============================] - 811s 2s/step - loss: 0.1473 - accuracy: 0.9535 - val_loss: 0.0834 - val_accuracy: 0.9720 - lr: 5.0000e-06\n",
      "Epoch 8/20\n",
      "531/531 [==============================] - 809s 2s/step - loss: 0.0918 - accuracy: 0.9699 - val_loss: 0.0583 - val_accuracy: 0.9797 - lr: 5.0000e-06\n",
      "Epoch 9/20\n",
      "531/531 [==============================] - 811s 2s/step - loss: 0.0711 - accuracy: 0.9775 - val_loss: 0.1507 - val_accuracy: 0.9587 - lr: 5.0000e-06\n",
      "Epoch 10/20\n",
      "531/531 [==============================] - 804s 2s/step - loss: 0.0547 - accuracy: 0.9822 - val_loss: 0.0430 - val_accuracy: 0.9873 - lr: 5.0000e-06\n",
      "Epoch 11/20\n",
      "531/531 [==============================] - 762s 1s/step - loss: 0.0423 - accuracy: 0.9871 - val_loss: 0.0914 - val_accuracy: 0.9773 - lr: 5.0000e-06\n",
      "Epoch 12/20\n",
      "531/531 [==============================] - 762s 1s/step - loss: 0.0355 - accuracy: 0.9882 - val_loss: 0.0327 - val_accuracy: 0.9893 - lr: 5.0000e-06\n",
      "Epoch 13/20\n",
      "531/531 [==============================] - 756s 1s/step - loss: 0.0335 - accuracy: 0.9891 - val_loss: 0.0262 - val_accuracy: 0.9930 - lr: 5.0000e-06\n",
      "Epoch 14/20\n",
      "531/531 [==============================] - 803s 2s/step - loss: 0.0280 - accuracy: 0.9917 - val_loss: 0.0276 - val_accuracy: 0.9923 - lr: 5.0000e-06\n",
      "Epoch 15/20\n",
      "531/531 [==============================] - 812s 2s/step - loss: 0.0301 - accuracy: 0.9916 - val_loss: 0.0294 - val_accuracy: 0.9923 - lr: 5.0000e-06\n",
      "Epoch 16/20\n",
      "531/531 [==============================] - 812s 2s/step - loss: 0.0044 - accuracy: 0.9988 - val_loss: 0.0284 - val_accuracy: 0.9937 - lr: 1.0000e-06\n",
      "Epoch 17/20\n",
      "531/531 [==============================] - 811s 2s/step - loss: 0.0027 - accuracy: 0.9991 - val_loss: 0.0336 - val_accuracy: 0.9920 - lr: 1.0000e-06\n",
      "Epoch 18/20\n",
      "531/531 [==============================] - 806s 2s/step - loss: 0.0017 - accuracy: 0.9996 - val_loss: 0.0319 - val_accuracy: 0.9933 - lr: 1.0000e-06\n",
      "Epoch 19/20\n",
      "531/531 [==============================] - 811s 2s/step - loss: 0.0015 - accuracy: 0.9996 - val_loss: 0.0333 - val_accuracy: 0.9937 - lr: 1.0000e-06\n",
      "Epoch 20/20\n",
      "531/531 [==============================] - 811s 2s/step - loss: 0.0015 - accuracy: 0.9996 - val_loss: 0.0312 - val_accuracy: 0.9943 - lr: 1.0000e-06\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 14.581597222222216, 'Predicted Class')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##############################################################################\n",
    "import logging\n",
    "logging.getLogger(\"tensorflow\").disabled = True\n",
    "import numpy as np\n",
    "import math\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score\n",
    "\n",
    "import tensorflow\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dropout, Flatten, Dense\n",
    "from tensorflow.keras.layers import BatchNormalization as BatchNorm\n",
    "\n",
    "# pre-trained models\n",
    "from tensorflow.keras.applications.vgg16 import VGG16\n",
    "from tensorflow.keras.applications import ResNet50, InceptionV3, Xception, DenseNet121\n",
    "\n",
    "# custom learning rates\n",
    "import learning_rate as lr\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "##############################################################################\n",
    "# please change following for your dataset (dataset details and learning params)\n",
    "# dataset details\n",
    "train_data_dir = '/home/engs2426/Downloads/HCR-Net-main/Data/Offline/UCI_DevanagariHandwrittenCharacterDataset/digits/Train'\n",
    "validation_data_dir = '/home/engs2426/Downloads/HCR-Net-main/Data/Offline/UCI_DevanagariHandwrittenCharacterDataset/digits/Test'\n",
    "nb_train_samples = 17000\n",
    "nb_validation_samples = 3000\n",
    "num_classes = 10\n",
    "\n",
    "# learning params\n",
    "epochs1 = 30\n",
    "epochs2 = 20\n",
    "learning_rate1 = lr.decayed_learning_rate\n",
    "learning_rate2 = lr.decayed_learning_rate_tuned20\n",
    "\n",
    "# set seeds for reproduceability\n",
    "np.random.seed(4)\n",
    "tensorflow.random.set_seed(4)\n",
    "##############################################################################\n",
    "batch_size = 32\n",
    "# dimensions of our images.\n",
    "img_width, img_height = 32, 32\n",
    "input_shape = (img_width, img_height, 3)\n",
    "##############################################################################\n",
    "#### TRAINING PHASE - I #########\n",
    "#################################\n",
    "base_model = ResNet50(include_top=False, input_shape=input_shape)\n",
    "base_model.trainable = False\n",
    "inputs = tensorflow.keras.Input(shape=input_shape)\n",
    "x = base_model(inputs, training=False)\n",
    "x = tensorflow.keras.layers.GlobalAveragePooling2D()(x)\n",
    "outputs = tensorflow.keras.layers.Dense(num_classes, activation='softmax')(x)\n",
    "model = tensorflow.keras.Model(inputs, outputs)\n",
    "model.compile(loss='categorical_crossentropy', optimizer=tensorflow.keras.optimizers.RMSprop(), metrics=['accuracy'])\n",
    "print(model.summary())\n",
    "\n",
    "# using ImageDataGenerator to load and scale data\n",
    "train_datagen = ImageDataGenerator(rescale=1.0/255)\n",
    "val_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "                    train_data_dir,\n",
    "                    target_size=(img_width, img_height),\n",
    "                    batch_size=batch_size,\n",
    "                    color_mode=\"rgb\",\n",
    "                    class_mode='categorical',\n",
    "                    shuffle=True,\n",
    "                    seed=13)\n",
    "validation_generator = val_datagen.flow_from_directory(\n",
    "                    validation_data_dir,\n",
    "                    target_size=(img_width, img_height),\n",
    "                    batch_size=nb_validation_samples,\n",
    "                    color_mode=\"rgb\",\n",
    "                    shuffle=False,\n",
    "                    class_mode='categorical')\n",
    "\n",
    "# define callback for custom learning rate\n",
    "callback = tensorflow.keras.callbacks.LearningRateScheduler(learning_rate1)\n",
    "\n",
    "history1 = model.fit(\n",
    "                train_generator,\n",
    "                steps_per_epoch=nb_train_samples // batch_size,\n",
    "                epochs=epochs1,\n",
    "                callbacks=[callback],\n",
    "                validation_data=validation_generator,\n",
    "                validation_steps=1)\n",
    "\n",
    "# save the model\n",
    "# model.save_weights('./results/HCR-Net-I.h5')\n",
    "\n",
    "#################################\n",
    "#### TRAINING PHASE - II #########\n",
    "#################################\n",
