TF_CatsVsDogsII.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rX8mhOLljYeM"
   },
   "source": [
    "# A simple model for Dogs vs. Cats Convolutional Neuronal Network (CNN) Part II\n",
    "\n",
    "**NOTE1:** The 2,000 images used in this exercise are excerpted from the [\"Dogs vs. Cats\" dataset](https://www.kaggle.com/c/dogs-vs-cats/data) available on Kaggle, which contains 25,000 images. Here, we use a subset of the full dataset to decrease training time for educational purposes.\n",
    "\n",
    "**NOTE2:** This notebook was inspired and modified from Lawrence Moroney's project <a href=\"https://colab.research.google.com/github/lmoroney/dlaicourse/blob/master/Course 2 - Part 6 - Lesson 3 - Notebook.ipynb\" target=\"_blank\">(link to notebook on Google CoLab)</a>.\n",
    "\n",
    "\n",
    "We will follow these steps:\n",
    "\n",
    "1. Transfer Learning\n",
    "2. Define Trainable Layers\n",
    "3. Download Dataset\n",
    "4. Pre-Processing - Image Data Generator and Image Augmentation\n",
    "5. Train Model\n",
    "6. Evaluate the Training and Validation Accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Transfer Learning\n",
    "\n",
    "Loading a pre-trained Convolutional Neural Network (Inception V3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\ChristianV700\\\\Documents\\\\GitHub\\\\DataScienceProjects\\\\Data\\\\inception_v3_weights_tf_dim_ordering_tf_kernels_notop 1.h5'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import wget\n",
    "\n",
    "url = 'https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'\n",
    "wget.download(url, out=f'{os.getcwd()}\\\\Data\\\\inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 231
    },
    "colab_type": "code",
    "id": "1xJZ5glPPCRz",
    "outputId": "a143c4d7-13d8-4417-d025-1517c8b52307"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import Model\n",
    "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
    "\n",
    "# all the weights for the trained Inception V3\n",
    "local_weights_file = f'{os.getcwd()}\\\\Data\\\\inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'\n",
    "\n",
    "# the network shape of Inception V3 is integraded in Keras \n",
    "pre_trained_model = InceptionV3(input_shape = (150, 150, 3), \n",
    "                                include_top = False, \n",
    "                                weights = None)\n",
    "\n",
    "# combine layout and weights\n",
    "pre_trained_model.load_weights(local_weights_file)\n",
    "\n",
    "# make pre-trained model read-only\n",
    "for layer in pre_trained_model.layers:\n",
    "  layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"inception_v3\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 150, 150, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 74, 74, 32)   864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 74, 74, 32)   96          conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 74, 74, 32)   0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 72, 72, 32)   9216        activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 72, 72, 32)   96          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 72, 72, 32)   0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 72, 72, 64)   18432       activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 72, 72, 64)   192         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 72, 72, 64)   0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 35, 35, 64)   0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 35, 35, 80)   5120        max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 35, 35, 80)   240         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 35, 35, 80)   0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 33, 33, 192)  138240      activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 33, 33, 192)  576         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 33, 33, 192)  0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 16, 16, 192)  0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 16, 16, 64)   12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 16, 16, 64)   192         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 16, 16, 64)   0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 16, 16, 48)   9216        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 16, 16, 96)   55296       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 16, 16, 48)   144         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 16, 16, 96)   288         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 16, 16, 48)   0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 16, 16, 96)   0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d (AveragePooli (None, 16, 16, 192)  0           max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 16, 16, 64)   12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 16, 16, 64)   76800       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 16, 16, 96)   82944       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 16, 16, 32)   6144        average_pooling2d[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 16, 16, 64)   192         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 16, 16, 64)   192         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 16, 16, 96)   288         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 16, 16, 32)   96          conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 16, 16, 64)   0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 16, 16, 64)   0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 16, 16, 96)   0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 16, 16, 32)   0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)            (None, 16, 16, 256)  0           activation_5[0][0]               \n",
