Capstone 2 Modelling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "import os\n",
    "import json\n",
    "from typing import Tuple, Callable\n",
    "from keras.callbacks import CSVLogger, ModelCheckpoint, EarlyStopping\n",
    "from keras.callbacks import ReduceLROnPlateau\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.layers import Dense, Activation, Dropout, Flatten\n",
    "from keras import optimizers\n",
    "from keras.models import Sequential\n",
    "from keras import backend as K\n",
    "from keras import metrics\n",
    "from keras.applications import InceptionV3, MobileNetV2\n",
    "from keras.engine.training import Model\n",
    "from keras import layers\n",
    "from keras import models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AM General Hummer SUV 2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acura RL Sedan 2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Acura TL Sedan 2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Acura TL Type-S 2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Acura TSX Sedan 2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   model_name\n",
       "0  AM General Hummer SUV 2000\n",
       "1         Acura RL Sedan 2012\n",
       "2         Acura TL Sedan 2012\n",
       "3        Acura TL Type-S 2008\n",
       "4        Acura TSX Sedan 2012"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv('clean_train_data.csv', index_col= 0)\n",
    "test_df = pd.read_csv('clean_test_data.csv', index_col= 0)\n",
    "names_df = pd.read_csv('names.csv',header= None)\n",
    "names_df.columns = ['model_name']\n",
    "names_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_width = 299\n",
    "img_height = 299\n",
    "num_channels = 3\n",
    "num_classes = 196\n",
    "batch_size = 64\n",
    "\n",
    "train_dir = '../Capstone 2 Stanford Car Classification/car_data/train'\n",
    "test_dir = '../Capstone 2 Stanford Car Classification/car_data/test'\n",
    "datagen = ImageDataGenerator(rescale=1/255,\n",
    "                                   rotation_range= 40,\n",
    "                                   width_shift_range= 0.2,\n",
    "                                   height_shift_range= 0.2,\n",
    "                                   shear_range= 0.2,\n",
    "                                   zoom_range= 0.2,\n",
    "                                   horizontal_flip= True,\n",
    "                                   fill_mode= 'nearest')\n",
    "\n",
    "classList = names_df.model_name.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 8103 images belonging to 196 classes.\n"
     ]
    }
   ],
   "source": [
    "train_generator = datagen.flow_from_directory(directory= train_dir,\n",
    "                                            target_size= (img_width,img_height),\n",
    "                                            classes= classList,\n",
    "                                            class_mode= 'categorical',\n",
    "                                            batch_size= batch_size)\n",
    "\n",
    "train_images_count = len(train_generator.filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 8000 images belonging to 196 classes.\n"
     ]
    }
   ],
   "source": [
    "test_generator = datagen.flow_from_directory(directory= test__dir,\n",
    "                                            target_size= (img_width,img_height),\n",
    "                                            classes= classList,\n",
    "                                            class_mode= 'categorical',\n",
    "                                            batch_size= batch_size)\n",
    "\n",
    "test_images_count = len(train_generator.filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_base = InceptionV3(weights= 'imagenet',\n",
    "                    include_top= False,\n",
    "                    pooling= 'avg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the number of trainable weights before freezing the conv base: 190\n",
      "This is the number of trainable weights before freezing the conv base: 2\n"
     ]
    }
   ],
   "source": [
    "pred_layer_config = {\n",
    "    'activation': 'softmax',\n",
    "    'activity_regularizer': None,\n",
    "    'bias_constraint': None,\n",
    "    'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
    "    'bias_regularizer': None,\n",
    "    'kernel_constraint': None,\n",
    "    'kernel_initializer': {'class_name': 'VarianceScaling',\n",
    "                           'config': {\n",
    "                               'distribution': 'uniform',\n",
    "                               'mode': 'fan_avg',\n",
    "                               'scale': 1.0,\n",
    "                               'seed': 8}\n",
    "                          },\n",
    "    'kernel_regularizer': None,\n",
    "    'name': 'predictions',\n",
    "    'trainable': True,\n",
    "    'units': 196,\n",
    "    'use_bias': True}\n",
    "\n",
    "model = Sequential()\n",
    "model.add(conv_base)\n",
    "model.add(Dropout(0.5, seed=21))\n",
    "model.add(Dense(**pred_layer_config))\n",
    "\n",
    "conv_base.trainable = True\n",
    "print('This is the number of trainable weights '\n",
    "      'before freezing the conv base:', len(model.trainable_weights))\n",
    "conv_base.trainable = False\n",
    "print('This is the number of trainable weights '\n",
    "      'before freezing the conv base:', len(model.trainable_weights))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "inception_v3 (Model)         (None, 2048)              21802784  \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "predictions (Dense)          (None, 196)               401604    \n",
      "=================================================================\n",
      "Total params: 22,204,388\n",
      "Trainable params: 401,604\n",
      "Non-trainable params: 21,802,784\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def top_5_acc(y_true, y_pred):\n",
    "    return metrics.top_k_categorical_accuracy(y_true, y_pred, k=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = optimizers.Nadam(lr=0.002, beta_1=0.9, beta_2=0.999, schedule_decay=0.004)\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=optimizer,\n",
    "              metrics=[metrics.categorical_accuracy, top_5_acc])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "126/126 [==============================] - 6028s 48s/step - loss: 5.3357 - categorical_accuracy: 0.0234 - top_5_acc: 0.0776 - val_loss: 4.7488 - val_categorical_accuracy: 0.0373 - val_top_5_acc: 0.1408\n",
      "Epoch 2/10\n",
      "126/126 [==============================] - 5912s 47s/step - loss: 4.6541 - categorical_accuracy: 0.0684 - top_5_acc: 0.2036 - val_loss: 4.5665 - val_categorical_accuracy: 0.0621 - val_top_5_acc: 0.1975\n",
      "Epoch 3/10\n",
      "126/126 [==============================] - 5928s 47s/step - loss: 4.2897 - categorical_accuracy: 0.1121 - top_5_acc: 0.2862 - val_loss: 4.5071 - val_categorical_accuracy: 0.0695 - val_top_5_acc: 0.2221\n",
      "Epoch 4/10\n",
      "126/126 [==============================] - 6097s 48s/step - loss: 4.0365 - categorical_accuracy: 0.1440 - top_5_acc: 0.3486 - val_loss: 4.3821 - val_categorical_accuracy: 0.0900 - val_top_5_acc: 0.2486\n",
      "Epoch 5/10\n",
      "126/126 [==============================] - 6396s 51s/step - loss: 3.8541 - categorical_accuracy: 0.1630 - top_5_acc: 0.3917 - val_loss: 4.6656 - val_categorical_accuracy: 0.0882 - val_top_5_acc: 0.2619\n",
      "Epoch 6/10\n",
      "126/126 [==============================] - 6157s 49s/step - loss: 3.7606 - categorical_accuracy: 0.1774 - top_5_acc: 0.4079 - val_loss: 4.3703 - val_categorical_accuracy: 0.0882 - val_top_5_acc: 0.2609\n",
      "Epoch 7/10\n",
      "126/126 [==============================] - 5988s 48s/step - loss: 3.6637 - categorical_accuracy: 0.1989 - top_5_acc: 0.4319 - val_loss: 4.1636 - val_categorical_accuracy: 0.0925 - val_top_5_acc: 0.2604\n",
      "Epoch 8/10\n",
      "126/126 [==============================] - 6006s 48s/step - loss: 3.5472 - categorical_accuracy: 0.2117 - top_5_acc: 0.4589 - val_loss: 4.2772 - val_categorical_accuracy: 0.0865 - val_top_5_acc: 0.2726\n",
      "Epoch 9/10\n",
      "126/126 [==============================] - 6072s 48s/step - loss: 3.5410 - categorical_accuracy: 0.2118 - top_5_acc: 0.4631 - val_loss: 4.2704 - val_categorical_accuracy: 0.0835 - val_top_5_acc: 0.2681\n",
      "Epoch 10/10\n",
      "126/126 [==============================] - 6321s 50s/step - loss: 3.4932 - categorical_accuracy: 0.2248 - top_5_acc: 0.4752 - val_loss: 4.4681 - val_categorical_accuracy: 0.0981 - val_top_5_acc: 0.2763\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_generator,\n",
    "                            steps_per_epoch= 8103 // batch_size,\n",
    "                            epochs=10,\n",
    "                            validation_data=test_generator,\n",
    "                            validation_steps= 8000 // batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.figure(figsize=(10,10))\n",
    "_ = plt.plot(history.history['loss'], label='Train Set')\n",
    "_ = plt.plot(history.history['val_loss'], label='Test Set')\n",
    "_ = plt.title('Train and Test Set Loss ')\n",
    "_ = plt.ylabel('Loss')\n",
    "_ = plt.xlabel('Epoch')\n",
    "_ = plt.legend(loc=\"upper left\")\n",
    "_ = plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.figure(figsize=(10,10))\n",
    "_ = plt.plot(history.history['categorical_accuracy'], label='Train Set')\n",
    "_ = plt.plot(history.history['val_categorical_accuracy'], label='Test Set')\n",
    "_ = plt.title('Categorical Accuracy on Train and Test Set')\n",
    "_ = plt.ylabel('Accuracy')\n",
    "_ = plt.xlabel('Epoch')\n",
    "_ = plt.legend(loc=\"upper left\")\n",
    "_ = plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.figure(figsize=(10,10))\n",