    "# fine tuning the model by unfreezing the lower layers (used from VGG16)\n",
    "base_model.trainable = True\n",
    "\n",
    "# reset the image generators\n",
    "train_generator.reset()\n",
    "validation_generator.reset()\n",
    "\n",
    "# recompile the model\n",
    "model.compile(loss='categorical_crossentropy', optimizer=tensorflow.keras.optimizers.RMSprop(), metrics=['accuracy'])\n",
    "print(model.summary())\n",
    "\n",
    "# define callback for custom learning rate\n",
    "callback = tensorflow.keras.callbacks.LearningRateScheduler(learning_rate2)\n",
    "\n",
    "history2 = model.fit(\n",
    "                train_generator,\n",
    "                steps_per_epoch=nb_train_samples // batch_size,\n",
    "                epochs=epochs2,\n",
    "                callbacks=[callback],\n",
    "                validation_data=validation_generator,\n",
    "                validation_steps=1)\n",
    "\n",
    "# save the model\n",
    "model.save_weights('./results/HCR-Net-II.h5')\n",
    "\n",
    "##############################################################################\n",
    "# plot the convergence, in terms of accuracy and loss\n",
    "plt.plot(history1.history['accuracy']+history2.history['accuracy'])\n",
    "plt.plot(history1.history['val_accuracy']+history2.history['val_accuracy'])\n",
    "# plt.title('model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(['Train', 'Test'], loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history1.history['loss']+history2.history['loss'])\n",
    "plt.plot(history1.history['val_loss']+history2.history['val_loss'])\n",
    "# plt.title('model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(['Train', 'Test'], loc='upper right')\n",
    "plt.show()\n",
    "\n",
    "# evaluate model and plot confusion matrix\n",
    "# filenames = validation_generator.filenames\n",
    "predict = model.predict(validation_generator)\n",
    "y_pred = np.argmax(predict,axis=-1)\n",
    "cf_matrix = confusion_matrix(validation_generator.classes,y_pred)\n",
    "\n",
    "# calculate size of confusion matrix figure, and plot it\n",
    "fig, ax = plt.subplots(figsize=(math.ceil(num_classes/3.0), math.ceil(num_classes/3.0)))\n",
    "labels = list(validation_generator.class_indices.keys())\n",
    "sns.heatmap(cf_matrix, annot=True, xticklabels=labels, yticklabels=labels, fmt='', cmap='Blues')\n",
    "plt.ylabel('Actual Class')\n",
    "plt.xlabel('Predicted Class')\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision_score:  0.9943440984420177\n",
      "recall_score   :  0.9943333333333333\n",
      "f1_score       :  0.9943288073554551\n",
      "accuracy_score :  0.9943333333333333\n",
      "\n",
      "Best accuracy  :  0.9943333268165588\n"
     ]
    }
   ],
   "source": [
    "# calculate different metrics for HCR-Net on the test dataset\n",
    "print('precision_score: ', precision_score(validation_generator.classes,y_pred, average=\"macro\"))\n",
    "print('recall_score   : ', recall_score(validation_generator.classes,y_pred, average=\"macro\"))\n",
    "print('f1_score       : ', f1_score(validation_generator.classes,y_pred, average=\"macro\"))\n",
    "print('accuracy_score : ', accuracy_score(validation_generator.classes,y_pred, normalize=True))\n",
    "\n",
    "print(\"\\nBest accuracy  : \", max(max(history1.history['val_accuracy']), max(history2.history['val_accuracy'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_4\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_10 (InputLayer)       [(None, 32, 32, 3)]       0         \n",
      "                                                                 \n",
      " resnet50 (Functional)       (None, 1, 1, 2048)        23587712  \n",
      "                                                                 \n",
      " global_average_pooling2d_4   (None, 2048)             0         \n",
      " (GlobalAveragePooling2D)                                        \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 10)                20490     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 23,608,202\n",
      "Trainable params: 20,490\n",
      "Non-trainable params: 23,587,712\n",
      "_________________________________________________________________\n",
      "None\n",
      "Found 17000 images belonging to 10 classes.\n",
      "Found 3000 images belonging to 10 classes.\n",
      "Epoch 1/30\n",
      "531/531 [==============================] - 45s 73ms/step - loss: 1.8948 - accuracy: 0.4015 - val_loss: 1.5378 - val_accuracy: 0.5903 - lr: 1.0000e-04\n",
      "Epoch 2/30\n",
      "531/531 [==============================] - 37s 69ms/step - loss: 1.3619 - accuracy: 0.6344 - val_loss: 1.2024 - val_accuracy: 0.6963 - lr: 1.0000e-04\n",
      "Epoch 3/30\n",
      "531/531 [==============================] - 36s 68ms/step - loss: 1.1084 - accuracy: 0.7107 - val_loss: 1.0044 - val_accuracy: 0.7507 - lr: 1.0000e-04\n",
      "Epoch 4/30\n",
      "531/531 [==============================] - 36s 69ms/step - loss: 0.9489 - accuracy: 0.7562 - val_loss: 0.8715 - val_accuracy: 0.7877 - lr: 1.0000e-04\n",
      "Epoch 5/30\n",
      "531/531 [==============================] - 36s 68ms/step - loss: 0.8374 - accuracy: 0.7864 - val_loss: 0.7764 - val_accuracy: 0.8150 - lr: 1.0000e-04\n",
      "Epoch 6/30\n",
      "531/531 [==============================] - 20s 37ms/step - loss: 0.7708 - accuracy: 0.8019 - val_loss: 0.7395 - val_accuracy: 0.8237 - lr: 5.0000e-05\n",
      "Epoch 7/30\n",
      "531/531 [==============================] - 20s 37ms/step - loss: 0.7343 - accuracy: 0.8099 - val_loss: 0.7062 - val_accuracy: 0.8277 - lr: 5.0000e-05\n",
      "Epoch 8/30\n",
      "531/531 [==============================] - 29s 55ms/step - loss: 0.7029 - accuracy: 0.8175 - val_loss: 0.6783 - val_accuracy: 0.8357 - lr: 5.0000e-05\n",
      "Epoch 9/30\n",
      "531/531 [==============================] - 36s 67ms/step - loss: 0.6745 - accuracy: 0.8247 - val_loss: 0.6499 - val_accuracy: 0.8380 - lr: 5.0000e-05\n",
      "Epoch 10/30\n",
      "531/531 [==============================] - 36s 68ms/step - loss: 0.6491 - accuracy: 0.8316 - val_loss: 0.6258 - val_accuracy: 0.8443 - lr: 5.0000e-05\n",
      "Epoch 11/30\n",
      "531/531 [==============================] - 36s 68ms/step - loss: 0.6259 - accuracy: 0.8369 - val_loss: 0.6058 - val_accuracy: 0.8453 - lr: 5.0000e-05\n",
      "Epoch 12/30\n",
      "531/531 [==============================] - 36s 68ms/step - loss: 0.6045 - accuracy: 0.8412 - val_loss: 0.5848 - val_accuracy: 0.8517 - lr: 5.0000e-05\n",
      "Epoch 13/30\n",
      "531/531 [==============================] - 36s 68ms/step - loss: 0.5856 - accuracy: 0.8454 - val_loss: 0.5663 - val_accuracy: 0.8583 - lr: 5.0000e-05\n",
      "Epoch 14/30\n",
      "531/531 [==============================] - 36s 68ms/step - loss: 0.5681 - accuracy: 0.8488 - val_loss: 0.5515 - val_accuracy: 0.8603 - lr: 5.0000e-05\n",