      "                                                                 activation_7[0][0]               \n",
      "                                                                 activation_10[0][0]              \n",
      "                                                                 activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 16, 16, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 16, 16, 64)   192         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 16, 16, 64)   0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 16, 16, 48)   12288       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 16, 16, 96)   55296       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 16, 16, 48)   144         conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 16, 16, 96)   288         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 16, 16, 48)   0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 16, 16, 96)   0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, 16, 16, 256)  0           mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 16, 16, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 16, 16, 64)   76800       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 16, 16, 96)   82944       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 16, 16, 64)   16384       average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 16, 16, 64)   192         conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 16, 16, 64)   192         conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 16, 16, 96)   288         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 16, 16, 64)   192         conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 16, 16, 64)   0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 16, 16, 64)   0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 16, 16, 96)   0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 16, 16, 64)   0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)            (None, 16, 16, 288)  0           activation_12[0][0]              \n",
      "                                                                 activation_14[0][0]              \n",
      "                                                                 activation_17[0][0]              \n",
      "                                                                 activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 16, 16, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 16, 16, 64)   192         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 16, 16, 64)   0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 16, 16, 48)   13824       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 16, 16, 96)   55296       activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 16, 16, 48)   144         conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 16, 16, 96)   288         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 16, 16, 48)   0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 16, 16, 96)   0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_2 (AveragePoo (None, 16, 16, 288)  0           mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 16, 16, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 16, 16, 64)   76800       activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 16, 16, 96)   82944       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 16, 16, 64)   18432       average_pooling2d_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 16, 16, 64)   192         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 16, 16, 64)   192         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 16, 16, 96)   288         conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 16, 16, 64)   192         conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 16, 16, 64)   0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 16, 16, 64)   0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 16, 16, 96)   0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 16, 16, 64)   0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)            (None, 16, 16, 288)  0           activation_19[0][0]              \n",
      "                                                                 activation_21[0][0]              \n",
      "                                                                 activation_24[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 16, 16, 64)   18432       mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 16, 16, 64)   192         conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 16, 16, 64)   0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 16, 16, 96)   55296       activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 16, 16, 96)   288         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 16, 16, 96)   0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 7, 7, 384)    995328      mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 7, 7, 96)     82944       activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 7, 7, 384)    1152        conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 7, 7, 96)     288         conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 7, 7, 384)    0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 7, 7, 96)     0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 7, 7, 288)    0           mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed3 (Concatenate)            (None, 7, 7, 768)    0           activation_26[0][0]              \n",
      "                                                                 activation_29[0][0]              \n",