    "_ = plt.plot(history.history['top_5_acc'], label='Train Set')\n",
    "_ = plt.plot(history.history['val_top_5_acc'], label='Test Set')\n",
    "_ = plt.title('Top 5 Categorical Accuracy Train and Test Set Loss ')\n",
    "_ = plt.ylabel('Accuracy')\n",
    "_ = plt.xlabel('Epoch')\n",
    "_ = plt.legend(loc=\"upper left\")\n",
    "_ = plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the number of trainable weights after limited freezing the conv base: 50\n"
     ]
    }
   ],
   "source": [
    "conv_base.trainable = True\n",
    "set_trainable = False\n",
    "layerlist = ['conv2d_71', 'batch_normalization_71',\n",
    "            'conv2d_72', 'batch_normalization_72',\n",
    "            'conv2d_73', 'batch_normalization_73',\n",
    "            'conv2d_74', 'batch_normalization_74',\n",
    "            'conv2d_75', 'batch_normalization_75',\n",
    "            'conv2d_76', 'batch_normalization_76'\n",
    "            'conv2d_77', 'batch_normalization_77',\n",
    "            'conv2d_78', 'batch_normalization_78',\n",
    "            'conv2d_79', 'batch_normalization_79',\n",
    "            'conv2d_80', 'batch_normalization_80',\n",
    "            'conv2d_81', 'batch_normalization_81',\n",
    "            'conv2d_82', 'batch_normalization_82',\n",
    "            'conv2d_83', 'batch_normalization_83',\n",
    "            'conv2d_84', 'batch_normalization_84',\n",
    "            'conv2d_85', 'batch_normalization_85'\n",
    "            'conv2d_86', 'batch_normalization_86',\n",
    "            'conv2d_87', 'batch_normalization_86',\n",
    "            'conv2d_88', 'batch_normalization_88',\n",
    "            'conv2d_89', 'batch_normalization_89',\n",
    "            'conv2d_90', 'batch_normalization_90',\n",
    "            'conv2d_91', 'batch_normalization_90',\n",
    "            'conv2d_92', 'batch_normalization_92',\n",
    "            'conv2d_93', 'batch_normalization_93',\n",
    "            'conv2d_94', 'batch_normalization_94']\n",
    "\n",
    "for layer in conv_base.layers:\n",
    "    if layer.name in layerlist:\n",
    "        set_trainable = True\n",
    "        \n",
    "    if set_trainable:\n",
    "        layer.trainable = True\n",
    "        \n",
    "    else:\n",
    "        layer.trainable = False\n",
    "\n",
    "print('This is the number of trainable weights after limited freezing the conv base:', len(model.trainable_weights))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "inception_v3 (Model)         (None, 2048)              21802784  \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "predictions (Dense)          (None, 196)               401604    \n",
      "=================================================================\n",
      "Total params: 20,507,364\n",
      "Trainable params: 11,516,484\n",
      "Non-trainable params: 8,990,880\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sayantanchattopadhyay/opt/anaconda3/lib/python3.7/site-packages/keras/engine/training.py:297: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "  'Discrepancy between trainable weights and collected trainable'\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "126/126 [==============================] - 7368s 58s/step - loss: 2.7456 - categorical_accuracy: 0.3183 - top_5_acc: 0.6320 - val_loss: 2.6492 - val_categorical_accuracy: 0.2866 - val_top_5_acc: 0.6126\n",
      "Epoch 2/15\n",
      "126/126 [==============================] - 7482s 59s/step - loss: 1.9525 - categorical_accuracy: 0.4754 - top_5_acc: 0.7943 - val_loss: 1.6553 - val_categorical_accuracy: 0.4277 - val_top_5_acc: 0.7559\n",
      "Epoch 3/15\n",
      "126/126 [==============================] - 7347s 58s/step - loss: 1.5322 - categorical_accuracy: 0.5715 - top_5_acc: 0.8665 - val_loss: 1.6422 - val_categorical_accuracy: 0.5064 - val_top_5_acc: 0.8154\n",
      "Epoch 4/15\n",
      "126/126 [==============================] - 7283s 58s/step - loss: 1.2498 - categorical_accuracy: 0.6424 - top_5_acc: 0.8979 - val_loss: 2.0648 - val_categorical_accuracy: 0.5421 - val_top_5_acc: 0.8372\n",
      "Epoch 5/15\n",
      "126/126 [==============================] - 7333s 58s/step - loss: 1.0305 - categorical_accuracy: 0.6942 - top_5_acc: 0.9362 - val_loss: 1.8493 - val_categorical_accuracy: 0.5866 - val_top_5_acc: 0.8708\n",
      "Epoch 6/15\n",
      "126/126 [==============================] - 7381s 59s/step - loss: 0.8812 - categorical_accuracy: 0.7409 - top_5_acc: 0.9506 - val_loss: 1.7534 - val_categorical_accuracy: 0.5771 - val_top_5_acc: 0.8529\n",
      "Epoch 7/15\n",
      "126/126 [==============================] - 7311s 58s/step - loss: 0.7673 - categorical_accuracy: 0.7697 - top_5_acc: 0.9632 - val_loss: 1.3100 - val_categorical_accuracy: 0.6194 - val_top_5_acc: 0.8775\n",
      "Epoch 8/15\n",
      "126/126 [==============================] - 7329s 58s/step - loss: 0.6494 - categorical_accuracy: 0.8028 - top_5_acc: 0.9723 - val_loss: 1.4970 - val_categorical_accuracy: 0.5796 - val_top_5_acc: 0.8476\n",
      "Epoch 9/15\n",
      "126/126 [==============================] - 7397s 59s/step - loss: 0.5859 - categorical_accuracy: 0.8227 - top_5_acc: 0.9790 - val_loss: 1.1868 - val_categorical_accuracy: 0.6485 - val_top_5_acc: 0.8903\n",
      "Epoch 10/15\n",
      "126/126 [==============================] - 7349s 58s/step - loss: 0.5178 - categorical_accuracy: 0.8460 - top_5_acc: 0.9826 - val_loss: 1.2946 - val_categorical_accuracy: 0.6233 - val_top_5_acc: 0.8712\n",
      "Epoch 11/15\n",
      "126/126 [==============================] - 7406s 59s/step - loss: 0.4811 - categorical_accuracy: 0.8518 - top_5_acc: 0.9837 - val_loss: 1.2169 - val_categorical_accuracy: 0.6430 - val_top_5_acc: 0.8890\n",
      "Epoch 12/15\n",
      "126/126 [==============================] - 7308s 58s/step - loss: 0.4075 - categorical_accuracy: 0.8742 - top_5_acc: 0.9877 - val_loss: 1.6570 - val_categorical_accuracy: 0.6525 - val_top_5_acc: 0.8895\n",
      "Epoch 13/15\n",
      "126/126 [==============================] - 7415s 59s/step - loss: 0.3981 - categorical_accuracy: 0.8750 - top_5_acc: 0.9898 - val_loss: 1.4090 - val_categorical_accuracy: 0.6361 - val_top_5_acc: 0.8799\n",
      "Epoch 14/15\n",
      "126/126 [==============================] - 7845s 62s/step - loss: 0.3868 - categorical_accuracy: 0.8778 - top_5_acc: 0.9889 - val_loss: 1.1813 - val_categorical_accuracy: 0.6655 - val_top_5_acc: 0.8928\n",
      "Epoch 15/15\n",
      "126/126 [==============================] - 7457s 59s/step - loss: 0.3321 - categorical_accuracy: 0.8994 - top_5_acc: 0.9924 - val_loss: 1.2635 - val_categorical_accuracy: 0.6675 - val_top_5_acc: 0.8988\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=optimizer,\n",
    "              metrics=[metrics.categorical_accuracy, top_5_acc])\n",
    "\n",
    "history = model.fit_generator(train_generator,\n",
    "                            steps_per_epoch= 8103 // batch_size,\n",
    "                            epochs= 15,\n",
    "                            validation_data=test_generator,\n",
    "                            validation_steps= 8000 // batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.figure(figsize=(10,10))\n",
    "_ = plt.plot(history.history['loss'], label='Train Set')\n",
    "_ = plt.plot(history.history['val_loss'], label='Test Set')\n",
    "_ = plt.title('Train and Test Set Loss ')\n",
    "_ = plt.ylabel('Loss')\n",
    "_ = plt.xlabel('Epoch')\n",
    "_ = plt.legend(loc=\"upper left\")\n",
    "_ = plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.figure(figsize=(10,10))\n",
    "_ = plt.plot(history.history['categorical_accuracy'], label='Train Set')\n",
    "_ = plt.plot(history.history['val_categorical_accuracy'], label='Test Set')\n",
    "_ = plt.title('Categorical Accuracy on Train and Test Set')\n",
    "_ = plt.ylabel('Accuracy')\n",
    "_ = plt.xlabel('Epoch')\n",
    "_ = plt.legend(loc=\"upper left\")\n",
    "_ = plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.figure(figsize=(10,10))\n",
    "_ = plt.plot(history.history['top_5_acc'], label='Train Set')\n",
    "_ = plt.plot(history.history['val_top_5_acc'], label='Test Set')\n",
    "_ = plt.title('Top 5 Categorical Accuracy Train and Test Set Loss ')\n",
    "_ = plt.ylabel('Accuracy')\n",
    "_ = plt.xlabel('Epoch')\n",
    "_ = plt.legend(loc=\"upper left\")\n",
    "_ = plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mobilenetv2(_num_classes) -> Tuple[Model, Model, int, Callable]:\n",
    "    \n",
    "    initial_model: Model = MobileNetV2(weights= 'imagenet', alpha= 1.4, include_top= False,\n",
    "                                       input_shape= (224, 224, 3))\n",
    "\n",
    "    initial_model.trainable = True\n",
    "    \n",
    "    for i, layer in enumerate(initial_model.layers):\n",
    "        layer.trainable = True\n",
    "\n",
    "\n",
    "    initial_model_output = initial_model.output\n",
    "    x = layers.GlobalAveragePooling2D()(initial_model_output)\n",
    "    x = layers.Dense(1024, activation='relu')(x)\n",
    "    predictions = layers.Dense(_num_classes, activation='softmax')(x)\n",
    "\n",
    "    model = Model(initial_model.input, predictions)\n",
    "\n",
    "    return model, initial_model, image_size, keras.applications.mobilenet_v2.preprocess_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_callbacks_list(_early_stopping_patience, _reduce_lr_on_plateau_factor, _reduce_lr_on_plateau_patience):\n",
    "    return [keras.callbacks.EarlyStopping(monitor= 'val_categorical_accuracy',patience= _early_stopping_patience),\n",
    "        keras.callbacks.ModelCheckpoint(verbose= 1,\n",
    "                                        filepath= 'best_model.h5',\n",
    "                                        monitor= 'val_loss',\n",
    "                                        save_best_only= True),\n",
    "        keras.callbacks.ReduceLROnPlateau(verbose= 1,\n",