      "Epoch 15/30\n",
      "531/531 [==============================] - 36s 68ms/step - loss: 0.5520 - accuracy: 0.8522 - val_loss: 0.5373 - val_accuracy: 0.8653 - lr: 5.0000e-05\n",
      "Epoch 16/30\n",
      "531/531 [==============================] - 36s 68ms/step - loss: 0.5375 - accuracy: 0.8571 - val_loss: 0.5236 - val_accuracy: 0.8673 - lr: 5.0000e-05\n",
      "Epoch 17/30\n",
      "531/531 [==============================] - 36s 68ms/step - loss: 0.5233 - accuracy: 0.8604 - val_loss: 0.5092 - val_accuracy: 0.8690 - lr: 5.0000e-05\n",
      "Epoch 18/30\n",
      "531/531 [==============================] - 36s 68ms/step - loss: 0.5107 - accuracy: 0.8637 - val_loss: 0.4970 - val_accuracy: 0.8700 - lr: 5.0000e-05\n",
      "Epoch 19/30\n",
      "531/531 [==============================] - 28s 53ms/step - loss: 0.4982 - accuracy: 0.8667 - val_loss: 0.4852 - val_accuracy: 0.8720 - lr: 5.0000e-05\n",
      "Epoch 20/30\n",
      "531/531 [==============================] - 36s 68ms/step - loss: 0.4873 - accuracy: 0.8698 - val_loss: 0.4744 - val_accuracy: 0.8743 - lr: 5.0000e-05\n",
      "Epoch 21/30\n",
      "531/531 [==============================] - 36s 67ms/step - loss: 0.4763 - accuracy: 0.8709 - val_loss: 0.4666 - val_accuracy: 0.8783 - lr: 5.0000e-05\n",
      "Epoch 22/30\n",
      "531/531 [==============================] - 36s 68ms/step - loss: 0.4664 - accuracy: 0.8736 - val_loss: 0.4570 - val_accuracy: 0.8820 - lr: 5.0000e-05\n",
      "Epoch 23/30\n",
      "531/531 [==============================] - 36s 68ms/step - loss: 0.4568 - accuracy: 0.8776 - val_loss: 0.4493 - val_accuracy: 0.8797 - lr: 5.0000e-05\n",
      "Epoch 24/30\n",
      "531/531 [==============================] - 36s 68ms/step - loss: 0.4484 - accuracy: 0.8794 - val_loss: 0.4388 - val_accuracy: 0.8817 - lr: 5.0000e-05\n",
      "Epoch 25/30\n",
      "531/531 [==============================] - 36s 68ms/step - loss: 0.4403 - accuracy: 0.8824 - val_loss: 0.4317 - val_accuracy: 0.8837 - lr: 5.0000e-05\n",
      "Epoch 26/30\n",
      "531/531 [==============================] - 36s 68ms/step - loss: 0.4320 - accuracy: 0.8848 - val_loss: 0.4240 - val_accuracy: 0.8867 - lr: 5.0000e-05\n",
      "Epoch 27/30\n",
      "531/531 [==============================] - 36s 68ms/step - loss: 0.4245 - accuracy: 0.8857 - val_loss: 0.4204 - val_accuracy: 0.8890 - lr: 5.0000e-05\n",
      "Epoch 28/30\n",
      "531/531 [==============================] - 36s 67ms/step - loss: 0.4176 - accuracy: 0.8876 - val_loss: 0.4106 - val_accuracy: 0.8893 - lr: 5.0000e-05\n",
      "Epoch 29/30\n",
      "531/531 [==============================] - 36s 68ms/step - loss: 0.4104 - accuracy: 0.8887 - val_loss: 0.4047 - val_accuracy: 0.8927 - lr: 5.0000e-05\n",
      "Epoch 30/30\n",
      "531/531 [==============================] - 36s 68ms/step - loss: 0.4036 - accuracy: 0.8917 - val_loss: 0.3992 - val_accuracy: 0.8907 - lr: 5.0000e-05\n",
      "Model: \"model_4\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_10 (InputLayer)       [(None, 32, 32, 3)]       0         \n",
      "                                                                 \n",
      " resnet50 (Functional)       (None, 1, 1, 2048)        23587712  \n",
      "                                                                 \n",
      " global_average_pooling2d_4   (None, 2048)             0         \n",
      " (GlobalAveragePooling2D)                                        \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 10)                20490     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 23,608,202\n",
      "Trainable params: 23,555,082\n",
      "Non-trainable params: 53,120\n",
      "_________________________________________________________________\n",
      "None\n",
      "Epoch 1/20\n",
      "531/531 [==============================] - 794s 1s/step - loss: 0.3346 - accuracy: 0.8983 - val_loss: 0.2941 - val_accuracy: 0.9117 - lr: 1.0000e-07\n",
      "Epoch 2/20\n",
      "531/531 [==============================] - 426s 802ms/step - loss: 0.2799 - accuracy: 0.9134 - val_loss: 0.2558 - val_accuracy: 0.9193 - lr: 1.0000e-07\n",
      "Epoch 3/20\n",
      "531/531 [==============================] - 425s 801ms/step - loss: 0.2469 - accuracy: 0.9244 - val_loss: 0.2330 - val_accuracy: 0.9283 - lr: 1.0000e-07\n",
      "Epoch 4/20\n",
      "531/531 [==============================] - 425s 801ms/step - loss: 0.2226 - accuracy: 0.9338 - val_loss: 0.2077 - val_accuracy: 0.9397 - lr: 1.0000e-07\n",
      "Epoch 5/20\n",
      "531/531 [==============================] - 426s 802ms/step - loss: 0.2040 - accuracy: 0.9388 - val_loss: 0.1912 - val_accuracy: 0.9420 - lr: 1.0000e-07\n",
      "Epoch 6/20\n",
      "531/531 [==============================] - 426s 803ms/step - loss: 0.3685 - accuracy: 0.8864 - val_loss: 0.1191 - val_accuracy: 0.9613 - lr: 5.0000e-06\n",
      "Epoch 7/20\n",
      "531/531 [==============================] - 426s 803ms/step - loss: 0.1448 - accuracy: 0.9540 - val_loss: 0.2071 - val_accuracy: 0.9307 - lr: 5.0000e-06\n",
      "Epoch 8/20\n",
      "531/531 [==============================] - 426s 802ms/step - loss: 0.0926 - accuracy: 0.9707 - val_loss: 0.0568 - val_accuracy: 0.9807 - lr: 5.0000e-06\n",
      "Epoch 9/20\n",
      "531/531 [==============================] - 426s 802ms/step - loss: 0.0730 - accuracy: 0.9757 - val_loss: 0.0642 - val_accuracy: 0.9770 - lr: 5.0000e-06\n",
      "Epoch 10/20\n",
      "531/531 [==============================] - 426s 801ms/step - loss: 0.0548 - accuracy: 0.9831 - val_loss: 0.0463 - val_accuracy: 0.9867 - lr: 5.0000e-06\n",
      "Epoch 11/20\n",
      "531/531 [==============================] - 426s 802ms/step - loss: 0.0484 - accuracy: 0.9847 - val_loss: 0.0519 - val_accuracy: 0.9857 - lr: 5.0000e-06\n",
      "Epoch 12/20\n",
      "531/531 [==============================] - 425s 801ms/step - loss: 0.0398 - accuracy: 0.9875 - val_loss: 0.0729 - val_accuracy: 0.9807 - lr: 5.0000e-06\n",
      "Epoch 13/20\n",
      "531/531 [==============================] - 426s 801ms/step - loss: 0.0319 - accuracy: 0.9902 - val_loss: 0.0281 - val_accuracy: 0.9930 - lr: 5.0000e-06\n",
      "Epoch 14/20\n",
      "531/531 [==============================] - 426s 802ms/step - loss: 0.0317 - accuracy: 0.9907 - val_loss: 0.0390 - val_accuracy: 0.9900 - lr: 5.0000e-06\n",
      "Epoch 15/20\n",
      "531/531 [==============================] - 426s 802ms/step - loss: 0.0264 - accuracy: 0.9921 - val_loss: 0.0486 - val_accuracy: 0.9873 - lr: 5.0000e-06\n",
      "Epoch 16/20\n",
      "531/531 [==============================] - 426s 801ms/step - loss: 0.0041 - accuracy: 0.9985 - val_loss: 0.0256 - val_accuracy: 0.9947 - lr: 1.0000e-06\n",
      "Epoch 17/20\n",
      "531/531 [==============================] - 426s 802ms/step - loss: 0.0032 - accuracy: 0.9988 - val_loss: 0.0305 - val_accuracy: 0.9933 - lr: 1.0000e-06\n",
      "Epoch 18/20\n",