      "                                                                 max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, 7, 7, 128)    98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, 7, 7, 128)    384         conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 7, 7, 128)    0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, 7, 7, 128)    114688      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, 7, 7, 128)    384         conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 7, 7, 128)    0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 7, 7, 128)    98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, 7, 7, 128)    114688      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, 7, 7, 128)    384         conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, 7, 7, 128)    384         conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 7, 7, 128)    0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 7, 7, 128)    0           batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 7, 7, 128)    114688      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, 7, 7, 128)    114688      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, 7, 7, 128)    384         conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, 7, 7, 128)    384         conv2d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 7, 7, 128)    0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 7, 7, 128)    0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_3 (AveragePoo (None, 7, 7, 768)    0           mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 7, 7, 192)    147456      mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 7, 7, 192)    172032      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, 7, 7, 192)    172032      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_3[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, 7, 7, 192)    576         conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, 7, 7, 192)    576         conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, 7, 7, 192)    576         conv2d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, 7, 7, 192)    576         conv2d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 7, 7, 192)    0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 7, 7, 192)    0           batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 7, 7, 192)    0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 7, 7, 192)    0           batch_normalization_39[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed4 (Concatenate)            (None, 7, 7, 768)    0           activation_30[0][0]              \n",
      "                                                                 activation_33[0][0]              \n",
      "                                                                 activation_38[0][0]              \n",
      "                                                                 activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_44 (Conv2D)              (None, 7, 7, 160)    122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, 7, 7, 160)    480         conv2d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 7, 7, 160)    0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)              (None, 7, 7, 160)    179200      activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, 7, 7, 160)    480         conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 7, 7, 160)    0           batch_normalization_45[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, 7, 7, 160)    122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, 7, 7, 160)    179200      activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, 7, 7, 160)    480         conv2d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, 7, 7, 160)    480         conv2d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 7, 7, 160)    0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 7, 7, 160)    0           batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, 7, 7, 160)    179200      activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, 7, 7, 160)    179200      activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, 7, 7, 160)    480         conv2d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_47 (BatchNo (None, 7, 7, 160)    480         conv2d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 7, 7, 160)    0           batch_normalization_42[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 7, 7, 160)    0           batch_normalization_47[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_4 (AveragePoo (None, 7, 7, 768)    0           mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, 7, 7, 192)    147456      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, 7, 7, 192)    215040      activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_48 (Conv2D)              (None, 7, 7, 192)    215040      activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_4[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, 7, 7, 192)    576         conv2d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNo (None, 7, 7, 192)    576         conv2d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_48 (BatchNo (None, 7, 7, 192)    576         conv2d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_49 (BatchNo (None, 7, 7, 192)    576         conv2d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 7, 7, 192)    0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 7, 7, 192)    0           batch_normalization_43[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 7, 7, 192)    0           batch_normalization_48[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 7, 7, 192)    0           batch_normalization_49[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed5 (Concatenate)            (None, 7, 7, 768)    0           activation_40[0][0]              \n",