    "                                        monitor= 'val_loss',\n",
    "                                        factor= _reduce_lr_on_plateau_factor,\n",
    "                                        patience =_reduce_lr_on_plateau_patience),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_lr = 0.01\n",
    "momentum = 0.9\n",
    "epochs = 25\n",
    "optimazer = keras.optimizers.SGD(lr=init_lr, momentum=momentum)\n",
    "early_stopping_patience = 10\n",
    "reduce_lr_on_plateau_factor = 0.2\n",
    "reduce_lr_on_plateau_patience = 3\n",
    "model_function = get_mobilenetv2\n",
    "num_classes = len(os.listdir(train_dir))\n",
    "model, conv_base, image_size, preprocess_function = model_function(196)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 8144 images belonging to 196 classes.\n"
     ]
    }
   ],
   "source": [
    "train_image_datagen = ImageDataGenerator(\n",
    "    preprocessing_function=preprocess_function,\n",
    "    rotation_range=40,\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    fill_mode='nearest')\n",
    "\n",
    "train_generator = train_image_datagen.flow_from_directory(train_dir,\n",
    "                                                        target_size= (image_size, image_size),\n",
    "                                                        batch_size= batch_size,\n",
    "                                                        class_mode= 'categorical')\n",
    "\n",
    "train_images_count = len(train_generator.filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 8041 images belonging to 196 classes.\n"
     ]
    }
   ],
   "source": [
    "validation_image_datagen = ImageDataGenerator(preprocessing_function=preprocess_function)\n",
    "validation_generator = validation_image_datagen.flow_from_directory(test_dir,\n",
    "                                                                    target_size= (image_size, image_size),\n",
    "                                                                    batch_size= batch_size,\n",
    "                                                                    class_mode= 'categorical')\n",
    "                                                                    \n",
    "validation_images_count = len(validation_generator.filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss= 'categorical_crossentropy', optimizer= optimazer, metrics= [metrics.categorical_accuracy, top_5_acc])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_6\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_7 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "Conv1_pad (ZeroPadding2D)       (None, 225, 225, 3)  0           input_7[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "Conv1 (Conv2D)                  (None, 112, 112, 48) 1296        Conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_Conv1 (BatchNormalization)   (None, 112, 112, 48) 192         Conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "Conv1_relu (ReLU)               (None, 112, 112, 48) 0           bn_Conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise (Depthw (None, 112, 112, 48) 432         Conv1_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise_BN (Bat (None, 112, 112, 48) 192         expanded_conv_depthwise[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise_relu (R (None, 112, 112, 48) 0           expanded_conv_depthwise_BN[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_project (Conv2D)  (None, 112, 112, 24) 1152        expanded_conv_depthwise_relu[0][0\n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_project_BN (Batch (None, 112, 112, 24) 96          expanded_conv_project[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand (Conv2D)         (None, 112, 112, 144 3456        expanded_conv_project_BN[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand_BN (BatchNormali (None, 112, 112, 144 576         block_1_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand_relu (ReLU)      (None, 112, 112, 144 0           block_1_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_pad (ZeroPadding2D)     (None, 113, 113, 144 0           block_1_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise (DepthwiseCon (None, 56, 56, 144)  1296        block_1_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise_BN (BatchNorm (None, 56, 56, 144)  576         block_1_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise_relu (ReLU)   (None, 56, 56, 144)  0           block_1_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_1_project (Conv2D)        (None, 56, 56, 32)   4608        block_1_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_1_project_BN (BatchNormal (None, 56, 56, 32)   128         block_1_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand (Conv2D)         (None, 56, 56, 192)  6144        block_1_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand_BN (BatchNormali (None, 56, 56, 192)  768         block_2_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand_relu (ReLU)      (None, 56, 56, 192)  0           block_2_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise (DepthwiseCon (None, 56, 56, 192)  1728        block_2_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise_BN (BatchNorm (None, 56, 56, 192)  768         block_2_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise_relu (ReLU)   (None, 56, 56, 192)  0           block_2_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_2_project (Conv2D)        (None, 56, 56, 32)   6144        block_2_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_2_project_BN (BatchNormal (None, 56, 56, 32)   128         block_2_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_2_add (Add)               (None, 56, 56, 32)   0           block_1_project_BN[0][0]         \n",
      "                                                                 block_2_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand (Conv2D)         (None, 56, 56, 192)  6144        block_2_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand_BN (BatchNormali (None, 56, 56, 192)  768         block_3_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand_relu (ReLU)      (None, 56, 56, 192)  0           block_3_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_pad (ZeroPadding2D)     (None, 57, 57, 192)  0           block_3_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_3_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_3_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_3_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_3_project (Conv2D)        (None, 28, 28, 48)   9216        block_3_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_3_project_BN (BatchNormal (None, 28, 28, 48)   192         block_3_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand (Conv2D)         (None, 28, 28, 288)  13824       block_3_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand_BN (BatchNormali (None, 28, 28, 288)  1152        block_4_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand_relu (ReLU)      (None, 28, 28, 288)  0           block_4_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise (DepthwiseCon (None, 28, 28, 288)  2592        block_4_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise_BN (BatchNorm (None, 28, 28, 288)  1152        block_4_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise_relu (ReLU)   (None, 28, 28, 288)  0           block_4_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_4_project (Conv2D)        (None, 28, 28, 48)   13824       block_4_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_4_project_BN (BatchNormal (None, 28, 28, 48)   192         block_4_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_4_add (Add)               (None, 28, 28, 48)   0           block_3_project_BN[0][0]         \n",
      "                                                                 block_4_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand (Conv2D)         (None, 28, 28, 288)  13824       block_4_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand_BN (BatchNormali (None, 28, 28, 288)  1152        block_5_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand_relu (ReLU)      (None, 28, 28, 288)  0           block_5_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise (DepthwiseCon (None, 28, 28, 288)  2592        block_5_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise_BN (BatchNorm (None, 28, 28, 288)  1152        block_5_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise_relu (ReLU)   (None, 28, 28, 288)  0           block_5_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_5_project (Conv2D)        (None, 28, 28, 48)   13824       block_5_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_5_project_BN (BatchNormal (None, 28, 28, 48)   192         block_5_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_5_add (Add)               (None, 28, 28, 48)   0           block_4_add[0][0]                \n",