      "531/531 [==============================] - 426s 802ms/step - loss: 0.0020 - accuracy: 0.9994 - val_loss: 0.0268 - val_accuracy: 0.9947 - lr: 1.0000e-06\n",
      "Epoch 19/20\n",
      "531/531 [==============================] - 425s 801ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.0239 - val_accuracy: 0.9960 - lr: 1.0000e-06\n",
      "Epoch 20/20\n",
      "531/531 [==============================] - 426s 802ms/step - loss: 0.0013 - accuracy: 0.9994 - val_loss: 0.0279 - val_accuracy: 0.9950 - lr: 1.0000e-06\n"
     ]
    },
    {
     "data": {
      "image/png": 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csZ+36i7h0qCvCcr+xtwYGGyuRyXiYQo3IiKdwb4tZm9N3loAqgdczJ+tN/Pye8W4jP3YA61MHhzPWQPjOaVvDOHBQU3eYuKgOJ75fCvLtu7jYI2TEFtAiz9+5Y797CWKn1KvZsD2l8yNvc8EexdPfDuRRhRuREQ6MpcLVv4DPnsY6qowgiP5ovcsZvyQSllVMQDnDk1g1uT+9OgaetS3GpgQQY+uIew+cJDlW/cxcVB8i5pQVetkbY75WWFn/Ab2vAtVJVpLSrxG4UZEpKPa+TV8+gdzZBJQFD+e6aU3sGp1MFDHoMQIHjp/0KGlEH6BxWJh4sB4Fny9g0827m1xuFmXU0yN00VsuJ2k7okw9XVzTanBFx/rNxM5KoUbEZGOZt8WyHjIXNgScAWG8s/wm3ho52jAQkwXG7+Z1I9L05MIsLZgIr7DTBwUx4Kvd/D55r3UOV0EBvzyRPffHbaelMVigdTx5kPESxRuREQ6irK9sGQ2fP8qGE4MSwBrYqcwPedMCsodBAVYuHFcKnee0bvZmpqWGJHSla6hQRyorGXVzgOM6RX9i+c0Wk9KxAcUbkRE2rvqcnOV7q+fhdoKAEqSJ3Jv0RS+2BUJwJkD4rj/3AGkxoQd10cFBliZMCCOtzN382lW/i+Gm1qni8xdBwAYmfrLQUjEExRuRETaK2cdrH0NvvwTlO8FwJU4nEVdb+EPaxw4XQYxXez86aLBLa6PaYmJA+vDzca9PHjeQPNW0xH8kFvCwVonkaFB9InVyCjxDYUbEZH2prYK1r0BK541F7QE6NqT7BN/w69WJ7FlezlgcMGwRB65YFCrZxP+JeP7dCM4yEpu8UGy8koZlOg44rENt6RO6hmFtZX1PSLHSuFGRKS9qCqF1Qvg27nunhpCoqg7+T6eKz2V5z/OwekqJ6aLjcemDOHswZ7rrTlciC2AU/p049OsvXy6cW+Lwo3qbcSXFG5ERNq68gL4dh6sevHQat0R3WHMnWQlTOHe935iy95sAM4bmsCjFw4mysO9NT83cVC8GW6y9nLvWX2bPcbpMli589BIKRFfUbgREWmr9m+HFc/BmtfBWW1ui+kH4+7BGHIp//h6N3+ZvxanyyAqzMZjUwZzzpAEnzRtQv9YAqwWNuWVkrO/0r1Ew+G25JdRVlVHmC2AgQkRPmmXCCjciIi0PS4XLP41ZL4Ehsvc1n0EjJ8JfSfjwsIfP8zipa93AnDukAQevXAQ0V3sPmti1zAbI3tG8c32Ij7ZmM/N49OaHLNyRxEA6T2jWjQfjoinKNyIiLQ1y/4Cq180X/c+E06+F1LGgcVCrdPFb99ex7trcgF48LyB3Hhyql+aOXFQHN9sL+LTrL3Nh5udqrcR/1CUFhFpS7Z+Zk7EB3DhXLjmHeh5MlgsVNU6ue21TN5dk0uA1cLTU4f5LdgAnDUwDoDVO/dTVF7daJ9hGO5iYtXbiK8p3IiItBUHdsI7NwEGjLgRTrzavau0qpbrFqzks00F2AOtzL82nYtO7OG3pgL06BrKoMQIXAZ8vrmg0b7thRUUltdgC7QytMeRR1OJeIPfw83cuXNJTU0lODiY9PR0li9fftTj//a3vzFgwABCQkLo168fr776qo9aKiLiRbUH4c3roKoYuqfD2Y+7dxWWV3Pl/G9ZuWM/4fZAXr1xJBMGxPmvrYeZONAcbv7pxr2Ntjf02pyYFIk9MMDn7ZLOza/hZtGiRcyYMYP777+fNWvWMH78eCZPnkx2dnazx8+bN49Zs2bx8MMPs3HjRh555BHuuOMO/vOf//i45SIiHrb415C3DkKj4fJXIdAsDs7ZX8llf/+GjXtKieliY+GtoxmV1naWMZg4yAxZy7fuo7Kmzr1d89uIP/k13Dz11FPcdNNN3HzzzQwYMIA5c+aQlJTEvHnzmj3+n//8J7feeitTp04lLS2NK664gptuuok///nPPm65iIgHZb4Ca14DixUuXQAO83bT1r1lXPb3b9hRWEH3yBDemj72qBPm+UP/+HCSokKornOx7MdCwKy3+W67OVKqLQUx6Tz8Fm5qamrIzMxk4sSJjbZPnDiRFStWNHtOdXU1wcHBjbaFhISwcuVKamtrj3hOaWlpo4eISJuRm2n22gCc8QCknQZA5q4DXPaPb8gvraJPbBfeuW3scS966Q0Wi+WwW1P5AOw+cJA9JVUEWi2cmBzpx9ZJZ+W3cFNYWIjT6SQurvF947i4OPLz85s9Z9KkSbzwwgtkZmZiGAarV69mwYIF1NbWUlhY2Ow5s2fPxuFwuB9JSUke/y4iIsekogjenAbOGuh/HjWj7+HD9Xlc/cK3XDJvBcWVtZyQFMmbt44h3hH8y+/nJ5PqF+X8fHMBtU6X+5bUkB4OQm2acUR8z+//1P18NVnDMI64wuwDDzxAfn4+o0ePxjAM4uLiuP7663niiScICGi+YG3WrFnMnDnT/XdpaakCjoj4n8sJ794MJTnURqbxfJd7ef3PX1BYXgOAxQLnDEngiUuGEmb3+3+qjyo9pStRYTb2V9Swasd+DQEXv/Nbz01MTAwBAQFNemkKCgqa9OY0CAkJYcGCBVRWVrJz506ys7Pp2bMn4eHhxMTENHuO3W4nIiKi0UNExCsMAyr3g7P52+SHc37xJ9j2BdWWYM4vuIVnvtpLYXkN3cLt3Hl6b5b95nT+dtXwNh9sAAKsFs4cEAvAp1l7NXmf+J3f/q2x2Wykp6eTkZHBRRdd5N6ekZHBhRdeeNRzg4KC6NHDLLhbuHAh5513Hlar30e1i0hnU14Ae9Y0fpTvNQuDwxMgMhkcSRCZhMuRTL6lGz9WRVK0Yx2X/PRXAH5TfRObXcmM7xPDVSOTOXNgHEHtcKmCiQPjeXP1bv69NpcDlbVYLJCeonAj/uHX/0swc+ZMrr32WkaMGMGYMWOYP38+2dnZTJ8+HTBvKeXm5rrnsvnxxx9ZuXIlo0aN4sCBAzz11FP88MMPvPLKK/78GiLS3hkG1JSbPS7OWnDVmnUwzrr61/WPqhLIW1sfZNZC6e4jvJ8LSnPNB98AZjd5Yv2jwULLZBLHX8fSkUmkRLe9YuHWOLlPDCFBARyoNHutBsRH4AgJ8nOrpLPya7iZOnUqRUVFPProo+Tl5TF48GAWL15MSkoKAHl5eY3mvHE6nTz55JNs2bKFoKAgTj/9dFasWEHPnj399A1EpN0wDCjLh/3bzNW2i+qfGx61la1/SywU2JPZHtSXzdZerHemsaqqO9UVJfSw7KO7pbDRI9m6jx7WIkKMgxTGjOTiX72Mzd52C4VbIzgogFP7duPj+hFTqrcRf7IYhmH4uxG+VFpaisPhoKSkRPU3Ih2RYZg9Jns3Qv4G2PsDFG5tcYAxsOC0BFJHALVGADVGIDUEUGcEcBAbm41k1rnS2OBKY6PRkwpCmn2f5KhQ+seH0z8hggH1z8lRoQRYgOpSsHUBa8eauffd73cz8811AMy7ejiThyT4uUXSkbTm97vtV6qJiBxJbRXs22wGmPwfYO8PGPk/YKk60OzhTqzkEctOI47trlh2uOLZaZiPfKMrNQThxAo0HbHpCAkiPiKYrmFBOEKCSA2xcUKo+doREkRkaBCRITYiQ4PoGRNGl6MVAge3rYn4POWM/rHYA60YBpyknhvxI4UbEfEcZy1Ul0FIV3Ms8/GqqYDiHCjOhpJs92ujOAdncTaBFXubnGIBao0AthmJbDKS2eRK5kejBzuNeHKNbtT+7D97gVYL4SGBxIfaSIwMJsERQmJkCImOYBIiQ+hev609jFryt8hQG//61WjqnC5iutj93RzpxPRvq4gcu/ICyFkJOd/B7lVmoW1dFdgdEJUK0b0gKg2i6p+je5lrJ1ks5u2jqmIzuBTnQElO/XP2oW0H9zf7sRYO/cdrv9GFTa4UNhvJ9WEmhX3BPeneLZLU6DB6xoRxjiOYiOBAutiDCA8OJDw4kC7BgUQEB2EPtB5xbi1pvfSUrv5ugojCjYi0kLPOvP2ze9WhQFO8q/ljq+tHFeWtbbrPHgFd4szi3pqyX/zYEiOUXKMbuUYMu+sfuUY39lpiCe6WQlxcIj27daFndBjXxISRGh2GI1SjdEQ6M4UbEWleXbXZE7PzK9i1wgwzNeU/O8gCsQOgx0mQNAqSRkJEdzP0NBqRtA1j/3YoycVSXWoW1NYrMiLIMWLIrQ8tuYcFmFwjhjJCieliZ0BCOAMTIhiSEMHlCRGkdQtrl/PBiIj3KdyIiKn2oNkrs/Nr2PW1+bquqvExdgf0GGGGmKSR0D29+eLY2AHURPVjQ0gx39bsZ+W+/WQWH6C2upIkSwHdLCUUGJHkGjFUYSfAaiEu3E58fZ1LmiOYsY4Q+sR2YUBCBN3CVb8hIi2ncCPS2Rwsrp/rZceh3pWirZC33pyw7nChMdBzHKTUP2IHQjOzgbtcBrnFB/mpoJz1u0v4bkcR32cfoKrW1ei4iOBQeqYOJz0lipToUOIdwSQ6QugWbgYcERFPULgR6WictVC6p36EUf1Io4aJ6oq2HbFIFzCXDEgZdyjQxPRtNOqpzuli175ytu4tZ9u+crbuLeOnfeVsK6jgYK2zydtFhdkY2TOKUWlRjEyNon98hEKMiHidwo1Ie+CsM+tUqkoOPapLobKo8Uij4mwo22NO/380XeLcI5jquqaSF5DA7uC+5FvjKT5Yx4EDtZTk1nCgci3FB2spqazhQGUteSUHqXU2P+9nUICFtJgu9IsP56TUKEanRtE7totGIomIzynciPhL7UFzxFBZPpTlNX2uKDwUYpoU8v6CAJt7wUYcSeaw7KhelIQkkVUdww/7nGTllbJpZyk/rSynzmUABfWPowu1BdCrWxf6xHahV6z53Du2C8lRoQSqwFdE2gCFGxFvMAyo2Nd03paGHpbSXHOOl9YKCjMLeIMj6p8jwdHDDDGRyeBIhsgknKHd2Ln/IJvzytiUV8qmbaVkfVVKXkk+kN/kbR0hQfToGmLOshtqIzIkiK6htsZ/hwURF2HWyFh1a0lE2jCFGxFPKN0DP34MWzPMdYxKcpqONGpOYAiEx5u1Lj9/7tLNDC/BEeazPRwCms7fcqCihs35ZojZvLmUzfk/sSV/DdV1zd+aSokOZWBCBAMTIhiQEMHAxAgSHMG6fSQiHYbCjcixMAxzQrstH8GWxeZ8ME1YzJDi7lVpuE2UDI7u5r5gR6uWKSgqr2Z9bgkbdpewfncxP+SWkl/afIgKDrLSLz6C/nHhDEw0Q0z/+HDCgzXBnYh0bAo3Ii1VVwO7vqoPNB+ZvTNuFnMiu35nQ/cRZoiJ6AGBtmP+uJKDtfyQW8L6+iCzfncJucUHmz32iCtQ6/aRiHRCCjciDQyjfvTRrqYjkEpyzHlhaisOHR8YAr1Oh36Toe/Z0CX2mD+6rKqWjXtK2bC7hA255mNHYUWzx6Z1C2NYj0iGdHcwtIeD/gkRR1+BWkSkk9F/EaVzK9oGmS+ZtTLF2VBbefTjw2LN3pl+50DqqWALbfVHVtbU8UNuaf1tpRLW1wcZo5kR1klRIQztHsnQHg6G9HAwpLtDt5VERH6Bwo10Ps5a87bS6gWw/cum+7vEmzUyDcOoI5MgMsXcFt2n2Rl6f8nuA5V8sbmAzzcV8M32ImqaKfZNdAQzpIeDoT0iGdzdDDJRYcd+W0tEpLNSuJHOo2Q3ZL4C378K5Q3DoS3Q+0wYfh3EDTKHVQce/zpGTpfB2pxiPt+0ly82F7A5v/Hq13ERdobW31pq6JGJ6aL1k0REPEHhRjo2lxO2fWH20vz48aGZe8O6wYnXQvo06NrzuD/GMMy1ldZkF/PllgKWbNnH/ooa936rBdJTujJhQBwT+sdq5l4RES9SuJGOqTgH1r4Oa143J9Fr0HM8jLgB+p9/XCOZ9lfUsG53MetyzFFM63KKKToszACEBwdyat9unDkgjlP7dqOrbjGJiPiEwo10HHXVsPm/sOY12PYlUF+hG+yAE66G9BugW99Wv215dR0b64dkr9tdzLrdxeTsbzokO9BqoX9COKNSo5kwIJaTekYRpOUIRER8TuFG2r/8H2DNP2H9Ijh44ND21FPgxOtgwHkQFNKit6qqNddcWp9T7J4s76d95c2OZGoYkj20h4NhSZEMTIggOCjAQ19KRESOlcKNtF0ul7lgZMPike4VsetfH9xv1tEcPjtweCKccBWceI25WOQvyC+pYsW2Qlbu2M/63SX8uLesfhHJxg4fyXRCkjmayRGiIdkiIm2Rwo20LYVbzV6YDe9A2Z5DBcBHYw00J9I78TroPQGsR+492V9Rw7fbi1ixrZAVPxWxvZmJ8mK62NwjmYYlORjc3UFsePDxfCsREfEhhRvxv5oK2Pi+GWqyv2m6P8BWvwK2A+wRjVfF7tYfhlxuLjLZjPLqOlbuKGLFT0Ws2FZEVl5po/1WCwzp7mB0WjQnJkcytEekFpEUEWnnFG7EPwwDcjPNOWd+eBdq6ueBsVihz0TztlKPkWaACWp5r0l1nZM12cWs+KmQr7cVsS6nuMltpr5xXRjbK4axvaIZlRat20siIh2Mwo34hstlTpxXtA3y1ppDtPdtOrS/ayoMvxaGXQURCa14W4OsvFK+rg8zq3bs52Cts9ExSVEhjOsVw5he0YzpFa1bTCIiHZzCjXhW6R4zwOzfBvu317/eYb6u+9nw6cAQGHihGWpSxkELbwWVV9exZEsBn2zcy1db93GgsrbR/ugwG2N7xzCuVzTjeseQFNX69Z9ERKT9UriR42cY8NNnsPQJ2L3yyMdZAurXZ+plFgAPvhRCIlv0EQcqavhs014+2ZjPsq2FjdZmCrMFMCotmrH1YaZfXDhWq2pmREQ6K4UbOXaGAVsWw7K/HBqObQmArikQ1Qui0swgE5VmPiKTIaDl9S17S6v4dGM+H2/M59vt+3EeVjvTMzqUSYPjOXNAHCckRWqyPBERcVO4kdZzuWDTv2HZX2HvD+a2oFA46SYYcxeExx3zW1fXOVm8IY/Xv81m9a4Djfb1jw/n7MHxnD04nn5x4RrRJCIizVK4kZZz1sHGd81QU7jF3GYLh1G3wOg7ICz6mN86v6SK17/bxRsrsyksP7RG0/DkSM4eHM+kQfGkRIcd7zcQEZFOQOFGjs5ZB/nrYOdXkPmyWRgM5hDtUbfBqFshNOqY3towDFbtPMAr3+zkkx/y3UO24yOCuWZ0MpemJxHv0MgmERFpHYUbaayuBvZ8b4aZXSsg5ztzCYQGIVEw5g4Y+Ssz4ByDgzVOPliXy8srdrHpsEn1RqZGMW1MTyYOilMNjYiIHDOFG4Hc72Hrp2ag2b0K6qoa7w92QPJY6HWGuW6Tvcsxfcye4oO8+s0uFq7Kprh++HZwkJUpJ3TnujE9GZgYcbzfREREROGmU8v/Ab74o7n45OFCYyBlLPQ82XyOHQTWY+tJMQyD77OLWfD1Dj7+Id894qlH1xCuG5PC5SOSiAy1He83ERERcVO46Yz274Av/wQb3gIMc/j2gPMg9VQz0MT0bfGEekdSU+fiox/yWPDVDtbtLnFvH5MWzQ3jejJhQBwBmotGRES8QOGmMynLN+ekyXwZXHXmtkEXwel/gJjeHvmIovJq/vVdNv/8dhcFZdUA2AKtTDkhkRvGpTIgQbeeRETEuxRuOoODxfD1M/DtvENLIPQ+E854ABJP8MhH7Cys4B/LtvPO97vdswd3C7dz3egUrhqVTHQXu0c+R0RE5Jco3HRkLhd8+zezt6aq/tZQj5Fw5kPm7ScP+CG3hHlLt/HRhjwaJhAe2sPBjeNSOWdIArZAjXoSERHfUrjpyFa/CJ/+wXwdO9Dsqek3+bjraQzD4JttRcxbuo3lWwvd28/oH8v0U3txUs+umj1YRET8RuGmo6ooNEdCAZw2C075DVgDjustXS6DT7Pymbdkm7tIOMBq4fyhCdx6ai/V04iISJugcNNRffaQeSsqfuhxB5tap4v31uTy96Xb2L6vAgB7oJUrTkri5vFpJEWFeqrVIiIix03hpiPKWQVrXjNfn/vkMQeb6jonb2fuZt6Sbew+YBYiRwQHMm1sT6aN7UmMioRFRKQNUrjpaFxOWHyf+fqEqyFpZKvfoqrWycKV2fxj2XbySszZimO62LnllFSuGpVCF7v+sRERkbbL70NZ5s6dS2pqKsHBwaSnp7N8+fKjHv/6668zbNgwQkNDSUhI4IYbbqCoqMhHrW0HMl+CvHVgd8CZj7Tq1MqaOl5Yvp3xT3zJw//JIq+kirgIOw+dP5Cvfns6t5zSS8FGRETaPL/+Ui1atIgZM2Ywd+5cxo0bxz/+8Q8mT55MVlYWycnJTY7/6quvuO6663j66ac5//zzyc3NZfr06dx888289957fvgGbUxFIXxeX0R8xh+gS7cWnVZeXcc/v9nFC8u3U1RRA0D3yBCmn9aLy9J7EBx0fIXIIiIivmQxDMPw14ePGjWK4cOHM2/ePPe2AQMGMGXKFGbPnt3k+L/+9a/MmzePbdu2ubc999xzPPHEE+Tk5LToM0tLS3E4HJSUlBAR0cFG9/z7TljzT4gbArcsgYBfzq5b8su4bsF37C01ZxNOjgrljtN7cdGJPTRHjYiItBmt+f32269XTU0NmZmZTJw4sdH2iRMnsmLFimbPGTt2LLt372bx4sUYhsHevXt5++23Offcc4/4OdXV1ZSWljZ6dEi7V5vBBuDcv7Yo2KzfXczU+d+wt7Sa5KhQnrxsGF/cdypTT0pWsBERkXbLb79ghYWFOJ1O4uLiGm2Pi4sjPz+/2XPGjh3L66+/ztSpU7HZbMTHxxMZGclzzz13xM+ZPXs2DofD/UhKSvLo92gTXE74sL6IeNhVkDz6F0/5bnsRV/3fdxRX1nJCUiQf3DmOS9J7EBigUCMiIu2b33/Jfj6TrWEYR5zdNisri7vvvpsHH3yQzMxMPv74Y3bs2MH06dOP+P6zZs2ipKTE/Wjp7at2JfNlyFtrFhGf9ctFxEu2FDDtpZWUV9cxJi2a124eRWSozevNFBER8QW/FRTHxMQQEBDQpJemoKCgSW9Og9mzZzNu3Dh+85vfADB06FDCwsIYP348jz32GAkJCU3Osdvt2O0deD6WiiL4/FHz9Rn3Q5fYox7+0YY87l64hlqnwRn9Y5l79XAVDIuISIfit54bm81Geno6GRkZjbZnZGQwduzYZs+prKzEam3c5IAA84fZj3XR/vX5w1BVDHGDYcRNRz307czd3PGv76l1Gpw7NIG/X5OuYCMiIh2OX29LzZw5kxdeeIEFCxawadMm7r33XrKzs923mWbNmsV1113nPv7888/n3XffZd68eWzfvp2vv/6au+++m5EjR5KYmOivr+E/uzPh+/oi4nOOXkT86jc7+fVb63AZcPmIHjx7xYkqGhYRkQ7Jr/PcTJ06laKiIh599FHy8vIYPHgwixcvJiUlBYC8vDyys7Pdx19//fWUlZXx/PPPc9999xEZGckZZ5zBn//8Z399Bf9xz0RswLArIWXMEQ+du+Qnnvh4CwA3jOvJA+cOxGrVqt0iItIx+XWeG3/oMPPcrHkN/n0H2CPgrsxma20Mw+Avn2xh7hJzXqC7z+jNvWf1PWLBtoiISFvVmt9vzaXfHhkGfP2s+Xr8fUcsIl68Id8dbGZN7s+tp/byVQtFRET8RkUX7dFPn0PhFrCFw4gbmj2kps7FE59sBuCO03sp2IiISKehcNMeffs383n4tRDsaPaQN1Zms6uokpgudm4/rbcPGyciIuJfCjftzd4s2PYFWKww6tZmDymrquWZz7cCcO9ZfQjTSt4iItKJKNy0N9/ONZ/7nwddezZ7yPxl29lfUUNatzCmjuiAy02IiIgchcJNe1K+D9a/ab4ec0ezhxSUVvHC8h0A/M+k/lorSkREOh398rUnqxeAsxoSh0PSqGYPefqzrRysdZKe0pVJg5pfxkJERKQjU7hpL2qrYNX/ma/H3AHNzFXzU0EZi1aZkx7Omtxf89mIiEinpHDTXvzwNlTsg4juMPDCZg/588dbcBkwcWAcI3pG+biBIiIibYPCTXtgGPBNfSHxyFsgIKjJIat37icjay8BVgv/c3Z/HzdQRESk7VC4aQ92LIWCjRAUCunTmuw2DIM/Ld4EwOUjkugd28XXLRQREWkzFG7ag2/qJ+074WoI6dpk9ycb8/k+u5iQoADuPbOPjxsnIiLStijctHX7foStnwIWGH1bk921Tpd7xe+bx6cSGxHs4waKiIi0LQo3bd1388znfpMhuun6UItW5bC9sIKoMBu3nJLm48aJiIi0PQo3bVnlflj7hvl69O1NdldU1zHnM3OZhXsm9CE8uGmhsYiISGejcNOWrV4AdQchfij0PLnJ7heW76CwvJqU6FCuHJnshwaKiIi0PQo3bVVdDaw88qR9+8qq+ceybQD8ZlI/bIH6n1JERAQUbtquje9CeT50iYdBFzfZ/cqKnVTWOBnWw8G5QxL80EAREZG2SeGmLTKMQ8O/R94MgbZGu10ug/fW5AJw8/g0LbMgIiJyGIWbtmjX15C/HgJDIP3GJrtX7zpAbvFButgDOWugFscUERE5nMJNW7TqBfN52BUQFt1kd0OvzeTB8QQHBfiyZSIiIm1eq8NNz549efTRR8nOzvZGe6SuGrZmmK9PvLbJ7uo6Jx+u3wPARSd292XLRERE2oVWh5v77ruPf//736SlpXHWWWexcOFCqqurvdG2zmnnV1BTDl3iIPHEJru/3LyP0qo64iOCGZXWtFdHRESks2t1uLnrrrvIzMwkMzOTgQMHcvfdd5OQkMCdd97J999/7402di4/fmw+950E1qb/87xff0vqwhMTCbCqkFhEROTnjrnmZtiwYTzzzDPk5uby0EMP8cILL3DSSScxbNgwFixYgGEYnmxn52AYsKUh3ExusrukspYvNhcAuiUlIiJyJIHHemJtbS3vvfceL730EhkZGYwePZqbbrqJPXv2cP/99/PZZ5/xr3/9y5Nt7fgKsqAkGwKDIe20Jrs/3JBHjdNF//hw+sdH+L59IiIi7UCrw83333/PSy+9xBtvvEFAQADXXnstTz/9NP3793cfM3HiRE455RSPNrRT2PKR+Zx6KthCm+xuuCWlXhsREZEja3W4OemkkzjrrLOYN28eU6ZMISio6WKNAwcO5IorrvBIAzuVhnDT7+wmu3L2V7Jy534sFrjghEQfN0xERKT9aHW42b59OykpKUc9JiwsjJdeeumYG9UplRdAbqb5um/TcPPBOnP495i0aBIcIb5smYiISLvS6oLigoICvvvuuybbv/vuO1avXu2RRnVKP34CGJBwAkQ07pkxDIN3v98NwBTdkhIRETmqVoebO+64g5ycnCbbc3NzueOOOzzSqE6pYQh4v6ajpDbuKWXbvgrsgVbOHhzv44aJiIi0L60ON1lZWQwfPrzJ9hNPPJGsrCyPNKrTqa2CbV+Yr5u5JdWw3MKZA+OICG5a4yQiIiKHtDrc2O129u7d22R7Xl4egYHHPLK8c9u5HGorITwREoY12lXndLnrbS46QbekREREfkmrw81ZZ53FrFmzKCkpcW8rLi7m97//PWeddZZHG9dpbFlsPvc7GyyNZx1esa2IfWXVdA0N4pS+3fzQOBERkfal1V0tTz75JKeccgopKSmceKK59tHatWuJi4vjn//8p8cb2OEZRn0xMc3OStwwt815QxOxBWoRdxERkV/S6nDTvXt31q9fz+uvv866desICQnhhhtu4Morr2x2zhv5BfnroTQXgkIhtfHEh5U1dXy8MR/QKCkREZGWOqYimbCwMG655RZPt6VzalhLKu10CAputCsjay+VNU6So0IZnhzp+7aJiIi0Q8dcAZyVlUV2djY1NTWNtl9wwQXH3ahO5ccjz0rcMEpqyondsVi0AriIiEhLHNMMxRdddBEbNmzAYrG4V/9u+PF1Op2ebWFHVpoHe9aYr/tMarRrX1k1y7cWAjBFyy2IiIi0WKsrVO+55x5SU1PZu3cvoaGhbNy4kWXLljFixAiWLFnihSZ2YA0T93UfAeFxjXb9d/0enC6DYUmRpHXr4ofGiYiItE+t7rn55ptv+OKLL+jWrRtWqxWr1crJJ5/M7Nmzufvuu1mzZo032tkxuWclbnpLyr0CuHptREREWqXVPTdOp5MuXcyehJiYGPbsMSeYS0lJYcuWLZ5tXUdWUwnbl5ivfzYEfNu+ctbtLiHAauG8YQo3IiIirdHqnpvBgwezfv160tLSGDVqFE888QQ2m4358+eTlpbmjTZ2TDuWQl0VOJIgblCjXf+u77U5pU8MMV3s/midiIhIu9XqcPOHP/yBiooKAB577DHOO+88xo8fT3R0NIsWLfJ4AzusLfWjpPo2nZV4WX0h8blD1WsjIiLSWq0ON5MmHRrVk5aWRlZWFvv376dr164artxSLtehWYl/Vm9TXecka08pACf17OrrlomIiLR7raq5qaurIzAwkB9++KHR9qioKAWb1shbA+X5YOsCPcc32rUpr4wap4uoMBvJUaF+aqCIiEj71apwExgYSEpKikfnspk7dy6pqakEBweTnp7O8uXLj3js9ddfj8ViafIYNGjQEc9pkxpmJe51BgQ2rqlZm30AgGE9HAqMIiIix6DVo6X+8Ic/MGvWLPbv33/cH75o0SJmzJjB/fffz5o1axg/fjyTJ08mOzu72eOfeeYZ8vLy3I+cnByioqK47LLLjrstPuWelbjpQplrc4oBOCFJt6RERESOhcVomGK4hU488UR++uknamtrSUlJISwsrNH+77//vsXvNWrUKIYPH868efPc2wYMGMCUKVOYPXv2L57//vvvc/HFF7Njxw5SUlKaPaa6uprq6mr336WlpSQlJVFSUkJERESL2+oxJbvh6UGABX7zE4TFNNp92l++ZGdRJa/cOJJT+3bzfftERETaoNLSUhwOR4t+v1tdUDxlypRjbVcjNTU1ZGZm8rvf/a7R9okTJ7JixYoWvceLL77ImWeeecRgAzB79mweeeSR42qrRzVM3Jc0skmwOVBRw86iSgBO6BHp44aJiIh0DK0ONw899JBHPriwsBCn00lcXONlB+Li4sjPz//F8/Py8vjoo4/417/+ddTjZs2axcyZM91/N/Tc+E1DvU1zt6R2FwOQFhOGIzTIh40SERHpOI55VXBP+XnRrGEYLSqkffnll4mMjPzFniS73Y7d3kYmwjMMyPnOfN37zCa712YXA3BCUqTv2iQiItLBtDrcWK3Wo4aPlo6kiomJISAgoEkvTUFBQZPenJ8zDIMFCxZw7bXXYrPZWvR5bUL5XqguBYsVYvo22e0uJk6O9G27REREOpBWh5v33nuv0d+1tbWsWbOGV155pVW1LTabjfT0dDIyMrjooovc2zMyMrjwwguPeu7SpUv56aefuOmmm1rXeH8r/NF87tqzyRBwwzBYV39bSj03IiIix67V4aa54HHppZcyaNAgFi1a1KrAMXPmTK699lpGjBjBmDFjmD9/PtnZ2UyfPh0w62Vyc3N59dVXG5334osvMmrUKAYPHtza5vtX4VbzObpPk107iyoprqzFFmilf7wfRnGJiIh0EB6ruRk1ahS/+tWvWnXO1KlTKSoq4tFHHyUvL4/BgwezePFi9+invLy8JnPelJSU8M477/DMM894qum+0xBuYpqGm7U55uR9gxMjsAW2evohERERqeeRcHPw4EGee+45evTo0epzb7/9dm6//fZm97388stNtjkcDiorK1v9OW1C0VHCjbuYWJP3iYiIHI9Wh5ufL5BpGAZlZWWEhoby2muvebRxHU5DzY2KiUVERLym1eHm6aefbhRurFYr3bp1Y9SoUXTtql6HI6o9CMU55uuf1dxU1TrJyjNXAj9RxcQiIiLHpdXh5vrrr/dCMzqBom2AAcGRTWYmzsorpdZpEB1mo0fXEL80T0REpKNodeXqSy+9xFtvvdVk+1tvvcUrr7zikUZ1SO5bUn3gZ/MEHT55n1YCFxEROT6tDjePP/44MTExTbbHxsbypz/9ySON6pCKfjKfj1Zvo1tSIiIix63V4WbXrl2kpqY22Z6SktJk2LYc5vCem59RMbGIiIjntDrcxMbGsn79+ibb161bR3R0tEca1SEdYQK//RU1ZO83h7YP1UrgIiIix63V4eaKK67g7rvv5ssvv8TpdOJ0Ovniiy+45557uOKKK7zRxvbPMI54W2pdfa9Nr25hOEK0EriIiMjxavVoqccee4xdu3YxYcIEAgPN010uF9ddd51qbo6kLA9qysESYK4rdZg17nobDaMXERHxhFaHG5vNxqJFi3jsscdYu3YtISEhDBkyxL1kgjSjod4mKhUCG69ifqiY2OHjRomIiHRMx7z8Qp8+fejTp2lxrDTjCPU2hmG4b0up50ZERMQzWl1zc+mll/L444832f6Xv/yFyy67zCON6nCOsGDmjsIKSg7WYg+00j8h3A8NExER6XhaHW6WLl3Kueee22T72WefzbJlyzzSqA7nCMPAG25JDe7uIChAK4GLiIh4Qqt/UcvLy7HZbE22BwUFUVpa6pFGdThHGCmlyftEREQ8r9XhZvDgwSxatKjJ9oULFzJw4ECPNKpDqamAkuYXzFS4ERER8bxWFxQ/8MADXHLJJWzbto0zzjgDgM8//5x//etfvP322x5vYLtXtM18DomCsEOTHFbVOtlUvxK4wo2IiIjntDrcXHDBBbz//vv86U9/4u233yYkJIRhw4bxxRdfEBER4Y02tm9HqLfZuMdcCTymi1YCFxER8aRjGgp+7rnnuouKi4uLef3115kxYwbr1q3D6XR6tIHtnrve5si3pLQSuIiIiOcc8xCdL774gmuuuYbExESef/55zjnnHFavXu3JtnUM7p4bFROLiIj4Qqt6bnbv3s3LL7/MggULqKio4PLLL6e2tpZ33nlHxcRHcoQJ/NbmHAA0eZ+IiIintbjn5pxzzmHgwIFkZWXx3HPPsWfPHp577jlvtq39c7maHQZeVF5Nzv6DWCwwVMsuiIiIeFSLe24+/fRT7r77bm677TYtu9BSpblQWwnWQOh6aO2tte6VwLsQEayVwEVERDypxT03y5cvp6ysjBEjRjBq1Cief/559u3b5822tX9F9bekotIg4FCIUb2NiIiI97Q43IwZM4b/+7//Iy8vj1tvvZWFCxfSvXt3XC4XGRkZlJWVebOd7dMR622KAYUbERERb2j1aKnQ0FBuvPFGvvrqKzZs2MB9993H448/TmxsLBdccIE32th+NbNgpstlKNyIiIh40XGt1tivXz+eeOIJdu/ezRtvvOGpNnUczUzgt72wgrKqOoKDrPSP10rgIiIinuaRpagDAgKYMmUKH3zwgSferuNoZqRUQ6/NkO4OArUSuIiIiMfp19VbqsvN0VIA0b3dmw/NbxPph0aJiIh0fAo33tLQaxMaA6FR7s3rckoATd4nIiLiLQo33uIuJm687ELOgUoA+sZ18XWLREREOgWFG29xFxMfuiXlchmUHKwFIDLU5o9WiYiIdHgKN95S1LTnpqyqDsMwXztCNDOxiIiINyjceEszE/gVH6wBIMwWgC1Ql15ERMQb9AvrDY0WzDws3FTqlpSIiIi3Kdx4Q0kO1FWBNQgiDy2YWVxfb6NbUiIiIt6jcOMNDfU20b0g4NDC68WV5m2pyFCFGxEREW9RuPEGd71N70abD42UUrgRERHxFoUbbzjCHDcNNTeOENXciIiIeIvCjTc0s2AmHF5QrJ4bERERb1G48YYj9dzUDwWPVEGxiIiI1yjceFpVKZTnm69/XnOjnhsRERGvU7jxtIaRUmGxEBLZaNehoeCquREREfEWhRtPK2yYvK9vk10aCi4iIuJ9Cjee1syCmQ00FFxERMT7FG48rZkFMwEMwzg0Wkq3pURERLzG7+Fm7ty5pKamEhwcTHp6OsuXLz/q8dXV1dx///2kpKRgt9vp1asXCxYs8FFrW6CZBTMBKmqc1LnMJcHVcyMiIuI9gb98iPcsWrSIGTNmMHfuXMaNG8c//vEPJk+eTFZWFsnJyc2ec/nll7N3715efPFFevfuTUFBAXV1dT5u+RG4nFC0zXzdZI4bs97GHmglOCjA1y0TERHpNPwabp566iluuukmbr75ZgDmzJnDJ598wrx585g9e3aT4z/++GOWLl3K9u3biYqKAqBnz55H/Yzq6mqqq6vdf5eWlnruC/xccTY4qyHADpGNw5km8BMREfENv92WqqmpITMzk4kTJzbaPnHiRFasWNHsOR988AEjRozgiSeeoHv37vTt25df//rXHDx48IifM3v2bBwOh/uRlJTk0e/RSOFhC2ZaG/fOuIuJVW8jIiLiVX7ruSksLMTpdBIXF9doe1xcHPn5+c2es337dr766iuCg4N57733KCws5Pbbb2f//v1HrLuZNWsWM2fOdP9dWlrqvYBT1PyCmXDYulLquREREfEqv96WArBYLI3+NgyjybYGLpcLi8XC66+/jsPhAMxbW5deeil/+9vfCAkJaXKO3W7Hbrd7vuHNcQ8Db2aOGy29ICIi4hN+uy0VExNDQEBAk16agoKCJr05DRISEujevbs72AAMGDAAwzDYvXu3V9vbIu4J/Po02aWaGxEREd/wW7ix2Wykp6eTkZHRaHtGRgZjx45t9pxx48axZ88eysvL3dt+/PFHrFYrPXr08Gp7W+QIq4HD4RP4qeZGRETEm/w6z83MmTN54YUXWLBgAZs2beLee+8lOzub6dOnA2a9zHXXXec+/qqrriI6OpobbriBrKwsli1bxm9+8xtuvPHGZm9J+dTBYqgoMF9HN9dzY96Wcui2lIiIiFf5teZm6tSpFBUV8eijj5KXl8fgwYNZvHgxKSkpAOTl5ZGdne0+vkuXLmRkZHDXXXcxYsQIoqOjufzyy3nsscf89RUOObgf4odATQUERzTZrdtSIiIivmExDMPwdyN8qbS0FIfDQUlJCRERTUPIcTMMaKYg+vJ/fMPKHfv521XDOXdoguc/V0REpANrze+335df6HCOMNKrRD03IiIiPqFw4yMNQ8FVcyMiIuJdCjc+opobERER31C48YGqWifVdS5AQ8FFRES8TeHGBxp6bQKtFsJsWhFcRETEmxRufMC99EJo0BGXlhARERHPULjxAfeimSomFhER8TqFGx84VEysehsRERFvU7jxgRKtCC4iIuIzCjc+4L4tpWHgIiIiXqdw4wPFDSuCh+i2lIiIiLcp3PiAJvATERHxHYUbHyg5bCi4iIiIeJfCjQ9oKLiIiIjvKNz4gIaCi4iI+I7CjQ+UuAuK1XMjIiLibQo3PlBcqZobERERX1G48bKaOhcVNU5AQ8FFRER8QeHGyxpuSVksEB4c6OfWiIiIdHwKN17WMAzcERKE1aoVwUVERLxN4cbL3COlVEwsIiLiEwo3XnZoXSnV24iIiPiCwo2XFWsYuIiIiE8p3HiZhoGLiIj4lsKNl2kCPxEREd9SuPEy1dyIiIj4lsKNl6nmRkRExLcUbrxMNTciIiK+pXDjZe6aG4UbERERn1C48TJ3zY3WlRIREfEJhRsv020pERER31K48SKny6C0qg5QQbGIiIivKNx4UWl9vQ2YC2eKiIiI9ynceFHDMPBweyCBAbrUIiIivqBfXC9qqLdxqN5GRETEZxRuvKhYw8BFRER8TuHGi0oqG2Yn1jBwERERX1G48SLdlhIREfE9hRsv0rpSIiIivqdw40UNsxOr5kZERMR3FG68yL2ulGpuREREfEbhxotUcyMiIuJ7CjdepJobERER31O48SL3UPBQ3ZYSERHxFYUbL9IkfiIiIr7n93Azd+5cUlNTCQ4OJj09neXLlx/x2CVLlmCxWJo8Nm/e7MMWt4zLZbhrbnRbSkRExHf8Gm4WLVrEjBkzuP/++1mzZg3jx49n8uTJZGdnH/W8LVu2kJeX53706dPHRy1uufKaOlyG+TpC4UZERMRn/BpunnrqKW666SZuvvlmBgwYwJw5c0hKSmLevHlHPS82Npb4+Hj3IyAgwEctbrmGepuQoACCg9pe+0RERDoqv4WbmpoaMjMzmThxYqPtEydOZMWKFUc998QTTyQhIYEJEybw5ZdfHvXY6upqSktLGz18QRP4iYiI+Iffwk1hYSFOp5O4uLhG2+Pi4sjPz2/2nISEBObPn88777zDu+++S79+/ZgwYQLLli074ufMnj0bh8PhfiQlJXn0exxJ8cH6OW50S0pERMSnAv3dAIvF0uhvwzCabGvQr18/+vXr5/57zJgx5OTk8Ne//pVTTjml2XNmzZrFzJkz3X+Xlpb6JOCo50ZERMQ//NZzExMTQ0BAQJNemoKCgia9OUczevRotm7desT9drudiIiIRg9fKNbSCyIiIn7ht3Bjs9lIT08nIyOj0faMjAzGjh3b4vdZs2YNCQkJnm7ecStpGAaunhsRERGf8uttqZkzZ3LttdcyYsQIxowZw/z588nOzmb69OmAeUspNzeXV199FYA5c+bQs2dPBg0aRE1NDa+99hrvvPMO77zzjj+/RrMabktpXSkRERHf8mu4mTp1KkVFRTz66KPk5eUxePBgFi9eTEpKCgB5eXmN5rypqanh17/+Nbm5uYSEhDBo0CA+/PBDzjnnHH99hSPSbSkRERH/sBiGYfi7Eb5UWlqKw+GgpKTEq/U3N7+yms827WX2xUO4cmSy1z5HRESkM2jN77ffl1/oqEoOaukFERERf1C48RLV3IiIiPiHwo2XqOZGRETEPxRuvMAwDPfaUhoKLiIi4lsKN15wsNZJjdMFKNyIiIj4msKNFzTU29gCrIRoRXARERGfUrjxgsOLiY+0TpaIiIh4h8KNFxRrGLiIiIjfKNx4gYqJRURE/EfhxgsahoE7NAxcRETE5xRuvKBYPTciIiJ+49eFMzsq1dyIiHROTqeT2tpafzej3bLZbFitx9/vonDjBaq5ERHpXAzDID8/n+LiYn83pV2zWq2kpqZisx1fWYfCjRccGgqumhsRkc6gIdjExsYSGhqqaUCOgcvlYs+ePeTl5ZGcnHxc11Dhxgt0W0pEpPNwOp3uYBMdHe3v5rRr3bp1Y8+ePdTV1REUdOy/oSoo9gIVFIuIdB4NNTahoaF+bkn713A7yul0Htf7KNx4QYlWBBcR6XR0K+r4eeoaKtx4gXpuRERE/EfhxsOqap0crDW70xwKNyIi0omcdtppzJgxw9/NUEGxp5XW35IKsFoIt+vyiohI2/NLt3+mTZvGyy+/3Or3fffdd4+rENhT9OvrYYeWXtCK4CIi0jbl5eW5Xy9atIgHH3yQLVu2uLeFhIQ0Or62trZFoSUqKspzjTwOui3lYe56Gw0DFxHplAzDoLKmzi8PwzBa1Mb4+Hj3w+FwYLFY3H9XVVURGRnJm2++yWmnnUZwcDCvvfYaRUVFXHnllfTo0YPQ0FCGDBnCG2+80eh9f35bqmfPnvzpT3/ixhtvJDw8nOTkZObPn+/Jy90s9dx4WHGlOceN6m1ERDqng7VOBj74iV8+O+vRSYTaPPPT/tvf/pYnn3ySl156CbvdTlVVFenp6fz2t78lIiKCDz/8kGuvvZa0tDRGjRp1xPd58skn+eMf/8jvf/973n77bW677TZOOeUU+vfv75F2NkfhxsOKD6rnRkRE2r8ZM2Zw8cUXN9r261//2v36rrvu4uOPP+att946arg555xzuP322wEzMD399NMsWbJE4aY9ObSulOa4ERHpjEKCAsh6dJLfPttTRowY0ehvp9PJ448/zqJFi8jNzaW6uprq6mrCwsKO+j5Dhw51v264/VVQUOCxdjZH4cbDGpZecKjnRkSkU7JYLB67NeRPPw8tTz75JE8//TRz5sxhyJAhhIWFMWPGDGpqao76Pj8vRLZYLLhcLo+393Dt/+q3MZrAT0REOqLly5dz4YUXcs011wDmQpdbt25lwIABfm5ZUxot5WGquRERkY6od+/eZGRksGLFCjZt2sStt95Kfn6+v5vVLIUbD1PNjYiIdEQPPPAAw4cPZ9KkSZx22mnEx8czZcoUfzerWbot5WHumhvdlhIRkXbg+uuv5/rrr3f/3bNnz2bny4mKiuL9998/6nstWbKk0d87d+5scszatWtb38hWUs+Nh2kSPxEREf9SuPEw3ZYSERHxL4UbD6p1uiirrgPUcyMiIuIvCjce1LAiOECEwo2IiIhfKNx4UMMw8IjgQAKsWhFcRETEHxRuPKhY9TYiIiJ+p3DjQSX1w8A1O7GIiIj/KNx4UEPPjdaVEhER8R+FGw/SbSkRERH/U7jxoOLK+ttS6rkRERHxG4UbD3IvmqmaGxERacMsFstRH4cvx9BaPXv2ZM6cOR5r67HQ2lIepJobERFpD/Ly8tyvFy1axIMPPsiWLVvc20JCQvzRLI9Rz40HHeq5Uc2NiEinZRhQU+GfRzMLXjYnPj7e/XA4HFgslkbbli1bRnp6OsHBwaSlpfHII49QV1fnPv/hhx8mOTkZu91OYmIid999NwCnnXYau3bt4t5773X3AvmDem48qEQ1NyIiUlsJf0r0z2f/fg/Ywo7rLT755BOuueYann32WcaPH8+2bdu45ZZbAHjooYd4++23efrpp1m4cCGDBg0iPz+fdevWAfDuu+8ybNgwbrnlFn71q18d99c5Vgo3HqSaGxERae/+93//l9/97ndMmzYNgLS0NP74xz/yP//zPzz00ENkZ2cTHx/PmWeeSVBQEMnJyYwcORKAqKgoAgICCA8PJz4+3m/fwe/hZu7cufzlL38hLy+PQYMGMWfOHMaPH/+L53399deceuqpDB48mLVr13q/oS1waCi4wo2ISKcVFGr2oPjrs49TZmYmq1at4n//93/d25xOJ1VVVVRWVnLZZZcxZ84c0tLSOPvssznnnHM4//zzCQz0e6Rw82tLFi1axIwZM5g7dy7jxo3jH//4B5MnTyYrK4vk5OQjnldSUsJ1113HhAkT2Lt3rw9bfGROl0FpVUNBsWpuREQ6LYvluG8N+ZPL5eKRRx7h4osvbrIvODiYpKQktmzZQkZGBp999hm33347f/nLX1i6dClBQW3j/9z7taD4qaee4qabbuLmm29mwIABzJkzh6SkJObNm3fU82699VauuuoqxowZ46OW/rKyqlp3HZdGS4mISHs1fPhwtmzZQu/evZs8rFYzNoSEhHDBBRfw7LPPsmTJEr755hs2bNgAgM1mw+l0+vMr+K/npqamhszMTH73u9812j5x4kRWrFhxxPNeeukltm3bxmuvvcZjjz32i59TXV1NdXW1++/S0tJjb/RRlFfXEREciMsAW6AGoYmISPv04IMPct5555GUlMRll12G1Wpl/fr1bNiwgccee4yXX34Zp9PJqFGjCA0N5Z///CchISGkpKQA5jw3y5Yt44orrsButxMTE+Pz7+C3X+HCwkKcTidxcXGNtsfFxZGfn9/sOVu3buV3v/sdr7/+eovv7c2ePRuHw+F+JCUlHXfbm9OjayjrH57EuocmeuX9RUREfGHSpEn897//JSMjg5NOOonRo0fz1FNPucNLZGQk//d//8e4ceMYOnQon3/+Of/5z3+Ijo4G4NFHH2Xnzp306tWLbt26+eU7+L365+dj4A3DaHZcvNPp5KqrruKRRx6hb9++LX7/WbNmMXPmTPffpaWlXgs4AAFW/4zpFxERORbXX399kxmJJ02axKRJk5o9fsqUKUyZMuWI7zd69Gj30HB/8Vu4iYmJISAgoEkvTUFBQZPeHICysjJWr17NmjVruPPOOwGz6MkwDAIDA/n0008544wzmpxnt9ux2+3e+RIiIiLS5vjttpTNZiM9PZ2MjIxG2zMyMhg7dmyT4yMiItiwYQNr1651P6ZPn06/fv1Yu3Yto0aN8lXTRUREpA3z622pmTNncu211zJixAjGjBnD/Pnzyc7OZvr06YB5Syk3N5dXX30Vq9XK4MGDG50fGxtLcHBwk+0iIiLSefk13EydOpWioiIeffRR8vLyGDx4MIsXL3YXLeXl5ZGdne3PJoqIiEg7YzGMFq6y1UGUlpbicDgoKSkhIiLC380REZF2rqqqih07dtCzZ892v5q2vx08eJCdO3eSmppKcHBwo32t+f3WhCwiIiLHoWFW3srKSj+3pP2rqTEXoA4ICDiu9/H7UHAREZH2LCAggMjISAoKCgAIDQ1tdkoTOTqXy8W+ffsIDQ097nWqFG5ERESOU8MK2A0BR46N1WolOTn5uMOhwo2IiMhxslgsJCQkEBsbS21trb+b027ZbDb3+lXHQ+FGRETEQwICAo67XkSOnwqKRUREpENRuBEREZEOReFGREREOpROV3PTMGdhaWmpn1siIiIiLdXwu92SuYc7XbgpKysDICkpyc8tERERkdYqKyvD4XAc9ZhOt/yCy+Viz549hIeHe3ySpdLSUpKSksjJydHSDj6g6+1but6+pevtW7revnUs19swDMrKykhMTPzF4eKdrufGarXSo0cPr35GRESE/uXwIV1v39L19i1db9/S9fat1l7vX+qxaaCCYhEREelQFG5ERESkQ1G48SC73c5DDz2E3W73d1M6BV1v39L19i1db9/S9fYtb1/vTldQLCIiIh2bem5ERESkQ1G4ERERkQ5F4UZEREQ6FIUbERER6VAUbjxk7ty5pKamEhwcTHp6OsuXL/d3kzqMZcuWcf7555OYmIjFYuH9999vtN8wDB5++GESExMJCQnhtNNOY+PGjf5pbDs3e/ZsTjrpJMLDw4mNjWXKlCls2bKl0TG63p4zb948hg4d6p7IbMyYMXz00Ufu/brW3jV79mwsFgszZsxwb9M195yHH34Yi8XS6BEfH+/e781rrXDjAYsWLWLGjBncf//9rFmzhvHjxzN58mSys7P93bQOoaKigmHDhvH88883u/+JJ57gqaee4vnnn2fVqlXEx8dz1llnudcRk5ZbunQpd9xxB99++y0ZGRnU1dUxceJEKioq3MfoentOjx49ePzxx1m9ejWrV6/mjDPO4MILL3T/B17X2ntWrVrF/PnzGTp0aKPtuuaeNWjQIPLy8tyPDRs2uPd59VobctxGjhxpTJ8+vdG2/v37G7/73e/81KKOCzDee+89998ul8uIj483Hn/8cfe2qqoqw+FwGH//+9/90MKOpaCgwACMpUuXGoah6+0LXbt2NV544QVday8qKysz+vTpY2RkZBinnnqqcc899xiGoX++Pe2hhx4yhg0b1uw+b19r9dwcp5qaGjIzM5k4cWKj7RMnTmTFihV+alXnsWPHDvLz8xtdf7vdzqmnnqrr7wElJSUAREVFAbre3uR0Olm4cCEVFRWMGTNG19qL7rjjDs4991zOPPPMRtt1zT1v69atJCYmkpqayhVXXMH27dsB71/rTrdwpqcVFhbidDqJi4trtD0uLo78/Hw/tarzaLjGzV3/Xbt2+aNJHYZhGMycOZOTTz6ZwYMHA7re3rBhwwbGjBlDVVUVXbp04b333mPgwIHu/8DrWnvWwoUL+f7771m1alWTffrn27NGjRrFq6++St++fdm7dy+PPfYYY8eOZePGjV6/1go3HmKxWBr9bRhGk23iPbr+nnfnnXeyfv16vvrqqyb7dL09p1+/fqxdu5bi4mLeeecdpk2bxtKlS937da09Jycnh3vuuYdPP/2U4ODgIx6na+4ZkydPdr8eMmQIY8aMoVevXrzyyiuMHj0a8N611m2p4xQTE0NAQECTXpqCgoImiVQ8r6HyXtffs+666y4++OADvvzyS3r06OHeruvteTabjd69ezNixAhmz57NsGHDeOaZZ3StvSAzM5OCggLS09MJDAwkMDCQpUuX8uyzzxIYGOi+rrrm3hEWFsaQIUPYunWr1//5Vrg5TjabjfT0dDIyMhptz8jIYOzYsX5qVeeRmppKfHx8o+tfU1PD0qVLdf2PgWEY3Hnnnbz77rt88cUXpKamNtqv6+19hmFQXV2ta+0FEyZMYMOGDaxdu9b9GDFiBFdffTVr164lLS1N19yLqqur2bRpEwkJCd7/5/u4S5LFWLhwoREUFGS8+OKLRlZWljFjxgwjLCzM2Llzp7+b1iGUlZUZa9asMdasWWMAxlNPPWWsWbPG2LVrl2EYhvH4448bDofDePfdd40NGzYYV155pZGQkGCUlpb6ueXtz2233WY4HA5jyZIlRl5envtRWVnpPkbX23NmzZplLFu2zNixY4exfv164/e//71htVqNTz/91DAMXWtfOHy0lGHomnvSfffdZyxZssTYvn278e233xrnnXeeER4e7v5t9Oa1VrjxkL/97W9GSkqKYbPZjOHDh7uHzsrx+/LLLw2gyWPatGmGYZhDCh966CEjPj7esNvtximnnGJs2LDBv41up5q7zoDx0ksvuY/R9facG2+80f3fjW7duhkTJkxwBxvD0LX2hZ+HG11zz5k6daqRkJBgBAUFGYmJicbFF19sbNy40b3fm9faYhiGcfz9PyIiIiJtg2puREREpENRuBEREZEOReFGREREOhSFGxEREelQFG5ERESkQ1G4ERERkQ5F4UZEREQ6FIUbERER6VAUbkSkU7JYLLz//vv+boaIeIHCjYj43PXXX4/FYmnyOPvss/3dNBHpAAL93QAR6ZzOPvtsXnrppUbb7Ha7n1ojIh2Jem5ExC/sdjvx8fGNHl27dgXMW0bz5s1j8uTJhISEkJqayltvvdXo/A0bNnDGGWcQEhJCdHQ0t9xyC+Xl5Y2OWbBgAYMGDcJut5OQkMCdd97ZaH9hYSEXXXQRoaGh9OnThw8++MC978CBA1x99dV069aNkJAQ+vTp0ySMiUjbpHAjIm3SAw88wCWXXMK6deu45ppruPLKK9m0aRMAlZWVnH322XTt2pVVq1bx1ltv8dlnnzUKL/PmzeOOO+7glltuYcOGDXzwwQf07t270Wc88sgjXH755axfv55zzjmHq6++mv3797s/Pysri48++ohNmzYxb948YmJifHcBROTYeWRtcRGRVpg2bZoREBBghIWFNXo8+uijhmEYBmBMnz690TmjRo0ybrvtNsMwDGP+/PlG165djfLycvf+Dz/80LBarUZ+fr5hGIaRmJho3H///UdsA2D84Q9/cP9dXl5uWCwW46OPPjIMwzDOP/9844YbbvDMFxYRn1LNjYj4xemnn868efMabYuKinK/HjNmTKN9Y8aMYe3atQBs2rSJYcOGERYW5t4/btw4XC4XW7ZswWKxsGfPHiZMmHDUNgwdOtT9OiwsjPDwcAoKCgC47bbbuOSSS/j++++ZOHEiU6ZMYezYscf0XUXEtxRuRMQvwsLCmtwm+iUWiwUAwzDcr5s7JiQkpEXvFxQU1ORcl8sFwOTJk9m1axcffvghn332GRMmTOCOO+7gr3/9a6vaLCK+p5obEWmTvv322yZ/9+/fH4CBAweydu1aKioq3Pu//vprrFYrffv2JTw8nJ49e/L5558fVxu6devG9ddfz2uvvcacOXOYP3/+cb2fiPiGem5ExC+qq6vJz89vtC0wMNBdtPvWW28xYsQITj75ZF5//XVWrlzJiy++CMDVV1/NQw89xLRp03j44YfZt28fd911F9deey1xcXEAPPzww0yfPp3Y2FgmT55MWVkZX3/9NXfddVeL2vfggw+Snp7OoEGDqK6u5r///S8DBgzw4BUQEW9RuBERv/j4449JSEhotK1fv35s3rwZMEcyLVy4kNtvv534+Hhef/11Bg4cCEBoaCiffPIJ99xzDyeddBKhoaFccsklPPXUU+73mjZtGlVVVTz99NP8+te/JiYmhksvvbTF7bPZbMyaNYudO3cSEhLC+PHjWbhwoQe+uYh4m8UwDMPfjRAROZzFYuG9995jypQp/m6KiLRDqrkRERGRDkXhRkRERDoU1dyISJuju+UicjzUcyMiIiIdisKNiIiIdCgKNyIiItKhKNyIiIhIh6JwIyIiIh2Kwo2IiIh0KAo3IiIi0qEo3IiIiEiH8v89TWNOAD2HnAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 14.581597222222216, 'Predicted Class')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##############################################################################\n",
    "import logging\n",
    "logging.getLogger(\"tensorflow\").disabled = True\n",
    "import numpy as np\n",
    "import math\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score\n",
    "\n",
    "import tensorflow\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dropout, Flatten, Dense\n",
    "from tensorflow.keras.layers import BatchNormalization as BatchNorm\n",
    "\n",
    "# pre-trained models\n",
    "from tensorflow.keras.applications.vgg16 import VGG16\n",
    "from tensorflow.keras.applications import ResNet50, InceptionV3, Xception, DenseNet121\n",
    "\n",
    "# custom learning rates\n",
    "import learning_rate as lr\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "##############################################################################\n",
    "# please change following for your dataset (dataset details and learning params)\n",
    "# dataset details\n",
    "train_data_dir = '/home/engs2426/Downloads/HCR-Net-main/Data/Offline/UCI_DevanagariHandwrittenCharacterDataset/digits/Train'\n",