      "                                                                 activation_43[0][0]              \n",
      "                                                                 activation_48[0][0]              \n",
      "                                                                 activation_49[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_54 (Conv2D)              (None, 7, 7, 160)    122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_54 (BatchNo (None, 7, 7, 160)    480         conv2d_54[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_54 (Activation)      (None, 7, 7, 160)    0           batch_normalization_54[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_55 (Conv2D)              (None, 7, 7, 160)    179200      activation_54[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_55 (BatchNo (None, 7, 7, 160)    480         conv2d_55[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_55 (Activation)      (None, 7, 7, 160)    0           batch_normalization_55[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_51 (Conv2D)              (None, 7, 7, 160)    122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_56 (Conv2D)              (None, 7, 7, 160)    179200      activation_55[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_51 (BatchNo (None, 7, 7, 160)    480         conv2d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_56 (BatchNo (None, 7, 7, 160)    480         conv2d_56[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_51 (Activation)      (None, 7, 7, 160)    0           batch_normalization_51[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_56 (Activation)      (None, 7, 7, 160)    0           batch_normalization_56[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_52 (Conv2D)              (None, 7, 7, 160)    179200      activation_51[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_57 (Conv2D)              (None, 7, 7, 160)    179200      activation_56[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_52 (BatchNo (None, 7, 7, 160)    480         conv2d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_57 (BatchNo (None, 7, 7, 160)    480         conv2d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_52 (Activation)      (None, 7, 7, 160)    0           batch_normalization_52[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_57 (Activation)      (None, 7, 7, 160)    0           batch_normalization_57[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_5 (AveragePoo (None, 7, 7, 768)    0           mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_50 (Conv2D)              (None, 7, 7, 192)    147456      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_53 (Conv2D)              (None, 7, 7, 192)    215040      activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_58 (Conv2D)              (None, 7, 7, 192)    215040      activation_57[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_59 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_5[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_50 (BatchNo (None, 7, 7, 192)    576         conv2d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_53 (BatchNo (None, 7, 7, 192)    576         conv2d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_58 (BatchNo (None, 7, 7, 192)    576         conv2d_58[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_59 (BatchNo (None, 7, 7, 192)    576         conv2d_59[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_50 (Activation)      (None, 7, 7, 192)    0           batch_normalization_50[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_53 (Activation)      (None, 7, 7, 192)    0           batch_normalization_53[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_58 (Activation)      (None, 7, 7, 192)    0           batch_normalization_58[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_59 (Activation)      (None, 7, 7, 192)    0           batch_normalization_59[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed6 (Concatenate)            (None, 7, 7, 768)    0           activation_50[0][0]              \n",
      "                                                                 activation_53[0][0]              \n",
      "                                                                 activation_58[0][0]              \n",
      "                                                                 activation_59[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_64 (Conv2D)              (None, 7, 7, 192)    147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_64 (BatchNo (None, 7, 7, 192)    576         conv2d_64[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_64 (Activation)      (None, 7, 7, 192)    0           batch_normalization_64[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_65 (Conv2D)              (None, 7, 7, 192)    258048      activation_64[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_65 (BatchNo (None, 7, 7, 192)    576         conv2d_65[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_65 (Activation)      (None, 7, 7, 192)    0           batch_normalization_65[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_61 (Conv2D)              (None, 7, 7, 192)    147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_66 (Conv2D)              (None, 7, 7, 192)    258048      activation_65[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_61 (BatchNo (None, 7, 7, 192)    576         conv2d_61[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_66 (BatchNo (None, 7, 7, 192)    576         conv2d_66[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_61 (Activation)      (None, 7, 7, 192)    0           batch_normalization_61[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_66 (Activation)      (None, 7, 7, 192)    0           batch_normalization_66[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_62 (Conv2D)              (None, 7, 7, 192)    258048      activation_61[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_67 (Conv2D)              (None, 7, 7, 192)    258048      activation_66[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_62 (BatchNo (None, 7, 7, 192)    576         conv2d_62[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_67 (BatchNo (None, 7, 7, 192)    576         conv2d_67[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_62 (Activation)      (None, 7, 7, 192)    0           batch_normalization_62[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_67 (Activation)      (None, 7, 7, 192)    0           batch_normalization_67[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_6 (AveragePoo (None, 7, 7, 768)    0           mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_60 (Conv2D)              (None, 7, 7, 192)    147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_63 (Conv2D)              (None, 7, 7, 192)    258048      activation_62[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_68 (Conv2D)              (None, 7, 7, 192)    258048      activation_67[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_69 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_6[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_60 (BatchNo (None, 7, 7, 192)    576         conv2d_60[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_63 (BatchNo (None, 7, 7, 192)    576         conv2d_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_68 (BatchNo (None, 7, 7, 192)    576         conv2d_68[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_69 (BatchNo (None, 7, 7, 192)    576         conv2d_69[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_60 (Activation)      (None, 7, 7, 192)    0           batch_normalization_60[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_63 (Activation)      (None, 7, 7, 192)    0           batch_normalization_63[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_68 (Activation)      (None, 7, 7, 192)    0           batch_normalization_68[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_69 (Activation)      (None, 7, 7, 192)    0           batch_normalization_69[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed7 (Concatenate)            (None, 7, 7, 768)    0           activation_60[0][0]              \n",
      "                                                                 activation_63[0][0]              \n",
      "                                                                 activation_68[0][0]              \n",
      "                                                                 activation_69[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_72 (Conv2D)              (None, 7, 7, 192)    147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_72 (BatchNo (None, 7, 7, 192)    576         conv2d_72[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_72 (Activation)      (None, 7, 7, 192)    0           batch_normalization_72[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_73 (Conv2D)              (None, 7, 7, 192)    258048      activation_72[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_73 (BatchNo (None, 7, 7, 192)    576         conv2d_73[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_73 (Activation)      (None, 7, 7, 192)    0           batch_normalization_73[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_70 (Conv2D)              (None, 7, 7, 192)    147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_74 (Conv2D)              (None, 7, 7, 192)    258048      activation_73[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_70 (BatchNo (None, 7, 7, 192)    576         conv2d_70[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_74 (BatchNo (None, 7, 7, 192)    576         conv2d_74[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_70 (Activation)      (None, 7, 7, 192)    0           batch_normalization_70[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_74 (Activation)      (None, 7, 7, 192)    0           batch_normalization_74[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_71 (Conv2D)              (None, 3, 3, 320)    552960      activation_70[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_75 (Conv2D)              (None, 3, 3, 192)    331776      activation_74[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_71 (BatchNo (None, 3, 3, 320)    960         conv2d_71[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_75 (BatchNo (None, 3, 3, 192)    576         conv2d_75[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_71 (Activation)      (None, 3, 3, 320)    0           batch_normalization_71[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_75 (Activation)      (None, 3, 3, 192)    0           batch_normalization_75[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 3, 3, 768)    0           mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed8 (Concatenate)            (None, 3, 3, 1280)   0           activation_71[0][0]              \n",
      "                                                                 activation_75[0][0]              \n",