      "                                                                 block_5_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand (Conv2D)         (None, 28, 28, 288)  13824       block_5_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand_BN (BatchNormali (None, 28, 28, 288)  1152        block_6_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand_relu (ReLU)      (None, 28, 28, 288)  0           block_6_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_6_pad (ZeroPadding2D)     (None, 29, 29, 288)  0           block_6_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise (DepthwiseCon (None, 14, 14, 288)  2592        block_6_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise_BN (BatchNorm (None, 14, 14, 288)  1152        block_6_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise_relu (ReLU)   (None, 14, 14, 288)  0           block_6_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_6_project (Conv2D)        (None, 14, 14, 88)   25344       block_6_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_6_project_BN (BatchNormal (None, 14, 14, 88)   352         block_6_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand (Conv2D)         (None, 14, 14, 528)  46464       block_6_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand_BN (BatchNormali (None, 14, 14, 528)  2112        block_7_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand_relu (ReLU)      (None, 14, 14, 528)  0           block_7_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise (DepthwiseCon (None, 14, 14, 528)  4752        block_7_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise_BN (BatchNorm (None, 14, 14, 528)  2112        block_7_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise_relu (ReLU)   (None, 14, 14, 528)  0           block_7_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_7_project (Conv2D)        (None, 14, 14, 88)   46464       block_7_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_7_project_BN (BatchNormal (None, 14, 14, 88)   352         block_7_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_7_add (Add)               (None, 14, 14, 88)   0           block_6_project_BN[0][0]         \n",
      "                                                                 block_7_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand (Conv2D)         (None, 14, 14, 528)  46464       block_7_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand_BN (BatchNormali (None, 14, 14, 528)  2112        block_8_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand_relu (ReLU)      (None, 14, 14, 528)  0           block_8_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise (DepthwiseCon (None, 14, 14, 528)  4752        block_8_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise_BN (BatchNorm (None, 14, 14, 528)  2112        block_8_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise_relu (ReLU)   (None, 14, 14, 528)  0           block_8_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_8_project (Conv2D)        (None, 14, 14, 88)   46464       block_8_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_8_project_BN (BatchNormal (None, 14, 14, 88)   352         block_8_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_8_add (Add)               (None, 14, 14, 88)   0           block_7_add[0][0]                \n",
      "                                                                 block_8_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand (Conv2D)         (None, 14, 14, 528)  46464       block_8_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand_BN (BatchNormali (None, 14, 14, 528)  2112        block_9_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand_relu (ReLU)      (None, 14, 14, 528)  0           block_9_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise (DepthwiseCon (None, 14, 14, 528)  4752        block_9_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise_BN (BatchNorm (None, 14, 14, 528)  2112        block_9_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise_relu (ReLU)   (None, 14, 14, 528)  0           block_9_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_9_project (Conv2D)        (None, 14, 14, 88)   46464       block_9_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_9_project_BN (BatchNormal (None, 14, 14, 88)   352         block_9_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_9_add (Add)               (None, 14, 14, 88)   0           block_8_add[0][0]                \n",
      "                                                                 block_9_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand (Conv2D)        (None, 14, 14, 528)  46464       block_9_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand_BN (BatchNormal (None, 14, 14, 528)  2112        block_10_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand_relu (ReLU)     (None, 14, 14, 528)  0           block_10_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise (DepthwiseCo (None, 14, 14, 528)  4752        block_10_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise_BN (BatchNor (None, 14, 14, 528)  2112        block_10_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise_relu (ReLU)  (None, 14, 14, 528)  0           block_10_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_10_project (Conv2D)       (None, 14, 14, 136)  71808       block_10_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_10_project_BN (BatchNorma (None, 14, 14, 136)  544         block_10_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand (Conv2D)        (None, 14, 14, 816)  110976      block_10_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand_BN (BatchNormal (None, 14, 14, 816)  3264        block_11_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand_relu (ReLU)     (None, 14, 14, 816)  0           block_11_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise (DepthwiseCo (None, 14, 14, 816)  7344        block_11_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise_BN (BatchNor (None, 14, 14, 816)  3264        block_11_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise_relu (ReLU)  (None, 14, 14, 816)  0           block_11_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_11_project (Conv2D)       (None, 14, 14, 136)  110976      block_11_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_11_project_BN (BatchNorma (None, 14, 14, 136)  544         block_11_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_11_add (Add)              (None, 14, 14, 136)  0           block_10_project_BN[0][0]        \n",
      "                                                                 block_11_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand (Conv2D)        (None, 14, 14, 816)  110976      block_11_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand_BN (BatchNormal (None, 14, 14, 816)  3264        block_12_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand_relu (ReLU)     (None, 14, 14, 816)  0           block_12_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise (DepthwiseCo (None, 14, 14, 816)  7344        block_12_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise_BN (BatchNor (None, 14, 14, 816)  3264        block_12_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise_relu (ReLU)  (None, 14, 14, 816)  0           block_12_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_12_project (Conv2D)       (None, 14, 14, 136)  110976      block_12_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_12_project_BN (BatchNorma (None, 14, 14, 136)  544         block_12_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_12_add (Add)              (None, 14, 14, 136)  0           block_11_add[0][0]               \n",
      "                                                                 block_12_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand (Conv2D)        (None, 14, 14, 816)  110976      block_12_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand_BN (BatchNormal (None, 14, 14, 816)  3264        block_13_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand_relu (ReLU)     (None, 14, 14, 816)  0           block_13_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_13_pad (ZeroPadding2D)    (None, 15, 15, 816)  0           block_13_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise (DepthwiseCo (None, 7, 7, 816)    7344        block_13_pad[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise_BN (BatchNor (None, 7, 7, 816)    3264        block_13_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise_relu (ReLU)  (None, 7, 7, 816)    0           block_13_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_13_project (Conv2D)       (None, 7, 7, 224)    182784      block_13_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_13_project_BN (BatchNorma (None, 7, 7, 224)    896         block_13_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand (Conv2D)        (None, 7, 7, 1344)   301056      block_13_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand_BN (BatchNormal (None, 7, 7, 1344)   5376        block_14_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand_relu (ReLU)     (None, 7, 7, 1344)   0           block_14_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise (DepthwiseCo (None, 7, 7, 1344)   12096       block_14_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise_BN (BatchNor (None, 7, 7, 1344)   5376        block_14_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise_relu (ReLU)  (None, 7, 7, 1344)   0           block_14_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_14_project (Conv2D)       (None, 7, 7, 224)    301056      block_14_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_14_project_BN (BatchNorma (None, 7, 7, 224)    896         block_14_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_14_add (Add)              (None, 7, 7, 224)    0           block_13_project_BN[0][0]        \n",
      "                                                                 block_14_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand (Conv2D)        (None, 7, 7, 1344)   301056      block_14_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand_BN (BatchNormal (None, 7, 7, 1344)   5376        block_15_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand_relu (ReLU)     (None, 7, 7, 1344)   0           block_15_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise (DepthwiseCo (None, 7, 7, 1344)   12096       block_15_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise_BN (BatchNor (None, 7, 7, 1344)   5376        block_15_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise_relu (ReLU)  (None, 7, 7, 1344)   0           block_15_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_15_project (Conv2D)       (None, 7, 7, 224)    301056      block_15_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_15_project_BN (BatchNorma (None, 7, 7, 224)    896         block_15_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_15_add (Add)              (None, 7, 7, 224)    0           block_14_add[0][0]               \n",