    "validation_data_dir = '/home/engs2426/Downloads/HCR-Net-main/Data/Offline/UCI_DevanagariHandwrittenCharacterDataset/digits/Test'\n",
    "nb_train_samples = 17000\n",
    "nb_validation_samples = 3000\n",
    "num_classes = 10\n",
    "\n",
    "# learning params\n",
    "epochs1 = 30\n",
    "epochs2 = 20\n",
    "learning_rate1 = lr.decayed_learning_rate\n",
    "learning_rate2 = lr.decayed_learning_rate_tuned20\n",
    "\n",
    "# set seeds for reproduceability\n",
    "np.random.seed(5)\n",
    "tensorflow.random.set_seed(5)\n",
    "##############################################################################\n",
    "batch_size = 32\n",
    "# dimensions of our images.\n",
    "img_width, img_height = 32, 32\n",
    "input_shape = (img_width, img_height, 3)\n",
    "##############################################################################\n",
    "#### TRAINING PHASE - I #########\n",
    "#################################\n",
    "base_model = ResNet50(include_top=False, input_shape=input_shape)\n",
    "base_model.trainable = False\n",
    "inputs = tensorflow.keras.Input(shape=input_shape)\n",
    "x = base_model(inputs, training=False)\n",
    "x = tensorflow.keras.layers.GlobalAveragePooling2D()(x)\n",
    "outputs = tensorflow.keras.layers.Dense(num_classes, activation='softmax')(x)\n",
    "model = tensorflow.keras.Model(inputs, outputs)\n",
    "model.compile(loss='categorical_crossentropy', optimizer=tensorflow.keras.optimizers.RMSprop(), metrics=['accuracy'])\n",
    "print(model.summary())\n",
    "\n",
    "# using ImageDataGenerator to load and scale data\n",
    "train_datagen = ImageDataGenerator(rescale=1.0/255)\n",
    "val_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "                    train_data_dir,\n",
    "                    target_size=(img_width, img_height),\n",
    "                    batch_size=batch_size,\n",
    "                    color_mode=\"rgb\",\n",
    "                    class_mode='categorical',\n",
    "                    shuffle=True,\n",
    "                    seed=13)\n",
    "validation_generator = val_datagen.flow_from_directory(\n",
    "                    validation_data_dir,\n",
    "                    target_size=(img_width, img_height),\n",
    "                    batch_size=nb_validation_samples,\n",
    "                    color_mode=\"rgb\",\n",
    "                    shuffle=False,\n",
    "                    class_mode='categorical')\n",
    "\n",
    "# define callback for custom learning rate\n",
    "callback = tensorflow.keras.callbacks.LearningRateScheduler(learning_rate1)\n",
    "\n",
    "history1 = model.fit(\n",
    "                train_generator,\n",
    "                steps_per_epoch=nb_train_samples // batch_size,\n",
    "                epochs=epochs1,\n",
    "                callbacks=[callback],\n",
    "                validation_data=validation_generator,\n",
    "                validation_steps=1)\n",
    "\n",
    "# save the model\n",
    "# model.save_weights('./results/HCR-Net-I.h5')\n",
    "\n",
    "#################################\n",
    "#### TRAINING PHASE - II #########\n",
    "#################################\n",
    "# fine tuning the model by unfreezing the lower layers (used from VGG16)\n",
    "base_model.trainable = True\n",
    "\n",
    "# reset the image generators\n",
    "train_generator.reset()\n",
    "validation_generator.reset()\n",
    "\n",
    "# recompile the model\n",
    "model.compile(loss='categorical_crossentropy', optimizer=tensorflow.keras.optimizers.RMSprop(), metrics=['accuracy'])\n",
    "print(model.summary())\n",
    "\n",
    "# define callback for custom learning rate\n",
    "callback = tensorflow.keras.callbacks.LearningRateScheduler(learning_rate2)\n",
    "\n",
    "history2 = model.fit(\n",
    "                train_generator,\n",
    "                steps_per_epoch=nb_train_samples // batch_size,\n",
    "                epochs=epochs2,\n",
    "                callbacks=[callback],\n",
    "                validation_data=validation_generator,\n",
    "                validation_steps=1)\n",
    "\n",
    "# save the model\n",
    "model.save_weights('./results/HCR-Net-II.h5')\n",
    "\n",
    "##############################################################################\n",
    "# plot the convergence, in terms of accuracy and loss\n",
    "plt.plot(history1.history['accuracy']+history2.history['accuracy'])\n",
    "plt.plot(history1.history['val_accuracy']+history2.history['val_accuracy'])\n",
    "# plt.title('model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(['Train', 'Test'], loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history1.history['loss']+history2.history['loss'])\n",
    "plt.plot(history1.history['val_loss']+history2.history['val_loss'])\n",
    "# plt.title('model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(['Train', 'Test'], loc='upper right')\n",
    "plt.show()\n",
    "\n",
    "# evaluate model and plot confusion matrix\n",
    "# filenames = validation_generator.filenames\n",
    "predict = model.predict(validation_generator)\n",
    "y_pred = np.argmax(predict,axis=-1)\n",
    "cf_matrix = confusion_matrix(validation_generator.classes,y_pred)\n",
    "\n",
    "# calculate size of confusion matrix figure, and plot it\n",
    "fig, ax = plt.subplots(figsize=(math.ceil(num_classes/3.0), math.ceil(num_classes/3.0)))\n",
    "labels = list(validation_generator.class_indices.keys())\n",
    "sns.heatmap(cf_matrix, annot=True, xticklabels=labels, yticklabels=labels, fmt='', cmap='Blues')\n",
    "plt.ylabel('Actual Class')\n",
    "plt.xlabel('Predicted Class')\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision_score:  0.9950164345362801\n",
      "recall_score   :  0.9949999999999999\n",
      "f1_score       :  0.9949993833212807\n",
      "accuracy_score :  0.995\n",
      "\n",
      "Best accuracy  :  0.9959999918937683\n"
     ]
    }
   ],
   "source": [
    "# calculate different metrics for HCR-Net on the test dataset\n",
    "print('precision_score: ', precision_score(validation_generator.classes,y_pred, average=\"macro\"))\n",
    "print('recall_score   : ', recall_score(validation_generator.classes,y_pred, average=\"macro\"))\n",
    "print('f1_score       : ', f1_score(validation_generator.classes,y_pred, average=\"macro\"))\n",
    "print('accuracy_score : ', accuracy_score(validation_generator.classes,y_pred, normalize=True))\n",
    "\n",
    "print(\"\\nBest accuracy  : \", max(max(history1.history['val_accuracy']), max(history2.history['val_accuracy'])))"
   ]
  }
 ],
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   "display_name": "base",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
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  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "b74b47d5e9938cd240a35a32c558a1a556ee459f62601c55a90ff138e807f9ec"
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