      "                                                                 max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_80 (Conv2D)              (None, 3, 3, 448)    573440      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_80 (BatchNo (None, 3, 3, 448)    1344        conv2d_80[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_80 (Activation)      (None, 3, 3, 448)    0           batch_normalization_80[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_77 (Conv2D)              (None, 3, 3, 384)    491520      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_81 (Conv2D)              (None, 3, 3, 384)    1548288     activation_80[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_77 (BatchNo (None, 3, 3, 384)    1152        conv2d_77[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_81 (BatchNo (None, 3, 3, 384)    1152        conv2d_81[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_77 (Activation)      (None, 3, 3, 384)    0           batch_normalization_77[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_81 (Activation)      (None, 3, 3, 384)    0           batch_normalization_81[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_78 (Conv2D)              (None, 3, 3, 384)    442368      activation_77[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_79 (Conv2D)              (None, 3, 3, 384)    442368      activation_77[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_82 (Conv2D)              (None, 3, 3, 384)    442368      activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_83 (Conv2D)              (None, 3, 3, 384)    442368      activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_7 (AveragePoo (None, 3, 3, 1280)   0           mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_76 (Conv2D)              (None, 3, 3, 320)    409600      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_78 (BatchNo (None, 3, 3, 384)    1152        conv2d_78[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_79 (BatchNo (None, 3, 3, 384)    1152        conv2d_79[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_82 (BatchNo (None, 3, 3, 384)    1152        conv2d_82[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_83 (BatchNo (None, 3, 3, 384)    1152        conv2d_83[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_84 (Conv2D)              (None, 3, 3, 192)    245760      average_pooling2d_7[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_76 (BatchNo (None, 3, 3, 320)    960         conv2d_76[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_78 (Activation)      (None, 3, 3, 384)    0           batch_normalization_78[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_79 (Activation)      (None, 3, 3, 384)    0           batch_normalization_79[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_82 (Activation)      (None, 3, 3, 384)    0           batch_normalization_82[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_83 (Activation)      (None, 3, 3, 384)    0           batch_normalization_83[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_84 (BatchNo (None, 3, 3, 192)    576         conv2d_84[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_76 (Activation)      (None, 3, 3, 320)    0           batch_normalization_76[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_0 (Concatenate)          (None, 3, 3, 768)    0           activation_78[0][0]              \n",
      "                                                                 activation_79[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 3, 3, 768)    0           activation_82[0][0]              \n",
      "                                                                 activation_83[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_84 (Activation)      (None, 3, 3, 192)    0           batch_normalization_84[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9 (Concatenate)            (None, 3, 3, 2048)   0           activation_76[0][0]              \n",
      "                                                                 mixed9_0[0][0]                   \n",
      "                                                                 concatenate[0][0]                \n",
      "                                                                 activation_84[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_89 (Conv2D)              (None, 3, 3, 448)    917504      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_89 (BatchNo (None, 3, 3, 448)    1344        conv2d_89[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_89 (Activation)      (None, 3, 3, 448)    0           batch_normalization_89[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_86 (Conv2D)              (None, 3, 3, 384)    786432      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_90 (Conv2D)              (None, 3, 3, 384)    1548288     activation_89[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_86 (BatchNo (None, 3, 3, 384)    1152        conv2d_86[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_90 (BatchNo (None, 3, 3, 384)    1152        conv2d_90[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_86 (Activation)      (None, 3, 3, 384)    0           batch_normalization_86[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_90 (Activation)      (None, 3, 3, 384)    0           batch_normalization_90[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_87 (Conv2D)              (None, 3, 3, 384)    442368      activation_86[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_88 (Conv2D)              (None, 3, 3, 384)    442368      activation_86[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_91 (Conv2D)              (None, 3, 3, 384)    442368      activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_92 (Conv2D)              (None, 3, 3, 384)    442368      activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_8 (AveragePoo (None, 3, 3, 2048)   0           mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_85 (Conv2D)              (None, 3, 3, 320)    655360      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_87 (BatchNo (None, 3, 3, 384)    1152        conv2d_87[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_88 (BatchNo (None, 3, 3, 384)    1152        conv2d_88[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_91 (BatchNo (None, 3, 3, 384)    1152        conv2d_91[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_92 (BatchNo (None, 3, 3, 384)    1152        conv2d_92[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_93 (Conv2D)              (None, 3, 3, 192)    393216      average_pooling2d_8[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_85 (BatchNo (None, 3, 3, 320)    960         conv2d_85[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_87 (Activation)      (None, 3, 3, 384)    0           batch_normalization_87[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_88 (Activation)      (None, 3, 3, 384)    0           batch_normalization_88[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_91 (Activation)      (None, 3, 3, 384)    0           batch_normalization_91[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_92 (Activation)      (None, 3, 3, 384)    0           batch_normalization_92[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_93 (BatchNo (None, 3, 3, 192)    576         conv2d_93[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_85 (Activation)      (None, 3, 3, 320)    0           batch_normalization_85[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_1 (Concatenate)          (None, 3, 3, 768)    0           activation_87[0][0]              \n",
      "                                                                 activation_88[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 3, 3, 768)    0           activation_91[0][0]              \n",
      "                                                                 activation_92[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_93 (Activation)      (None, 3, 3, 192)    0           batch_normalization_93[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed10 (Concatenate)           (None, 3, 3, 2048)   0           activation_85[0][0]              \n",
      "                                                                 mixed9_1[0][0]                   \n",
      "                                                                 concatenate_1[0][0]              \n",
      "                                                                 activation_93[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 21,802,784\n",
      "Trainable params: 0\n",
      "Non-trainable params: 21,802,784\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# the Inception V3 is very deep\n",
    "pre_trained_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "last layer output shape:  (None, 7, 7, 768)\n"
     ]
    }
   ],
   "source": [
    "# set layer \"mixed 7\" as end of pre-trained network \n",
    "last_layer = pre_trained_model.get_layer('mixed7')\n",
    "print('last layer output shape: ', last_layer.output_shape)\n",
    "last_output = last_layer.output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Define Trainable Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BMXb913pbvFg"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.optimizers import RMSprop\n",
    "\n",
    "# Flatten the output layer to 1 dimension\n",
    "x = layers.Flatten()(last_output)\n",
    "# Add a fully connected layer with 1,024 hidden units and ReLU activation\n",
    "x = layers.Dense(1024, activation='relu')(x)\n",
    "# Add a dropout rate of 0.2\n",
    "x = layers.Dropout(0.2)(x)                  \n",
    "# Add a final sigmoid layer for classification\n",
    "x = layers.Dense  (1, activation='sigmoid')(x)           \n",
    "\n",
    "model = Model( pre_trained_model.input, x) \n",
    "\n",
    "model.compile(optimizer = RMSprop(lr=0.0001), \n",
    "              loss = 'binary_crossentropy', \n",
    "              metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Download Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\ChristianV700\\\\Documents\\\\GitHub\\\\DataScienceProjects\\\\Data\\\\cats_and_dogs_filtered 1.zip'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'\n",
    "wget.download(url, out=f'{os.getcwd()}\\\\Data\\\\cats_and_dogs_filtered.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import zipfile\n",
    "\n",
    "local_zip = f'{os.getcwd()}\\\\Data\\\\cats_and_dogs_filtered.zip'\n",
    "\n",
    "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
    "\n",
    "zip_ref.extractall(f'{os.getcwd()}\\\\Data')\n",
    "zip_ref.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Pre-Processing - Image Data Generator and Image Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "O4s8HckqGlnb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# Define our example directories and files\n",
    "base_dir = f'{os.getcwd()}\\\\Data\\\\cats_and_dogs_filtered'\n",
    "\n",
    "# create directory names\n",
    "train_dir = os.path.join( base_dir, 'train')\n",
    "validation_dir = os.path.join( base_dir, 'validation')\n",
    "\n",
    "train_cats_dir = os.path.join(train_dir, 'cats') # Directory with our training cat pictures\n",
    "train_dogs_dir = os.path.join(train_dir, 'dogs') # Directory with our training dog pictures\n",
    "validation_cats_dir = os.path.join(validation_dir, 'cats') # Directory with our validation cat pictures\n",
    "validation_dogs_dir = os.path.join(validation_dir, 'dogs')# Directory with our validation dog pictures\n",
    "\n",
    "# get all training file names\n",
    "train_cat_fnames = os.listdir(train_cats_dir)\n",
    "train_dog_fnames = os.listdir(train_dogs_dir)\n",
    "\n",
    "# Add our data-augmentation parameters to ImageDataGenerator\n",
    "train_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",