      "                                                                 block_15_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand (Conv2D)        (None, 7, 7, 1344)   301056      block_15_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand_BN (BatchNormal (None, 7, 7, 1344)   5376        block_16_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand_relu (ReLU)     (None, 7, 7, 1344)   0           block_16_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise (DepthwiseCo (None, 7, 7, 1344)   12096       block_16_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise_BN (BatchNor (None, 7, 7, 1344)   5376        block_16_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise_relu (ReLU)  (None, 7, 7, 1344)   0           block_16_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_16_project (Conv2D)       (None, 7, 7, 448)    602112      block_16_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_16_project_BN (BatchNorma (None, 7, 7, 448)    1792        block_16_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "Conv_1 (Conv2D)                 (None, 7, 7, 1792)   802816      block_16_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Conv_1_bn (BatchNormalization)  (None, 7, 7, 1792)   7168        Conv_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "out_relu (ReLU)                 (None, 7, 7, 1792)   0           Conv_1_bn[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_6 (Glo (None, 1792)         0           out_relu[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_11 (Dense)                (None, 1024)         1836032     global_average_pooling2d_6[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "dense_12 (Dense)                (None, 196)          200900      dense_11[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 6,400,644\n",
      "Trainable params: 6,352,708\n",
      "Non-trainable params: 47,936\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "127/127 [==============================] - 8762s 69s/step - loss: 4.8044 - categorical_accuracy: 0.0489 - top_5_acc: 0.1525 - val_loss: 6.2711 - val_categorical_accuracy: 0.0284 - val_top_5_acc: 0.1000\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 6.27111, saving model to best_model.h5\n",
      "Epoch 2/25\n",
      "127/127 [==============================] - 8414s 66s/step - loss: 3.0042 - categorical_accuracy: 0.2687 - top_5_acc: 0.5741 - val_loss: 5.4241 - val_categorical_accuracy: 0.0790 - val_top_5_acc: 0.2448\n",
      "\n",
      "Epoch 00002: val_loss improved from 6.27111 to 5.42412, saving model to best_model.h5\n",
      "Epoch 3/25\n",
      "127/127 [==============================] - 8259s 65s/step - loss: 1.9579 - categorical_accuracy: 0.4750 - top_5_acc: 0.7962 - val_loss: 9.4749 - val_categorical_accuracy: 0.0184 - val_top_5_acc: 0.0923\n",
      "\n",
      "Epoch 00003: val_loss did not improve from 5.42412\n",
      "Epoch 4/25\n",
      "127/127 [==============================] - 8171s 64s/step - loss: 1.3765 - categorical_accuracy: 0.6095 - top_5_acc: 0.8882 - val_loss: 9.7028 - val_categorical_accuracy: 0.0133 - val_top_5_acc: 0.1167\n",
      "\n",
      "Epoch 00004: val_loss did not improve from 5.42412\n",
      "Epoch 5/25\n",
      "127/127 [==============================] - 8465s 67s/step - loss: 1.0793 - categorical_accuracy: 0.6897 - top_5_acc: 0.9267 - val_loss: 10.7395 - val_categorical_accuracy: 0.0357 - val_top_5_acc: 0.1140\n",
      "\n",
      "Epoch 00005: val_loss did not improve from 5.42412\n",
      "\n",
      "Epoch 00005: ReduceLROnPlateau reducing learning rate to 0.0019999999552965165.\n",
      "Epoch 6/25\n",
      "127/127 [==============================] - 8298s 65s/step - loss: 0.6056 - categorical_accuracy: 0.8316 - top_5_acc: 0.9708 - val_loss: 7.2643 - val_categorical_accuracy: 0.0711 - val_top_5_acc: 0.2007\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 5.42412\n",
      "Epoch 7/25\n",
      "127/127 [==============================] - 8320s 66s/step - loss: 0.3379 - categorical_accuracy: 0.9032 - top_5_acc: 0.9934 - val_loss: 6.6621 - val_categorical_accuracy: 0.1087 - val_top_5_acc: 0.2964\n",
      "\n",
      "Epoch 00007: val_loss did not improve from 5.42412\n",
      "Epoch 8/25\n",
      "127/127 [==============================] - 8655s 68s/step - loss: 0.2711 - categorical_accuracy: 0.9266 - top_5_acc: 0.9952 - val_loss: 4.3333 - val_categorical_accuracy: 0.2058 - val_top_5_acc: 0.4731\n",
      "\n",
      "Epoch 00008: val_loss improved from 5.42412 to 4.33328, saving model to best_model.h5\n",
      "Epoch 9/25\n",
      "127/127 [==============================] - 8583s 68s/step - loss: 0.2258 - categorical_accuracy: 0.9391 - top_5_acc: 0.9968 - val_loss: 2.8093 - val_categorical_accuracy: 0.3341 - val_top_5_acc: 0.6477\n",
      "\n",
      "Epoch 00009: val_loss improved from 4.33328 to 2.80932, saving model to best_model.h5\n",
      "Epoch 10/25\n",
      "127/127 [==============================] - 8733s 69s/step - loss: 0.2071 - categorical_accuracy: 0.9417 - top_5_acc: 0.9976 - val_loss: 2.4865 - val_categorical_accuracy: 0.4589 - val_top_5_acc: 0.7706\n",
      "\n",
      "Epoch 00010: val_loss improved from 2.80932 to 2.48654, saving model to best_model.h5\n",
      "Epoch 11/25\n",
      "127/127 [==============================] - 8656s 68s/step - loss: 0.1714 - categorical_accuracy: 0.9538 - top_5_acc: 0.9978 - val_loss: 2.2568 - val_categorical_accuracy: 0.5214 - val_top_5_acc: 0.8182\n",
      "\n",
      "Epoch 00011: val_loss improved from 2.48654 to 2.25680, saving model to best_model.h5\n",
      "Epoch 12/25\n",
      "127/127 [==============================] - 8322s 66s/step - loss: 0.1548 - categorical_accuracy: 0.9579 - top_5_acc: 0.9985 - val_loss: 1.5313 - val_categorical_accuracy: 0.6150 - val_top_5_acc: 0.8798\n",
      "\n",
      "Epoch 00012: val_loss improved from 2.25680 to 1.53125, saving model to best_model.h5\n",
      "Epoch 13/25\n",
      "127/127 [==============================] - 8313s 65s/step - loss: 0.1423 - categorical_accuracy: 0.9623 - top_5_acc: 0.9991 - val_loss: 0.8297 - val_categorical_accuracy: 0.6716 - val_top_5_acc: 0.9071\n",
      "\n",
      "Epoch 00013: val_loss improved from 1.53125 to 0.82975, saving model to best_model.h5\n",
      "Epoch 14/25\n",
      "127/127 [==============================] - 8440s 66s/step - loss: 0.1267 - categorical_accuracy: 0.9655 - top_5_acc: 0.9994 - val_loss: 0.6953 - val_categorical_accuracy: 0.6956 - val_top_5_acc: 0.9194\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.82975 to 0.69535, saving model to best_model.h5\n",
      "Epoch 15/25\n",
      "127/127 [==============================] - 8658s 68s/step - loss: 0.1207 - categorical_accuracy: 0.9682 - top_5_acc: 0.9993 - val_loss: 0.9095 - val_categorical_accuracy: 0.7115 - val_top_5_acc: 0.9278\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 0.69535\n",
      "Epoch 16/25\n",
      "127/127 [==============================] - 8288s 65s/step - loss: 0.1078 - categorical_accuracy: 0.9735 - top_5_acc: 0.9998 - val_loss: 1.0291 - val_categorical_accuracy: 0.7449 - val_top_5_acc: 0.9423\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 0.69535\n",
      "Epoch 17/25\n",
      "127/127 [==============================] - 8393s 66s/step - loss: 0.0976 - categorical_accuracy: 0.9767 - top_5_acc: 0.9993 - val_loss: 0.7982 - val_categorical_accuracy: 0.7638 - val_top_5_acc: 0.9452\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 0.69535\n",
      "\n",
      "Epoch 00017: ReduceLROnPlateau reducing learning rate to 0.0003999999724328518.\n",
      "Epoch 18/25\n",
      "127/127 [==============================] - 8411s 66s/step - loss: 0.0932 - categorical_accuracy: 0.9782 - top_5_acc: 0.9989 - val_loss: 0.6887 - val_categorical_accuracy: 0.7820 - val_top_5_acc: 0.9551\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.69535 to 0.68874, saving model to best_model.h5\n",
      "Epoch 19/25\n",
      "127/127 [==============================] - 9100s 72s/step - loss: 0.0786 - categorical_accuracy: 0.9840 - top_5_acc: 0.9996 - val_loss: 0.6285 - val_categorical_accuracy: 0.7962 - val_top_5_acc: 0.9579\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.68874 to 0.62855, saving model to best_model.h5\n",
      "Epoch 20/25\n",
      "127/127 [==============================] - 8463s 67s/step - loss: 0.0809 - categorical_accuracy: 0.9821 - top_5_acc: 0.9996 - val_loss: 0.7402 - val_categorical_accuracy: 0.7993 - val_top_5_acc: 0.9583\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 0.62855\n",
      "Epoch 21/25\n",
      "127/127 [==============================] - 8389s 66s/step - loss: 0.0732 - categorical_accuracy: 0.9849 - top_5_acc: 0.9991 - val_loss: 0.7045 - val_categorical_accuracy: 0.8081 - val_top_5_acc: 0.9631\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.62855\n",
      "Epoch 22/25\n",
      "127/127 [==============================] - 8878s 70s/step - loss: 0.0780 - categorical_accuracy: 0.9822 - top_5_acc: 0.9998 - val_loss: 0.4931 - val_categorical_accuracy: 0.8240 - val_top_5_acc: 0.9646\n",
      "\n",
      "Epoch 00022: val_loss improved from 0.62855 to 0.49306, saving model to best_model.h5\n",
      "Epoch 23/25\n",
      "127/127 [==============================] - 8362s 66s/step - loss: 0.0738 - categorical_accuracy: 0.9844 - top_5_acc: 0.9996 - val_loss: 0.7243 - val_categorical_accuracy: 0.8200 - val_top_5_acc: 0.9648\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 0.49306\n",
      "Epoch 24/25\n",
      "127/127 [==============================] - 8324s 66s/step - loss: 0.0736 - categorical_accuracy: 0.9834 - top_5_acc: 0.9998 - val_loss: 0.9385 - val_categorical_accuracy: 0.8265 - val_top_5_acc: 0.9643\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 0.49306\n",
      "Epoch 25/25\n",
      "127/127 [==============================] - 8677s 68s/step - loss: 0.0760 - categorical_accuracy: 0.9833 - top_5_acc: 0.9995 - val_loss: 0.4288 - val_categorical_accuracy: 0.8281 - val_top_5_acc: 0.9636\n",
      "\n",
      "Epoch 00025: val_loss improved from 0.49306 to 0.42880, saving model to best_model.h5\n",
      "Validation loss:  0.5778400897979736\n",
      "Validation acc:   0.827875018119812\n"
     ]
    }
   ],
   "source": [
    "train_steps = len(train_generator.filenames) // batch_size\n",
    "validation_steps = len(validation_generator.filenames) // batch_size\n",
    "history = model.fit_generator(train_generator,\n",
    "                            steps_per_epoch= train_steps,\n",
    "                            epochs= epochs,\n",
    "                            validation_data= validation_generator,\n",
    "                            validation_steps= validation_steps,\n",
    "                            callbacks= get_callbacks_list(early_stopping_patience, reduce_lr_on_plateau_factor, reduce_lr_on_plateau_patience))\n",
    "\n",
    "validation_score = model.evaluate_generator(validation_generator, steps= validation_steps)\n",
    "print('Validation loss: ', validation_score[0])\n",
    "print('Validation acc:  ', validation_score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PHXQT/rXXXsukSZOor6/n3HPP5Wc/+xllZWVcfPHF7Nixg2nTpjFlyhQuueQSAE455RQefvhhZs6cmftN+A9dD7ubYc7Hk8+rjkoe3YgvSdLLhLYVlnw2e/bsuHDhwhd9benSpRx77LEpJSoOWfsdtu6Db82CfkPhQ3964ev/OQkmnApv/k7X30OSpAIRQngwxjj7cM9xBUxdt+zWZKVr7gUv/np1HWzxFKQkSS9lAVPX3fs9GDgajnnDi79eXecpSEmSDqKgC1ghnD7NV1n73a15GJ77Bxx/PpS/5KLa6jHQ1AAt7b81kyRJpaBgC1jv3r3ZuHGjJawTYoxs3LiR3r17d/1gC74LPfrBrPe//HvVdUCEplVdfx9JkopIwc4BGzVqFA0NDaxf76DPzujduzejRo3q2kG2roUlv4bZH4A+VS//ftWY5HHzCqgZ//LvS5JUogq2gPXo0YOxY8emHaO0PfBDaG2BEz5y8O+3zQJzI74kSS9SsKcglbK9O2Hh1TDp9EOvblXWQnlPN+JLkvQSFjB1ziO/hB0bYc4Fh35OWVkykNUCJknSi1jA1HExJqMnhk2Dulcd/rnVdd6OSJKkl7CAqeOe+SusX5oMXg3h8M+tGuMKmCRJL2EBU8ct+G5y26Gpbzvyc6vrYNcW2Lml22NJklQoLGDqmPXLYPmf4BUfgopeR36+V0JKkvQyFjB1zH3fh/Jeyeyv9qg+YBaYJEkCLGDqiB2bYPENMP0d0H9I+17TtgLmRnxJkvazgKn9HrwGWnYefvTES/UeCL2rXAGTJOkAFjC1z769cP//wrhXw7ApHXttdZ0FTJKkA1jA1D6P3QRb13Rs9atNdZ2b8CVJOoAFTEcWI9z7Hag5Gia8tuOvrx4DW1ZCa2v2s0mSVIAsYDqyVffB6odgzkeT2wt1VHUd7NuTrKBJkiQLmNphwXeSjfQz3tO511c5ikKSpANZwHR4m1fAE7+H486Fnv06d4z9oyhWZCeTJEkFzgKmw7v/f4EAx5/f+WMMHA2hzI34kiRldFsBCyFcHUJYF0JYcsDXBoUQ/hRCeCrzWN1d768s2L0VFv0YprwZBo7s/HEqesKAka6ASZKU0Z0rYNcC81/ytYuAP8cYjwb+nPlc+eqhn8LuZpjz8a4fq7rOafiSJGV0WwGLMd4FbHrJl98EXJf5+Drgzd31/uqi1n3JfR9HnwCjjuv68arGuAImSVJGrveADYsxrgHIPA491BNDCOeHEBaGEBauX78+ZwGVsezWpDDN+Vh2jlddB9vWwt6d2TmeJEkFLG834ccYr4oxzo4xzh4ypJ03flb23PtdGHgUHPOG7Byv7UrILSuzczxJkgpYrgvY8yGEWoDM47ocv7/aY/VieO5uOOF8KK/IzjGrnQUmSVKbXBew3wHnZD4+B/i/HL+/2uPe70GPfjDz7Owd01lgkiTt151jKG4AFgCTQggNIYQPApcDrw0hPAW8NvO58snWtbDk1zDzLOhTlb3j9hsCPfp6JaQkSUCWzi+9XIzxUPeteU13vaey4IEfQmsLnPCR7B43BK+ElCQpI2834SsFe3fCwqth0ulQMz77x6+ucxq+JElYwHSgR34BOzbCnAu65/jVmRWwGLvn+JIkFQgLmBIxJpvvh0+Duld1z3tU18GebUnJkySphFnAlHj6L7D+iWT1K4TueY/9V0J6GlKSVNosYErc+13oNxSmvq373qOqbRbYs933HpIkFQALmGD9Mlh+Bxz/Yajo1X3v0zaM1Y34kqQSZwFTsvervBccd173vk/Pfsk8MEdRSJJKXLfNAVMBaN2XDF19+Ocw/Z3QPwf33Kyus4BJkkqeBawUtRWvO78GG5fD0Clw4r/m5r2r62DV/bl5L0mS8pQFrJTsa0mK111ff6F4vfPHcMwboCxHZ6OrxsCS3yRZsnWjb0mSCoz/BSwFLy1ew6bCO38Cx7w+d8WrTXUdxH3Q3PDCWApJkkqMBayY5VPxatN2JeTmFRYwSVLJsoAVo3wsXm32D2NdkWYKSZJSZQErJvtaYMmNcOfXYdPTSfF6109h0pnpF682A0ZCWYXT8CVJJc0CVgwKoXi1KSuHgaNdAZMklTQLWCF7WfGalr/F60DOApMklTgLWCEq1OLVpnoMLL057RSSJKXGAlZoHv5FMkB1f/G6HiadURjFq011HezYCLu3Qq/KtNNIkpRzFrBCsuIf8NvzC7d4tdl/JeRzMHxqqlEkSUqDBayQrH8ieXzfr2BAbbpZuqLqgFlgFjBJUgkqwOWTEtbUmIxw6D807SRd4ywwSVKJs4AVkuZGqByRjHIoZH2qodcA2OIsMElSabKAFZKmRhgwIu0UXRdCciWkK2CSpBJlASskzQ0wcGTaKbKjus5p+JKkkmUBKxQxQvPq5FY+xaBqTHIKsrU17SSSJOWcBaxQbN8A+/bAwFFpJ8mO6jpo2QXbnk87iSRJOWcBKxTNDcljsayAtV0J6UZ8SVIJsoAViqbG5LGY9oCBG/ElSSXJAlYomjMFrFhWwAaOBoIFTJJUkixghaKpAcp7Qt/BaSfJjh69obLWKyElSSXJAlYomlcnM8AK8d6Ph1Jd5wqYJKkkFdF/zYtccyMMKJIrINtU17kJX5JUkixghaKpsXg24LepHpOs7O3dlXYSSZJyygJWCFr3wdYiGsLaproOiNC0Ku0kkiTllAWsEGxbB60txXEfyANVjUke3YgvSSoxFrBC0DaColim4LfZPwvs2VRjSJKUaxawQlBsM8Da9B8GFb3diC9JKjkWsELQVKQrYGVlUHWUoygkSSXHAlYImhuhog/0qU47SfY5C0ySVIIsYIWgqSEZQRFC2kmyr2pMsgk/xrSTSJKUMxawQtDcWHxXQLaproPdzbBzc9pJJEnKGQtYIWgqwin4bfZfCbkizRSSJOWUBSzf7WuBbWuLbwp+m+rMLDCvhJQklRALWL7bthZia/GNoGizfxjrilRjSJKUSxawfFesIyja9B4AfQY5DV+SVFIsYPmuuSF5LNYVMHAUhSSp5FjA8l3bClixXgUJFjBJUsmxgOW75kbo2R96D0w7SfepHgNNq6B1X9pJJEnKCQtYvmtuTE4/FuMQ1jbVddDa8sI9LyVJKnIWsHzX1Fi8Iyja7L8S0o34kqTSYAHLd20rYMXMYaySpBJjActnLXtg27riHUHRZuAoCOUWMElSybCA5bOtq4FY3FdAApT3SE6zOg1fklQiLGD5bP8IiiI/BQmOopAklRQLWD5rXp08FvspSEg24lvAJEklwgKWz0phCn6b6jrYvh72bE87iSRJ3c4Cls+aGpMBrL36p52k++2/EtJ9YJKk4mcBy2fNjTCgBE4/wgsFzI34kqQSYAHLZ00NxX8FZBtngUmSSogFLJ81l8AU/DZ9a6BHPwuYJKkkWMDy1d5dsGNj6ZyCDCEzisJTkJKk4mcBy1dtN6YulRUwcBaYJKlkWMDyVXMJDWFtUz0m2YQfY9pJJEnqVhawfNU2Bb8UhrC2qa6DvTuSeWCSJBUxC1i+ahvCWlmbbo5c8kpISVKJsIDlq6ZG6DMIevZNO0nuVI1JHt2IL0kqchawfNW8urQ24ANUHZU8ugImSSpyFrB8VUpT8Nv07Av9h8GWFWknkSSpW1nA8lVTQ+mtgIGzwCRJJcEClo/2bIddW0prBEUbZ4FJkkqABSwfNZXgDLA2VWOS068te9JOIklSt7GA5aO2ERSlegoytkLTqrSTSJLUbSxg+ah5dfJYiitg1W2jKFakGkOSpO5kActH+09Bjkg3RxrahrFucSO+JKl4WcDyUXMD9BsKFb3STpJ7lbVQ3tMVMElSUbOA5aOmxtLc/wVQVg4DRzuKQpJU1Cxg+ai5sTT3f7VxFIUkqchZwPJRU6kXsDEWMElSUbOA5ZtdzbBna+megoRkBWzXFti5Je0kkiR1CwtYvmku4SGsbbwSUpJU5Cxg+aZtBMXAErsR94Gq2maBWcAkScXJApZv2qbguwLmPjBJUtGygOWbpkYgQOXwtJOkp08V9B5oAZMkFS0LWL5pbkzKV3mPtJOkq7rOPWCSpKKVSgELIXwqhPBYCGFJCOGGEELvNHLkpVKfAdbGWWCSpCKW8wIWQhgJ/AswO8Y4FSgH3p3rHHmrlKfgH6hqDGxZCa2taSeRJCnr0joFWQH0CSFUAH2B1SnlyC8xZlbASvgKyDbVdbBvD2xdk3YSSZKyLucFLMbYCPwnsBJYAzTFGG9/6fNCCOeHEBaGEBauX78+1zHTsXMz7N3hChgk0/DB05CSpKKUxinIauBNwFhgBNAvhHDWS58XY7wqxjg7xjh7yJAhuY6Zjv1DWEekmyMfVI9NHt2IL0kqQmmcgjwVeDbGuD7GuBf4DfDKFHLkn7YhrJ6ChIGjgeAKmCSpKKVRwFYCc0IIfUMIAXgNsDSFHPmnbQXMU5BQ0TO5GtQCJkkqQmnsAbsPuBFYBDyayXBVrnPkpeZGKKuA/sPSTpIfquu8HZEkqShVpPGmMcaLgYvTeO+81tQIlbVQVp52kvxQXQfL70g7hSRJWeck/HziENYXqx4D29bC3p1pJ5EkKassYPmkqcErIA/UdlPuLStTjSFJUrZZwPJFjNC82g34B6pyFpgkqThZwPLFjo2wb7cjKA7UtgLmRnxJUpGxgOWLpobk0RWwF/QfChV9XAGTJBUdC1i+2D8F3wK2XwjJRnwLmCSpyFjA8kXbFPyBnoJ8keo6b0ckSSo6FrB80dwAZT2g7+C0k+SXqswKWIxpJ5EkKWssYPmiqTEZQVHmX8mLVNfBnm2wY1PaSSRJyhr/a58vmld7+vFg9l8JuSLNFJIkZZUFLF80N7gB/2Cq22aBPZtuDkmSssgClg9aW6F5jSMoDqZtGKsb8SVJRcQClg+2r4PWva6AHUyv/smFCZ6ClCQVEQtYPmhyBthhVdc5DV+SVFQsYPmg2Sn4h1Vd5wqYJKmoWMDyQfPq5NH7QB5c9ZjkVk37WtJOIklSVljA8kFTA1T0hr6D0k6Sn6rrIO57YaVQkqQCZwHLB82Nyf6vENJOkp/aroT0NKQkqUhYwPJBU6P7vw5n/zBWN+JLkoqDBSwftK2A6eAGjISyClfAJElFwwKWtn0tsHWNBexwyiuS2zRZwCRJRcIClrZtz0Ns9RTkkVTXOQ1fklQ0LGBpa24bwuoIisOqGuMKmCSpaFjA0tbkENZ2qa6DHRth99a0k0iS1GUWsLQ1exuidvFKSElSEbGApa2pEXr0g94D006S36qdBSZJKh4WsLQ1NySnHx3CenjVY5NHN+JLkoqABSxtzas9/dgefaqh1wBYuyTtJJIkdZkFLG1OwW+fEGDqW+HhG2D5n9NOI0lSl1jA0tSyJ5kD5giK9jntqzD0WPjNh5OVQ0mSCpQFLE1b1wDRFbD26tkX3nEd7N0FN34A9u1NO5EkSZ1iAUvT/hEUI9LNUUiGTIQ3XAErF8Bfvph2GkmSOsUClqYmp+B3yvR3wOwPwN1XwLJb004jSVKHWcDS1LYC5inIjjvtqzB8Ovz2ow5nlSQVHAtYmpoboddA6FWZdpLC06M3vPO65Ebmvzo3uaBBkqQCYQFLkyMoumbQOHjTd2D1IvjTF9JOI0lSu1nA0tTc4BDWrpr8RphzAdz3fXjsprTTSJLULhawNDU1egVkNpx6KYycDf/3Cdj4dNppJEk6IgtYWvbugh0bYKBXQHZZRU94x7VQXgG/Ogf27kw7kSRJh2UBS8vWzCR3T0FmR9VoeMsPYO2j8MeL0k4jSdJhWcDS0uQIiqybeBq86lPw4LXw8C/STiNJ0iFZwNLS7BDWbnHy5+GoV8LvPwnrnkg7jSRJB2UBS0tTQ/LoJvzsKq+At18NPfom+8H2bE87kSRJL2MBS0tzI/SpTm4wrewaUAtv+yGsXwa//zTEmHYiSZJexAKWlqZGTz92p/Enw0n/Do/8HB76SdppJEl6EQtYWppXuwG/u530bzD2JLjlX2HtkrTTSJK0nwUsLU7B735l5cmpyN5V8Mv3w67mtBNJkgRYwNKxZwfs3OwKWC70H5psyt/8LNx8ofvBJEl5wQKWBkdQ5FbdPDjlC/DYb+CBH6adRpIkC3T5Ft4AACAASURBVFgqHEGRe/M+CUe/Dm77/6BxUdppJEklzgKWhman4OdcWVlyq6J+Q5P5YDs3p51IklTCLGBpaPY+kKnoOyi5aXfzarjp4+4HkySlxgKWhqYG6DcEKnqlnaT0jH4FvPaLsOwPsOA7aaeRJJUoC1gamhtd/UrTnI/BMa+HOy6GlfelnUaSVIIsYGloaoSBXgGZmhDgTd9J/g5uPA+2b0w7kSSpxFjA0tDc6BWQaetTBe+4Dravhz9elHYaSVKJsYDl2q5m2N3sKch8MKIepr4dnvmrG/IlSTllAcu1tisgPQWZH0bUJ6tgW9emnUSSVEIsYLnW3DaE1RWwvFA7I3lc83C6OSRJJcUClmtNDmHNK8OmAsECJknKKQtYrjU3AgEqa9NOIoBe/WHw0bBmcdpJJEklxAKWa02N0H8YlPdIO4na1M5wBUySlFMWsFxrbvD0Y76prU9WJretTzuJJKlEWMByrXm1G/DzTdtG/LWugkmScsMClksxOgU/Hw2fljx6GlKSlCMWsFzatQX2bncFLN/0qYLqsRYwSVLOWMByyREU+cuN+JKkHLKA5VJzpoC5ApZ/amfA5hWwc3PaSSRJJcAClktNTsHPW/sn4j+Sbg5JUkmwgOVS82oI5VA5PO0keqna+uTR05CSpBywgOVSc2MyAb+sPO0keql+NTBwtAVMkpQTFrBcanIIa15zI74kKUcsYG32tXT/ezQ3woAR3f8+6pzaGbBxOezemnYSSVKRs4AB/P2/4erXQWtr971HjE7Bz3e1M4AIa5eknUSSVOQsYJDs/Wl8EJb8uvveY8dGaNnlFPx8tv9KyMXp5pAkFT0LGMDUtyW3o/nLZdCyu3vewxlg+a9yOPQf5j4wSVK3s4ABlJXBqZfClpWw8JrueQ+n4BeG2noLmCSp21nA2ow/BcaeCHd9HXY1Z//4+1fAPAWZ12pnwPonYM+OtJNIkoqYBaxNCHDqJclerXu+lf3jNzVAWQ/oNyT7x1b21M6A2ArrHk87iSSpiFnADjTyOJjyFljwbdj6fHaP3dwIA2qT053KX27ElyTlgG3gpU75AuzbA3d+LbvHbWr09GMhGDgK+gyC1RYwSVL3sYC9VM14OO5cePBa2LA8e8dtbnQDfiEIwYn4kqRuZwE7mBP/DSp6w1++mJ3jtbY6hLWQjKiHdUu7bySJJKnkpVLAQghVIYQbQwhPhBCWhhDmppHjkCqHwSs/AY/flAxo7art66F1r0NYC0XtjOTva93StJNIkopUWitgVwB/jDEeA8wA8u+/dHM/AX0Hw58uTm4j1BXNDcmj94EsDPs34nsaUpLUPXJewEIIA4ATgR8BxBj3xBi35DrHEfUeACf9G6z4Ozz9564dq8kp+AWleiz0GmgBkyR1mzRWwMYB64FrQggPhRB+GELo99InhRDODyEsDCEsXL9+fe5TAhx3HlSNgT9d0rUbdbcNYfUUZGEIAWqnW8AkSd0mjQJWAcwCvhdjnAlsBy566ZNijFfFGGfHGGcPGZLS8NKKnslYiucfhSU3dv44zY3Jpv6+NdnLpu5VOwOeXwL7WtJOIkkqQmkUsAagIcZ4X+bzG0kKWX7af6PuL3b+qrimxmT/VwjZzabuUzsDWnbBhmVpJ5EkFaGcF7AY41pgVQhhUuZLrwHy974vL7pR99WdO0Zzo/u/Ck1tffLoaUhJUjdI6yrIfwauDyE8AtQDX0kpR/uMPwXGngR3fh12NXX89U0WsIJTMx569LOASZK6RSoFLMa4OLO/a3qM8c0xxs1p5Gi3tht179zU8Rt1t+6DrWucgl9oysqTU88WMElSN3ASfnuNnAVT3goLvgNb17b/dVvXQtznClghqp0Bax7p2hWwkiQdhAWsI075fOZG3V9v/2uaVyePjqAoPLUzYO922JjFe4JKkoQFrGNqxiezwTpyo+79U/BdASs4TsSXJHUTC1hHndTBG3W3TcF3D1jhGXJM8ne9ZnHaSSRJRcYC1lH9h8Ir/zm5UXdDO27U3dwIPfpC76ruz6bsKq+AYVNcAZMkZZ0FrDNemblR9x3tuFF3U0Ny+tEhrIWpbSN+V2/ILknSASxgndGr8oUbdS8/wo26mxs9/VjIamfA7ibYvCLtJJKkImIB66y2G3XfcfHhxxQ0r4YBXgFZsNyIL0nqBhawzqroCa/5j+SGzY/+6uDP2bc3mQPmCljhGjoZyirciC9JyioLWFdMeSsMnw5/+dLBb9S9dQ0QHUFRyCp6wdBjXQGTJGWVBawrysrgtZdC00p44Ecv/37bCAoLWGGrrU8KmBvxJUlZYgHrqvGnwLhXw13fePmNupudAVYUamfAjo0v/H1KktRFFrBsOPWSg9+ou8kp+EWhtj559DSkJClLLGDZMGImTH3by2/U3bwaeg2A3gPSy6auGzYFQpkFTJKUNe0qYCGE8SGEXpmPXx1C+JcQgqPdD7T/Rt1fe+FrzY2ufhWDnn1h8CRY7ZWQkqTsaO8K2K+BfSGECcCPgLHAz7otVSEaNC5zo+7rXrhRd1OD+7+KRe0MV8AkSVnT3gLWGmNsAd4CfDPG+CmgtvtiFaj9N+q+LPm8uREGjEg3k7KjdgZsW/viU8ySJHVSewvY3hDCe4BzgN9nvtajeyIVsP036v4/WHE3bF/vFPxiMaJtI/4j6eaQJBWF9haw84C5wJdjjM+GEMYCP+2+WAWs7UbdN/9L8rmnIIvD8GnJo6chJUlZ0K4CFmN8PMb4LzHGG0II1UBljPHybs5WmHpVwkn/Dhsz+8DchF8celVCzQRvSSRJyor2XgX5txDCgBDCIOBh4JoQwn93b7QCdty5UF2XfDzQU5BFo3aGpyAlSVnR3lOQA2OMzcBbgWtijMcBp3ZfrAJX0RNO/waMngNVR6WdRtlSOyO57dSOTWknkSQVuPYWsIoQQi3wTl7YhK/Dmfg6+OBtyc2cVRxqZySPnoaUJHVRewvYZcBtwNMxxgdCCOOAp7ovlpSH9hcwN+JLkrqmoj1PijH+CvjVAZ8/A7ytu0JJealPNVSNsYBJkrqsvZvwR4UQfhtCWBdCeD6E8OsQgrvLVXqciC9JyoL2noK8BvgdMAIYCdyc+ZpUWmpnwKZnYFdT2kkkSQWsvQVsSIzxmhhjS+bPtcCQbswl5afazET8tY+mm0OSVNDaW8A2hBDOCiGUZ/6cBWzszmBSXqqdnjyu9kpISVLntbeAfYBkBMVaYA3wdpLbE0mlpf9QqBzhPjBJUpe091ZEK2OMb4wxDokxDo0xvplkKKtUekbUW8AkSV3S3hWwg/l01lJIhaR2Bmx4EvZsTzuJJKlAdaWAhaylkApJ7QwgwtolaSeRJBWorhSwmLUUUiFxIr4kqYsOOwk/hLCVgxetAPTplkRSvqushX5DvCekJKnTDlvAYoyVuQoiFYwQnIgvSeqSrpyClEpX7QxYtxT27ko7iSSpAFnApM6orYe4D9Y9lnYSSVIBsoBJneFGfElSF1jApM6oOgp6V1nAJEmdYgGTOsON+JKkLrCASZ1VOwOefwxa9qSdRJJUYCxgUmfVzoB9e2D9E2knkSQVGAuY1FkjZiaPnoaUJHWQBUzqrOqx0LPSAiZJ6jALmNRZZWVQO90CJknqMAuY1BW1M2Dto9C6L+0kkqQCYgGTuqJ2BrTshA1Ppp1EklRALGAZLfta046gQuREfElSJ1jAgK//8Qle8993EmNMO4oKTc3RUNHHAiZJ6hALGDB8YG+e27iDVZt2ph1Fhaa8AoZPs4BJkjrEAgbMHVcDwIJnNqScRAWpdgaseQRaPY0tSWofCxgwYWh/BvfvxYKnN6YdRYWodgbs2Qqbn007iSSpQFjAgBACc8YNYsEzG90Hpo7bvxF/cbo5JEkFwwKWMXd8Dc837+bZDdvTjqJCM+QYKO8Jqy1gkqT2sYBlvLAPzNOQ6qCKnjB0shvxJUntZgHLGDu4H8MGuA9MnTSiPilgnsKWJLWDBSwjhMDccTXc+8wm94Gp42pnwK4tsGVl2kkkSQXAAnaAueNr2LBtN8vXbUs7igqNE/ElSR1gATvA3HGDAfeBqROGToFQbgGTJLWLBewAowf1YWRVH/eBqeN69IahxzqKQpLULhawAyTzwGq495mNtLa6D0wdVDsjGUXhHkJJ0hFYwF5i7vgaNu/Yy7Lnt6YdRYWmdgbs2ABb16SdRJKU5yxgLzF3fGYemKch1VG19cmj+8AkSUdgAXuJkVV9OGpQXzfiq+OGTwWCBUySdEQWsIOYO66G+57ZyD73gakjevaDwRMtYJKkI7KAHcTc8TU072ph6ZrmtKOo0NTOsIBJko7IAnYQczL3hbzX05DqqNoZ0NwI29annUSSlMcsYAcxfGBvxg7u50Z8dZwT8SVJ7WABO4Q542q4/9lNtOxrTTuKCknt9OTRgaySpMOwgB3C3PE1bN3dwmOr3QemDug9EAaNcwVMknRYFrBDmDNuEOB9IdUJtTOgcZET8SVJh2QBO4Shlb2ZMLS/+8DUcWNPguYGWP9E2kkkSXnKAnYYc8fV8MCKTex1H5g6YuJpyeOyW9PNIUnKWxaww5g7voYde/bxSENT2lFUSAaMSE5DPnlb2kkkSXnKAnYYzgNTp008HRruh+3+25EkvZwF7DAG9evJMcMr3Qemjpt4GsRWWP6ntJNIkvKQBewI5oyrYeFzm9jdsi/tKCoktfXQf7j7wCRJB2UBO4K542vYtbeVh1e5D0wdUFaWrIIt/zO07Ek7jSQpz1jAjmDO2BpCwNOQ6riJ82HPVlh5T9pJJEl5xgJ2BAP79mBy7QAWPLMh7SgqNONeDRW9Ydkf004iScozFrB2mDuuhkUrt7Brr/vA1AE9+8LYE+HJW52KL0l6EQtYO8wdX8OellYWrdycdhQVmonzYfMK2PBk2kkkSXnEAtYOrxg7iLIA97oPTB01cX7y6NWQkqQDpFbAQgjlIYSHQgi/TytDew3o3YNpIwd6Y2513MCRMHwaPOk+MEnSC9JcAbsQWJri+3fInPE1LF61hZ173AemDpp4Oqy6D3ZsSjuJJClPpFLAQgijgDOBH6bx/p0xd1wNe/dFFj7nf0TVQZPmJ1Pxn3IqviQpkdYK2DeBfwNaD/WEEML5IYSFIYSF69evz12yQ3hF3SAqyoLzwNRxtTOh/7DkakhJkkihgIUQXg+sizE+eLjnxRivijHOjjHOHjJkSI7SHVq/XhVMH+U+MHVCWRkc/bpkKv6+vWmnkSTlgTRWwOYBbwwhrAB+DpwSQvhpCjk6bO74Gh5paGLb7pa0o6jQTDoddjfDc07FlySlUMBijP8vxjgqxlgHvBv4S4zxrFzn6Iy54wazrzXywAr3gamDxr0aynt5NaQkCXAOWIccN6aaHuXBeWDquJ79kqn4y5yKL0lKuYDFGP8WY3x9mhk6ok/PcmaOrnYfmDpn0nzY/CxseCrtJJKklLkC1kFzxtewpLGJ5l1uplYHHX1a8ujVkJJU8ixgHTR3XA2tEe5/xn1g6qCq0TBsGjx5W9pJJEkps4B10MyjquhZUeZpSHXOpPmw8l6n4ktSibOAdVDvHuUcd1S1A1nVORPnQ9yXzASTJJUsC1gnzB1fw9K1zWzZsSftKCo0I2ZBvyHuA5OkEmcB64S542uIEe571tNI6qCysmQz/lN3OBVfkkqYBawTZoyqok+Pck9DqnMmzYfdTcleMElSSbKAdULPijJm11Vzrxvx1RnjTobynk7Fl6QSZgHrpDnjanhi7VY2btuddhQVml79oe6fkqn4kqSSZAHrpLnjawD3gamTJp0Om56GDcvTTiJJSoEFrJOmjRxIv57uA1MnTXQqviSVMgtYJ/UoL2N23SAHsqpzqo6CoVNgmfvAJKkUWcC6YO74Gpav28a6rbvSjqJCNGk+rFwAOzennUSSlGMWsC6YOy7ZB3av94VUZ0w83an4klSiLGBdMGXEACp7VbgPTJ0zchb0HezVkJJUgixgXVBRXsbxYwc5D0ydU1aebMZf/ifY15J2GklSDlnAumju+Bqe3bCdtU3uA1MnTJwPu5pglVPxJamUWMC6aE5mH9iCZzaknEQFaXxmKr6nISWppFjAumhy7QAG9unhPjB1Tq9KqHsVPHlb2kkkSTlkAeuisrLACWOdB6YumHg6bHwKNj6ddhJJUo5YwLJg7vgaVm3aScPmHWlHUSHaPxXfoaySVCosYFnQdl9IT0OqU6rHwNDJ7gOTpBJiAcuCiUMrGdSvp6ch1XkTT8tMxd+SdhJJUg5YwLKgrCwwZ9wg7n16IzHGtOOoEE08HVpb4Gmn4ktSKbCAZcnccTWsbtrFyk3uA1MnjJoNfWu8ObcklQgLWJa4D0xdUlYOR78OnrrdqfiSVAIsYFkyfkh/hlT2ch+YOm/ifNi1BRruTzuJJKmbWcCyJITAnHE1LHAfmDpr/ClQ1sOrISWpBFjAsmjuuBrWbd3NMxu2px1Fhaj3AKib5zwwSSoBFrAsch+Yumzi6bDhSafiS1KRs4BlUV1NX4YP6O0+MHXe/qn43htSkoqZBSyLQgjMHV/Dfc+4D0ydNGgsDDkGnnQfmCQVMwtYls0dV8OGbXt4at22tKOoUE2cD8/dA7ua0k4iSeomFrAscx+YumxSZir+cqfiS1KxsoBl2ehBfRlZ1ccCps4b9QroM8irISWpiFnAusHc8TXc++xGWlvdB6ZOOHAqfuu+tNNIkrqBBawbzB1Xw5Yde1n2/Na0o6hQTTwNdm6GVU7Fl6RiZAHrBu4DU5dNeA2UVXg1pCQVKQtYNxhR1YcxNX2dB6bO6z0QxsxzHpgkFSkLWDeZOy6ZB7bPfWDqrInzYf0TsOnZtJNIkrLMAtZNTj5mKM27Wrhj6fNpR1GhmjQ/eXQVTJKKjgWsm7zmmKGMrOrDNXe7eqFOGjQOBk9yH5gkFSELWDepKC/j/XPHcO8zm1i6pjntOCpUE0+DFXfDLv8NSVIxsYB1o3e/4ij69Ch3FUydN+l0aN0LT/8l7SSSpCyygHWjgX178NZZI7lp8Wo2btuddhwVolHHQ59qp+JLUpGxgHWz8+bVsaellRvuX5l2FBWi8gqY8Fqn4ktSkbGAdbMJQyv5p6MH85N7n2Pvvta046gQTZoPOzZCw8K0k0iSssQClgMfmDeW55t3c8uja9KOokI03qn4klRsLGA5cNLEIYwb3I9r7l6RdhQVoj5VcNRceOIWT0NKUpGwgOVAWVng3Hl1LF61hUUrN6cdR4Vo+jthwzK49vWwxf2EklToLGA58rZZo6jsXeEqmDpn5tnwlh/A2kfhe/PgkV+lnUiS1AUWsBzp16uCd80eza2PrmFt066046jQhAAz3g0f+wcMnQy/+RD8+kOwc0vaySRJnWABy6FzXllHa4z85N4VaUdRoaqug3P/ACd/Dpb8Br7/qmRSviSpoFjAcmj0oL6ceuwwfnbfSnbtdTO1Oqm8Ak76N/jg7cnVkdeeCXdcCi170k4mSWonC1iOnTdvLJt37OX/FjemHUWFbtRs+Og/YOZZ8I//hh+9FjY8lXYqSVI7WMBybM64QRwzvJJr7l5BjDHtOCp0vfrDm74N7/wJbHkOfnAiLLwa/LclSXnNApZjIQQ+MG8sT6zdyoJnNqYdR8Vi8hvhYwtg9Anw+0/Bz98L2zeknUqSdAgWsBS8sX4Eg/r1dCSFsmtALZz1Gzjtq7D8DvjuXHjqT2mnkiQdhAUsBb17lPPe44/ijqXPs3LjjrTjqJiUlcHcC+DDf4V+g+H6t8Mt/wp7d6adTJJ0AAtYSs6eO4byELhuwYq0o6gYDZ+alLATPgb3XwVXvRrWPJJ2KklShgUsJcMG9ObM6bX88oFVbNvdknYcFaMeveH0y+GsX8POzfDD18A934LW1rSTSVLJs4Cl6Lx5Y9m6u4UbF65KO4qK2YRTkw36R78Obv88/OTN0Lw67VSSVNIsYCmqH13FzKOquG7Bc7S2OjZA3ahfDbzrp/CGK6HhgWSD/mM3pZ1KkkqWBSxl580by7MbtvO3J9elHUXFLgQ47pxkeOugcfCrc+C3H4Otz6edTJJKjgUsZadPHc7wAb0dSaHcqRmf3MboxH+FR34BV8yA2z4H2/w/AZKUKxawlPUoL+PsuWP4+1MbeOr5rWnHUako7wGnfB4+8QBMeTPc+1345vRkj9i29Wmnk6SiZwHLA+85/ih6VZRxzT0r0o6iUlMzHt7yffj4A8k0/QXfgSumw+1fcJK+JHUjC1geGNSvJ2+uH8lvFjWwZceetOOoFA2eAG+9Ci64D445MxlX8c3p8KeLYbu3zJKkbLOA5YnzXlXHrr2t/PwBR1IoRUMmwtt+CB+/DybNh7uvSFbE7rgUdmxKO50kFQ0LWJ44ZvgA5o6r4cf3rKBln4MylbIhk+DtV8MFmflh//ifZEXsz1+0iElSFljA8sh58+pY3bSL2x5zLIDyxNBj4R3XwMfugQmvgb//Z1LE/vKlZLq+JKlTLGB55DXHDmP0oD5cc/ezaUeRXmzYZHjndUkRG38y3PWNpIj99auwc0va6SSp4FjA8kh5WeDcV45l4XObebShKe040ssNmwLv+kkyzHXsiXDn5UkR+9vlsMt/s5LUXhawPPOO2aPo17PcVTDlt+HT4N3Xw0f+DmP/Cf72VfjmNLjz67CrOe10kpT3LGB5ZkDvHrxj9mhufmQ167buSjuOdHi10zNF7C4YMw/++uXkqsnVi9NOJkl5zQKWh855ZR0trZHr712ZdhSpfWpnwHtugPP/BhW94XefgH0taaeSpLxlActDYwf34+RJQ7n+vufY3bIv7ThS+42YCad/DdY+mtzeSJJ0UBawPHXevDo2bNvD7x9ek3YUqWOOfSNMPD3ZF7b5ubTTSFJesoDlqVdNGMzRQ/tzzT3PEmNMO47UfiHAGd8AAvzhM+C/X0l6GQtYngohcO68OpY0NrPwOQdeqsBUjYZTPg/L/wSP/SbtNJKUdyxgeeytM0cxsE8PR1KoMJ3wEaith1svcmq+JL2EBSyP9elZzruPH80fl6ylYfOOtONIHVNWDm+4AnZsgDsuSTuNJOUVC1iee//cOkII/GSBm5lVgEbUw5wL4MFr4bkFaaeRpLxhActzI6v6cNqUYdxw/0p27HGukgrQq/8fDBwNv/8ktOxJO40k5YWcF7AQwugQwl9DCEtDCI+FEC7MdYZC84F5Y2ne1cJvFjWmHUXquF794cz/gvVPwN1XpJ1GkvJCGitgLcBnYozHAnOAj4cQJqeQo2AcN6aaaSMHcu09KxxJocI08TSY/Ga46xuwYXnaaSQpdTkvYDHGNTHGRZmPtwJLgZG5zlFIQgicN6+O5eu28fenNqQdR+qc07+W3Kbo9590NpikkpfqHrAQQh0wE7jvIN87P4SwMISwcP369bmOlnfOnF7L4P69HEmhwlU5HE69GFb8HR6+Ie00kpSq1ApYCKE/8GvgkzHG5pd+P8Z4VYxxdoxx9pAhQ3IfMM/0qijnrDlH8ddl63l41Za040idc9x5MPoEuO1zsH1j2mkkKTWpFLAQQg+S8nV9jNEx2e107ivrGFnVhwuuX8Sm7V5NpgJUVgav/ybsbobbP5d2GklKTRpXQQbgR8DSGON/5/r9C1lV3558932zWL91Nxf+/CH2tbqPRgVo2GSYd2FyGvKZv6WdRpJSkcYK2DzgbOCUEMLizJ8zUshRkGaMruKyN03h709t4H/+9GTacaTOOfFfYdA4+P2nYO/OtNNIUs6lcRXkP2KMIcY4PcZYn/lzS65zFLJ3H38U737FaL791+Xc/tjatONIHdejD7z+f2DTM3DXf6adRpJyzkn4BeqSN05h+qiBfOaXD/PM+m1px5E6btyrYfq74e5vwrqlaaeRpJyygBWo3j3K+e77ZlFRHvjoTx/0NkUqTKd9GXoNgJsvhNbWtNNIUs5YwArYqOq+fOs9s1i+bhv//utHnZKvwtNvMLzuS7DqPlh0bdppJClnLGAF7lVHD+Yzr5vEzQ+v5pq7V6QdR+q4+vdC3T/Bny6Bre5plFQaLGBF4IJXj+d1k4fxlVuWcv+zm9KOI3VMCMlssJZd8MeL0k4jSTlhASsCIQT+850zOGpQXy64fhHPN+9KO5LUMYMnwImfhcd+C0/ennYaSep2FrAiMaB3D75/9nHs2NPCBdcvYk+LG5pVYOZ9EgZPgj98BvZsTzuNJHUrC1gRmTiskq+9bToPPreZr9ziZf0qMBU94Q1XQNNK+OtX0k4jSd3KAlZk3jBjBB981ViuvWcFNz3UmHYcqWPGzIVZ58C934M1D6edRpK6jQWsCF10+jEcP3YQF/3mEZauaU47jtQxr70U+tZkZoPtSzuNJHULC1gR6lFexrffO5OBfXrw0Z8+SNPOvWlHktqvTzXM/yqsfgju/9+000hSt7CAFamhlb357vtmsXrLTj79i8W0tjqkVQVk6ttgwqnwly9CU0PaaSQp6yxgRey4MYP4wusn8+cn1vHtvy5PO47UfiHAmf+VnIK85d/STiNJWWcBK3JnzxnDW2aO5H/ueJK/LVuXdhyp/arr4OT/B8v+AEtvTjuNJGWVBazIhRD4ylumMWlYJRf+fDGrNu1IO5LUfnMugGHTklWwXV5QIql4WMBKQJ+e5fzg7OOIMfKRnzzIrr1eWaYCUd4D3ngFbF0Dd1ySdhpJyhoLWIkYU9OPb767nsfXNPO53y4hRjflq0CMPC5ZCVv4I1hxd9ppJCkrLGAl5JRjhnHha47m14sauP6+lWnHkdrvlM8le8J+98+wd2faaSSpyyxgJebC1xzNqycN4dKbH+OhlZvTjiO1T89+8IYrYdPT8Levpp1GkrrMAlZiysoC33xXPcMH9uZjP13Ehm27044ktc+4k2DW++Geb0HjorTTSFKXWMBKUFXfnnz/rOPYsvHzPwAAHWxJREFUvGMP//yzh2jZ15p2JKl9XvtF6D8sORXZsiftNJLUaRawEjVlxEC+8pZpLHhmI9+4bVnacaT26VMFZ/43PL8E7r4i7TSS1GkWsBL2tuNGcfacMfzgrmf42h+fcDyFCsMxZyS3Krrr67DuibTTSFKnWMBK3BdeP5l3zh7F9/72NGdc+XcWrtiUdiTpyE7/OvTsD7/7RHK7IkkqMBawEtezooyvv30GP/7A8eze28o7frCAS373GNt3t6QdTTq0foOTEtbwANz3g7TTSFKHWcAEwIkTh3D7p07knLl1XLdgBa/7n/+/vTuPj6q+9z/++s6WjX0JskhAQFFEFinKIqKt262irRvWXVut1rq1Vm79PW5t+/u13l7rdl2qVmytti7XeouK4lIVWVTAgoiyKARkTyAkISGTWb6/P74nySQkSCCZM0nez8djHnPmnDOTT3IY5j3f7/d8z1zmri7yuyyRpo08D4adBv/8Nexc53c1IiLNogAmtfKyQtw5bQQvXDuBrHCAy2Z+xE9fWEZpZczv0kT2ZgyceQ+YILx8E+jqDiLShiiAyV7GDerB7BtP4EcnDeGlf23iW/e+x+ufbvG7LJG9dR0Ap/4K1r0H//qL39WIiOw3BTBpVHY4yG2nDWfWDZPI75zFD5/+mOueXsL28iq/SxOpb+wVUDAZ5vwfKNMXBRFpGxTAZJ9G9OvK//5oEj87/QjeXrmdU+6Zy/8s2aiLeUvmCARg2gOQiMKrt6orUkTaBAUw+VrhYIDrpw5l9o0nMCy/Ez99YRmXP7mIjSWVfpcm4vQcAifdAatmw4q/+12NiMjXUgCT/TY0vxPPXzuBX04bweLCnZx271yeWlhIMqkWB8kAx18P/cbC7J9BxQ6/qxER2ScFMGmWQMBw+cRBvHHLFMYWdOc//rGCCx9byJdFu/0uTTq6YAjOfhCqSuH1GX5XIyKyTwpgckAGdM/lqavGc/f5o1i9bTdn3P8+D7/7hS7sLf7qMwJO+Aksfx5Wz/G7GhGRJimAyQEzxnDesQN489YpfHN4Pr97fRXnPDyfFZtL/S5NOrITfgK9j4RXboGqMr+rERFplAKYHLT8ztk8csmxPHLxWLaWRpn24Hz+a85KonFdo098EIrA2Q9B+RZ46xd+VyMi0igFMGkxZ4zsy1u3TuGc0f156J0v+cFTSxTCxB8DjnWD8hfPhMJ5flcjIrIXBTBpUd1yI/z+glH857kjmbu6iB//9V/ENC5M/HDSHdB9EMz6MVRryhQRySwKYNIqLvzGQH45bQRvfLaNW59fRkJTVUi6RXJh2n/DzrXw7m/9rsZ/VWWw6jV47XZ46Hi4ZwQseFDhVMQnIb8LkPbr8omDqIol+O1rK8kKBfjduccQCBi/y5KOZPAUOPYKWPggjDgH+h/rd0XpE6uCjR/B2vfctTI3fQw2AaFsGDgB8nrBG3fAggdg8q3u7xTO9rtqkQ7DtIVLyowbN84uXrzY7zLkAN331mrue2sNlx5fwK/OHoExCmGSRlWlrsUnpztc864bpN8eJROwZWld4NrwAcSrwASh/1g4bCoMPhEOHQ+hLPecwvnwzm9g/Tzo3NedQTr2srrtInJAjDFLrLXj9rmPApi0Nmstd72+kkffW8sPThjMz//tSIUwSa9Vr8HfpsPUn8PU2/2upmVYC8Wr6wJX4fsubALkH+XC1mEnQsEkyO6y79daN9cFsQ0LoUt/F8TGXNp+w6pIK9ufAKYuSGl1xhhmnD6cquoEj7+/jpxIiFtPOdzvsqQjOeIMOPo8mPtfcNQ0yD/S74oOTOkmF7ZqQlf5Fre+60A4cprXyjUFOuU373UHT4FBJ8Dad+Cd37qLms+7F6bcBqO/B8FwS/8mIk2r3AmL/ghblsFZD0BeT78rahVqAZO0SSYtM/7+Cc8v3sjtpw/nuqlD/C5JOpKKYnhoPHQfDFe/AYGg3xV9PWvdNBorXnKBa8cXbn1uTxeaalq5ug+GlmpVtha+eBve/Q1sWgLdCuDEn8Ex093lnkRaS9lmWPgQLH4SYhUQCEG/MXDZLHdSTRuiLkjJOImk5ZbnljJr2WbuPOsorpg02O+SpCNZ/j/w4tVw2m9gwo/8rqZpySSsmu1aoTYthnAeDJpUF7jyR0CglU9itxbWvOG6JrcsdSHvxNth5PkKYtKyitfA/Pth2bNgk3D0uTDpJncG8/OXweGnwYXPtKl/dwpgkpFiiSQ3/PVj5qzYxl3fHcn08QP9Lkk6CmvdWLC178H1C6DHYX5XVF+8Gpa/APPvc+O7uhW4D6LR34Nwjj81WevG0L37G9i6HHoOdUHs6HPbRiuiZK5NH7svGZ+/7E78GHMJTPyxm7+vxqI/wqveySFnPdByLb2tTAFMMlY0nuDavyzhvdVF3HvBaM4Z09/vkqSjKN0EDx8PfUfB5S9nxn/o1RXw8VNuXq6yjdDnaJh8Cxx1TuZ8608mYdWrbozY9hXQ63AXxEZ8t/Vb46T9sNZ1p8+7F9a+C1ldYfz34bgfNj128e1fw/t3w4kz4KR/T2u5B0oBTDJaVSzBlU8u4qPCnTx40RjOGNnX75Kko1j8JLxyM4y8AEae5wag+zHGpHInfPQYfPgH2FPizlicfAsM/VZmBMPGJJPw+Sx49y4o+txd+HzqDHcSgIKYNCWZgJWvuOC1+V/QqY8bBnDslV9/lq618I8bYOnTcOa9MO6q9NR8EBTAJONVRONc+sSHLN9UymOXjuOk4c08e0vkQFjrujWWPesG+4ay3fiqw0+FYadBt0Nb9+eXbnSDjZf8CWKVcPgZLngNPK51f25LSibhs5dcECte7VrtTvgJDP0mZHf1uzrJFPFq+OQ5N8Zrxxo3lnDSTTDqouZN/JuIwd8ugi/fhgufhuHfbr2aW4ACmLQJZVUxLn78Q1ZtK+fJK77BpKG9/C5JOop4FNbPh9VzYPXrUFLo1uePcGHs8NNhwDdabqxT0Wr3QfTJc26w8cjz3YdRn6Na5vX9kEzAp3+H9+5yZ2magAtjBZOgYAIMnAidevtdpaRbdLf7grHwISjfDIcc43Wrn33g76fqCvjTmbD9M3dmZAZ/YVEAkzajpKKa6Y99wIadlfzl6vGMG9TD75Kko7HWnY21Zo4LZBsWQjLuZtAf+i0XxoacDLkH8G9z4xKYdw+sfNW1to29zHW/dC9o+d/DL4m4C7PrF8CGBfDVIojvcdt6DqsLYwUTodvAzO1ilYNTsQM+ehQ+fBSqdrnu/cm3uPdOSxzzimJ44hTXZX/VG9A7M+eUVACTNqWoPMqFjy5ke3mUZ75/HKMO7eZ3SdKRVZXCl/90YWzNm1BZ7Fp3Dj2+rqsy/8imP1Ssdc+fd6+bpT67K4y/xg02zusArbzxajeR5vr5LsxuWFg3U3+X/i6IDZzg7nsd0XbHj1XscIFzwwfucW4PN09bw1tO9/Z91uiur9w1V5f82QXv4WfCpJvh0G+0/M/auc6FsFA2XP0mdMm88cMKYNLmbCndwwWPLqRsT5xnrzmeI/t+zeBMkXRIJtzA4dWvu0C29RO3vuvAujA2+AQ3VUQy4Qapz7vXBZDOfb3BxldAVmdffw1fJZOu62jDQq+lbCHs3uq25fSoC2MFE+CQUZlz9mdD5du8+ue7a2kWfe7WB7PcxKGxiiaeaCCnW+PhbK+bF+Kyu2Z+S2FJobvCxLJn3eNjLnTd6r2PaN2fu3kp/OnbbsqKK2dn3LhDBTBpk77aWcn5f1hILJHkuWsnMDS/k98lidRXttlNUrp6jjuVPlYJoRw3O/2ONW4CyR5DvMHG03Vx68ZY6/5OGxa6MLZ+PpSsc9vCee6i4QUTYeDxrguzUx9/WslKN7qgtX6e616tuRpBpBMcepybILdgEvQb666dGdvjzm6t3JFya/i4wS1R3fjPDoRci+ukG2HYqZkVxnZtgLl3w9Jn3AXfj73CzeHV2iewpPryn/DM+S68X/JiRr3PFMCkzVpbtJsLHv2AYACev3YCBT3z/C5JpHGxKvfhvHoOfPGW62qaeCMceVb77nJqDWVbXHfeeq/LctsKwPuMCmW7iWm7D3Jj57oPqrt1K4CsFviiZq0LgesX1IWuXRvctuyudWPYBk1quVY6a6F6d+NhrXyrO8GhbKO7wPrEG920KX5em7N0E7z/ezdvnTEueE2+Bbr086eeZc/BS9fAiO/AuTMzpitbAUzatFVby5n+2EJyIyFe+OEE+nXzaSZwEfHHnhJ3Pcqd61xXV0khlKx399Xl9ffN7VU/lKUGtS79Gw/DNSderJ/nBa4F7ow9cF2ABROhYLK77zPCn0CdiMGnL7qzZ7d/Bl0GwITrYezlLRM691fZFnciyZI/ub/b2EvdtCNdB6SvhqbMuw/e+gUcf727zFgGtBQqgEmb9+mmUi56/AN65kV4/toJ5HdpxrwxItI+WevCWUlqMCusC2elG8Em6vYPhF3XWE0LWue+bjb/9Qugosjt06mP60ocNMmFrt5HZMQHeS1r3ckg8+93gTG7G4z/AYy/tnWn+Sjf5i6NtXimOyt49MUw5afuTNZMYS28/u/w4SNwyq9dl63PFMCkXViyvoRLn/iQ/t1yePaa4+nZKXP6+UUkAyViLoTtWt8goBW6kLZnp2tJqhm/NWiyuy5oJgWufdm42J3ksfJVN+5p9MUw8YaWvbbp7iIXvBY94caojbrIBa8eg1vuZ7SkZBJevApWvATffRyOucDXchTApN1Y8GUxVz65iM7ZYX52+hGcN3YAgUAb+c9SRDJLbI9/FzdvScVrYMED7gzEZNxNcjrpJug35sBfs2KHe82PHoN4lTurccpt0HNIy9XdWuJRePpcNyXIxS/AkJN8K0UBTNqVTzeV8otZK1iyvoSR/bty57SjOLZAE7aKSAdXvhU+eMR1E0bL3GW1Jt8Mh520/616lTvdrPUf/sHNOD/yPHex9V7DWrf2llZVCjPPcK2fV86GvqN8KUMBTNoday2zlm3mt7NXsrWsirNH92PGGcPp27UdfJsVETkYVWWw5ElY+LCbY+2QY1yL2FHnNH3G5p5d8MHDLsBFy93ZhCfeDvnD01t7SyrbDE+c6lrEvv+mG/eXZgpg0m5VVsf5w7tf8ujctQSM4bqpQ7hmymFkh3Xav4h0cPEofPJ83QWwuxW4ObpGXwyRXLdPVZlr7VrwIERLXffliTPa9nVJUxWtciEstydc/Ubarz6hACbt3lc7K7nrtZW8unwL/bvlcMe3j+SMow/BtJXBtCIirSWZhNWvuWkaNn7kwsj4a910Ggv+212rcfiZMHUGHDLS72pb3oYP4Kmz3RQil78MkfTNJ6kAJh3Gwi938MuXV7ByaznHDe7BL84awVH9dBkjERGsdRPbzr/fXU4L3MXlp844uAH7bcHKV+G5S2DoKTD9r2m7xJUCmHQoiaTl2UUbuHvOKkr3xJg+fiA/OeVwTVshIlKjeI2bpqO9dDXuj8Uz4ZVbYMwlMO3BtEw3sj8BLEOvdirSfMGA4eLjCjhzZD/uf3sNTy0s5JVlm7n5W4dz6YQCwsHMuESFiIhv2tpZjS1h3FVuJv+5v4PO/eDkO/yuCAB9Ikm70zU3zH+cdRSv33wCow7txq9e+YzT75vLe6uL/C5NRET8cNLPYcylLoR9+Y7f1QDqgpR2zlrLP1du59evfEbhjkq+OTyfO759JIf1TuM11ERExH+JOHzyLIz6XqtftFtjwEQ80XiCPy8o5IG3vyAaT3DlpMHccPJQumSH/S5NRETamf0JYOqClA4hKxTkmilDeOenU/nOmP48/v5aTr77XZ5btIFkMvO/hIiISPuiFjDpkD7ZuItfvvwZS9aX0CMvwmG98hjUK4/BKbdBPfPIiWhiVxERaR51QYrsg7WWV5dvYd6aYtYWV1BYXMH28mi9ffp2zWZQzzwG985jcE8vmPXKY2CPXCIhNSCLiMjeNA2FyD4YYzjzmH6ceUy/2nW7o3EKiytY5wWydTvc8mvLt1BSGavdL2BgQPdcBvXKc61nPXMZ3LsTg3vm0b97DsGAZuIXEZGmKYCJpOiUFeLo/l05un/XvbbtqqxmXUo4W1tcQeGOCj5eX8LuaLx2v0gwQP/uOeR3ziK/SzZ9OmfRp0s2+V2yyO/s7vt0yaZTlt5+IiIdlT4BRPZTt9wIYwZGGDOwe7311lqKdkcpLK5kXfFu1hVX8lVJJUVlUZZv3MVbZVH2xBJ7vV5eJEh+l2wFNRGRDkj/s4scJGOMC0ydsxk/uMde2621lEfjbC+Lsr2siu3lUbaVVbGtLMr28iq2f01Qy40E6dMlm555EXKzQuSGg+RGguREvPtwkJxIqN46tz5UtxwJkuvtkxUK6GLlIiI+UwATaWXGGLpkh+mSHWZoftMTwH5dUNuxu5rSPTG2lu6hsjrBnuqEu28ktO27Hsj1QlteVpBuuRF65IbpnhehR26E7nkRuudG6JEX9u4jdMuN0D03TEiXcxIRaREKYCIZYn+DWkPJpCUaT1JZHa8NZJXVCSqr43UhzXtcGasf3CqicUoqYxTvrmb1tt2UVFZTWd10oOuSHaJHXqQ2rHWrCWoNwltuJEh2OEBWKEh22LXAZYcCCnAiIh4FMJE2LhAw5HjdjD1b4PWqYgl2VcbYWVFNSWV1/fuKakoqY5RUVrO1rIrPt5Sxo6KaaDy5X68dChhywkGywi6gZYddF2rNcu0tFKgX3LLCrus0KxQgUnMLBonsta7+46xQsHZ9OGjU9SoiGUMBTETqyQ4HOaRrkEO6Zu/3c/ZUJ9hZWRPQqtlTnaAqnqSqOkFVPEFVLEFVLMmeWN1yNJao97i8Kk5ReZRoPElVg20tJRIKkBUM1Aa32mCXEvCyveWG9641L2WfcIDskLuv2xYkHAwQDBiCAUPIu6+9GUMw6NYHTN12BUORjkcBTEQOWk4kSP9IDv275bT4a1vrulij8STV8STVCe++9nGCaCxJdK/19ZfdayTq7VOzrib07Y7GKd5d7dbFkrX3VfEEsUTrTVodMBAKBAgEvHsDoWCgNqSFgqZei15WMEBW2LXsNWwFTG31q9knK3V9yv6hQIBIyBAONlj2WgzDgQDhUN1yQPPbibQYBTARyWjGmNquST8lkjYlmLnA1vC+KpYgnrQkvFs8aUl694lksm6d9dYlLAnb2L516xLJJPGErQ2YLoi68Xs7G4TNaIMA2tKCAVMvmIUCLrBFvOVQMEAwAEFjCHgtfjX3wUDNsnudQMq6gHHr997XLTfVSpkVrt9Fnb2PfVpi/KG1lqR1/xaS1mItJK07hjYJJgDhQKD276SWTdkXBTARkf0QDBhvKg+/K9k/yaR1wSyRJBqr3yJY0xIYS1hiiSTxZJLqeN1yLO6eG0+4fdyy2x5L1D2vseWkFyhr7l2QTBKNWxLW1VVvuxdkEg3WJ1OCaU24PBg1QS47HCTihbGkF6isF6KSyfqhqnZb0i03V01LZihgagPqXstBQzDgWhmDAVMb4EJeV7UxpvbvkbSWZJK6Ze/vVld/3baE97skGnmetRAwhoBxX3BcIHbrjKnZlrIcqNlWt1/Nc1OfZ9i/wNncXGpM3Su7n0NtuDW1r2dqX9fU7mfqnlPz2MBtpx7BoF55zSuiFfgSwIwxpwP3A0Hgj9bau/yoQ0SkvQoEDNkBr+Vw/4fzZazUrujULuKqWLLRlsmG26pS9onG3Zm+NePvAimhoyZc1LTM1W4L1A8fDbcbY7Bey2Y8kfTuGzz2WkFjCevdpz5OfU6SqrhbBur9vKAXdkJeUDMpLYo19dQ+9moOpoSnmsuk1Wu9s3Vh1AW01MBW0/KXur1hCEzud0BtzvWnrVdn7TOsrV3ntrtaUvepef299knZ1type1pL2gOYMSYIPAScAmwEFhljZllrP0t3LSIi0jbU74oO+12OyEHzY1Ke8cAX1tq11tpq4FngbB/qEBEREfGFHwGsP/BVyuON3rp6jDHXGGMWG2MWFxUVpa04ERERkdbmRwBrbPjdXp3C1trHrLXjrLXjevfunYayRERERNLDjwC2ETg05fEAYLMPdYiIiIj4wo8AtggYZowZbIyJANOBWT7UISIiIuKLtJ8Faa2NG2NuAObgpqGYaa1dke46RERERPziyzxg1trZwGw/fraIiIiI3/zoghQRERHp0BTARERERNJMAUxEREQkzRTARERERNJMAUxEREQkzRTARERERNJMAUxEREQkzRTARERERNJMAUxEREQkzRTARERERNJMAUxEREQkzRTARERERNJMAUxEREQkzRTARERERNJMAUxEREQkzRTARERERNLMWGv9ruFrGWOKgPWt/GN6AcWt/DPk4Ok4tQ06TplPx6ht0HFqGxoepwJrbe99PaFNBLB0MMYsttaO87sO2Tcdp7ZBxynz6Ri1DTpObcOBHCd1QYqIiIikmQKYiIiISJopgNV5zO8CZL/oOLUNOk6ZT8eobdBxahuafZw0BkxEREQkzdQCJiIiIpJmCmAiIiIiaaYABhhjTjfGrDLGfGGMmeF3PbI3Y0yhMWa5MWapMWax3/WIY4yZaYzZboz5NGVdD2PMm8aYNd59dz9rlCaP053GmE3ee2qpMebf/KyxozPGHGqMeccY87kxZoUx5iZvvd5PGWQfx6nZ76cOPwbMGBMEVgOnABuBRcBF1trPfC1M6jHGFALjrLWakDCDGGOmALuBp6y1R3vrfgfstNbe5X2h6W6tvd3POju6Jo7TncBua+3dftYmjjGmL9DXWvuxMaYzsAQ4B7gCvZ8yxj6O0wU08/2kFjAYD3xhrV1rra0GngXO9rkmkTbBWjsX2Nlg9dnAn73lP+P+cxIfNXGcJINYa7dYaz/2lsuBz4H+6P2UUfZxnJpNAcz94b5KebyRA/xjSquywBvGmCXGmGv8Lkb2qY+1dgu4/6yAfJ/rkabdYIz5xOuiVNdWhjDGDALGAB+i91PGanCcoJnvJwUwMI2s69j9splpkrV2LHAG8COvS0VEDtwjwBBgNLAF+L2/5QiAMaYT8CJws7W2zO96pHGNHKdmv58UwFyL16EpjwcAm32qRZpgrd3s3W8HXsJ1HUtm2uaNk6gZL7Hd53qkEdbabdbahLU2CTyO3lO+M8aEcR/qz1hr/+6t1vspwzR2nA7k/aQA5gbdDzPGDDbGRIDpwCyfa5IUxpg8b7Ajxpg84FTg030/S3w0C7jcW74c+IePtUgTaj7UPd9B7ylfGWMM8ATwubX2npRNej9lkKaO04G8nzr8WZAA3umi9wFBYKa19v/5XJKkMMYchmv1AggBf9UxygzGmL8BU4FewDbgF8D/As8DA4ENwPnWWg0A91ETx2kqrrvEAoXAtTVjjST9jDGTgfeB5UDSW/1z3PgivZ8yxD6O00U08/2kACYiIiKSZuqCFBEREUkzBTARERGRNFMAExEREUkzBTARERGRNFMAExEREUkzBTARafOMMQljzNKU24wWfO1BxhjNkSUiLSrkdwEiIi1gj7V2tN9FiIjsL7WAiUi7ZYwpNMb8pzHmI+821FtfYIx527tw7tvGmIHe+j7GmJeMMcu820TvpYLGmMeNMSuMMW8YY3J8+6VEpF1QABOR9iCnQRfkhSnbyqy144EHcVe8wFt+ylp7DPAM8IC3/gHgPWvtKGAssMJbPwx4yFo7AtgFnNvKv4+ItHOaCV9E2jxjzG5rbadG1hcCJ1tr13oX0N1qre1pjCkG+lprY976LdbaXsaYImCAtTaa8hqDgDettcO8x7cDYWvt/23930xE2iu1gIlIe2ebWG5qn8ZEU5YTaPysiBwkBTARae8uTLlf6C0vAKZ7yxcD87zlt4HrAIwxQWNMl3QVKSIdi77FiUh7kGOMWZry+HVrbc1UFFnGmA9xXzgv8tbdCMw0xtwGFAFXeutvAh4zxlyNa+m6DtjS6tWLSIejMWAi0m55Y8DGWWuL/a5FRCSVuiBFRERE0kwtYCIiIiJpphYwERERkTRTABMRERFJMwUwERERkTRTABMRERFJMwUwERERkTT7/3+XA40xZqP1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.figure(figsize=(10,10))\n",
    "_ = plt.plot(history.history['loss'], label='Train Set')\n",
    "_ = plt.plot(history.history['val_loss'], label='Test Set')\n",
    "_ = plt.title('Train and Test Set Loss ')\n",
    "_ = plt.ylabel('Loss')\n",
    "_ = plt.xlabel('Epoch')\n",
    "_ = plt.legend(loc=\"upper left\")\n",
    "_ = plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.figure(figsize=(10,10))\n",
    "_ = plt.plot(history.history['categorical_accuracy'], label='Train Set')\n",
    "_ = plt.plot(history.history['val_categorical_accuracy'], label='Test Set')\n",
    "_ = plt.title('Categorical Accuracy on Train and Test Set')\n",
    "_ = plt.ylabel('Accuracy')\n",
    "_ = plt.xlabel('Epoch')\n",
    "_ = plt.legend(loc=\"upper left\")\n",
    "_ = plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.figure(figsize=(10,10))\n",
    "_ = plt.plot(history.history['top_5_acc'], label='Train Set')\n",
    "_ = plt.plot(history.history['val_top_5_acc'], label='Test Set')\n",
    "_ = plt.title('Top 5 Categorical Accuracy Train and Test Set Loss ')\n",
    "_ = plt.ylabel('Accuracy')\n",
    "_ = plt.xlabel('Epoch')\n",
    "_ = plt.legend(loc=\"upper left\")\n",
    "_ = plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "json_model = model.to_json()\n",
    "with open('mobilenetv2.json', 'w') as json_file:\n",
    "    json.dump(json_model,json_file)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}