    "\n",
    "# Note that the validation data should not be augmented!\n",
    "test_datagen = ImageDataGenerator( rescale = 1.0/255. )\n",
    "\n",
    "# Flow training images in batches of 20 using train_datagen generator\n",
    "train_generator = train_datagen.flow_from_directory(train_dir,\n",
    "                                                    batch_size = 20,\n",
    "                                                    class_mode = 'binary', \n",
    "                                                    target_size = (150, 150))     \n",
    "\n",
    "# Flow validation images in batches of 20 using test_datagen generator\n",
    "validation_generator =  test_datagen.flow_from_directory( validation_dir,\n",
    "                                                          batch_size  = 20,\n",
    "                                                          class_mode  = 'binary', \n",
    "                                                          target_size = (150, 150))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Blhq2MAUeyGA"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "100/100 - 27s - loss: 0.4954 - accuracy: 0.7655 - val_loss: 0.6056 - val_accuracy: 0.8500\n",
      "Epoch 2/20\n",
      "100/100 - 15s - loss: 0.3829 - accuracy: 0.8315 - val_loss: 0.2152 - val_accuracy: 0.9440\n",
      "Epoch 3/20\n",
      "100/100 - 15s - loss: 0.3400 - accuracy: 0.8505 - val_loss: 0.2933 - val_accuracy: 0.9360\n",
      "Epoch 4/20\n",
      "100/100 - 15s - loss: 0.3316 - accuracy: 0.8625 - val_loss: 0.1953 - val_accuracy: 0.9570\n",
      "Epoch 5/20\n",
      "100/100 - 16s - loss: 0.3227 - accuracy: 0.8615 - val_loss: 0.3282 - val_accuracy: 0.9470\n",
      "Epoch 6/20\n",
      "100/100 - 16s - loss: 0.3226 - accuracy: 0.8585 - val_loss: 0.3156 - val_accuracy: 0.9530\n",
      "Epoch 7/20\n",
      "100/100 - 15s - loss: 0.3046 - accuracy: 0.8700 - val_loss: 0.3480 - val_accuracy: 0.9470\n",
      "Epoch 8/20\n",
      "100/100 - 15s - loss: 0.2785 - accuracy: 0.8750 - val_loss: 0.4184 - val_accuracy: 0.9450\n",
      "Epoch 9/20\n",
      "100/100 - 15s - loss: 0.2843 - accuracy: 0.8815 - val_loss: 0.3369 - val_accuracy: 0.9500\n",
      "Epoch 10/20\n",
      "100/100 - 15s - loss: 0.2957 - accuracy: 0.8800 - val_loss: 0.4547 - val_accuracy: 0.9430\n",
      "Epoch 11/20\n",
      "100/100 - 15s - loss: 0.2685 - accuracy: 0.8970 - val_loss: 0.3231 - val_accuracy: 0.9470\n",
      "Epoch 12/20\n",
      "100/100 - 15s - loss: 0.2551 - accuracy: 0.8955 - val_loss: 0.4682 - val_accuracy: 0.9480\n",
      "Epoch 13/20\n",
      "100/100 - 16s - loss: 0.2701 - accuracy: 0.8935 - val_loss: 0.3559 - val_accuracy: 0.9540\n",
      "Epoch 14/20\n",
      "100/100 - 18s - loss: 0.2842 - accuracy: 0.8865 - val_loss: 0.4018 - val_accuracy: 0.9470\n",
      "Epoch 15/20\n",
      "100/100 - 17s - loss: 0.2458 - accuracy: 0.9025 - val_loss: 0.4076 - val_accuracy: 0.9550\n",
      "Epoch 16/20\n",
      "100/100 - 16s - loss: 0.2533 - accuracy: 0.9025 - val_loss: 0.3737 - val_accuracy: 0.9620\n",
      "Epoch 17/20\n",
      "100/100 - 15s - loss: 0.2465 - accuracy: 0.9080 - val_loss: 0.3398 - val_accuracy: 0.9650\n",
      "Epoch 18/20\n",
      "100/100 - 15s - loss: 0.2620 - accuracy: 0.8925 - val_loss: 0.3271 - val_accuracy: 0.9640\n",
      "Epoch 19/20\n",
      "100/100 - 15s - loss: 0.2321 - accuracy: 0.9060 - val_loss: 0.3551 - val_accuracy: 0.9670\n",
      "Epoch 20/20\n",
      "100/100 - 15s - loss: 0.2470 - accuracy: 0.9000 - val_loss: 0.4488 - val_accuracy: 0.9500\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "            train_generator,\n",
    "            validation_data = validation_generator,\n",
    "            steps_per_epoch = 100,\n",
    "            epochs = 20,\n",
    "            validation_steps = 50,\n",
    "            verbose = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6: Evaluate the Training and Validation Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Training and validation loss')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yVOU+Pj6mdoBoTU0NVVVVh+IMCgo6dN5ffPFFyM8//xyyfPnyDSEhITWpqakJZWVlXvZ6j3q9Pn36VEVGRtrmz58fsmrVqg6ffvrpNpdOrAk0wVBKtU1V5ZCTAfvXQe428A2CgFAICDt883d47hcMXg006hoD+TutJGL/usMJRb7DRJmBHSF6AAydat13GQA5G+GTm2DOJLhqLvg6/XGtmqGwsNA7Li6uEuDll1+ObKh8Y6WkpJTv3r3bf+PGjX4JCQmVc+fOjXBWbs6cORHPPPPMzptuuinXHpdXfHz8wKKiIq/TTjut6Iknnoh6+OGHs202G4WFhV5jxowpvOKKK/o89NBDWV26dKnOysryjo6Oru7Ro0flihUrgqZNm5b33nvvhTtLhADy8/O9w8LCqkNCQmpWrVoVkJaW1gHgrLPOKrnnnnt6bNiwwa+2i6S2FeP666/PmTZtWs/x48cfbMlBoppgKKVOfEVZ9pYDhy/+A5vB2Fubxfvw42OSwwmIv0MSEhAK3n5wYJPVSlFReLh8p97QdQicNAWiB0KXgRDaFer+coxJgRobfHoLfHAN/Old8PE7KgLVdA888MD+adOm9Xzuuee6jBw50u39UcHBwebpp5/eOWbMmL4RERG2IUOGlNQtU1RU5LVo0aKwt95661DGGRoaWjNs2LDiOXPmhL300ku7pk6d2qNfv36RXl5ePP/88zvPPffcknvuuSdz5MiRiV5eXmbAgAGl8+bN23H77bfnXHzxxX0GDhyYNGrUqMLAwMCauq8HMH78+IJXXnklql+/fsm9e/cuT0lJKQHo2rWr7bnnntsxduzYPjU1NXTq1Knq999/3wxw1VVXFdx2223e06dPP+ju98nRCbVc+7Bhw4yuRaJUO1ZdZbUGOHZFZK2HEodlBELjrFaD2taD6IEQ0dMaE1FRCOUFh2+Hnhc62eaw3VYGEb0d6h0InZMa392x7HX48m5IvgzGzwLv4/MbT0RWGGOGNfX4tLS0HSkpKS3WV3+iKCgo8AoLC6upqanhmmuu6d63b9/yf/zjH04X+mrNFi1aFHTXXXd1W7Fixcbm1pWWlhaZkpIS72yftmAopVovY2D7z7DmQ9ifBtkboKbK2uftD50Toe/5h7/4o/tDkNOWa/DyBp9I6OD21nPXnXwD2Mrh24fA51a4fGbD3TKq1XjmmWciZ8+eHVlVVSX9+/cvvfvuu0+4pOuhhx7q8uabb0a98cYb2xsu3TzagqGUan0qS2DNXOvqi5wMCAiH2KGHWyS6DIBOfY9bC4DbLXoCfnwUTroWLnn26C4VN9MWDNVStAVDKXViyNsBS1+FVe9YXRNdBsHlL0H/cW1rYOSo+6CqDH55CnwDYcyMFk8ylDreNMFQSnlWbTfIkldg41cgXpB8KQy/GboNb7tfvGf/PyvJWPyilWSc84+2e66qXdIEQynlGXW7QYI6wch7YNj1EBbr6ehangic/79WkvHrf8C3A5xxn6ejUsptNME4EWz7GTZ8AaffDaExDZdXqjVz1g1y2YswYHzb6gZxhQhc9LSVZPz0qHX+p97u6aiUcgsdvtzaHdwKc6fA0lfg+ZNh8UtQ3SLTxivVcoyBbQth9iR4drD1Oe59Nlz/Ldy0CIZMbn/JRS0vL7jsBUi+HL77u5V8qSOkpqYmzJs3L9Rx2z//+c/OV199dff6jlm0aFEQwBlnnNHnwIEDR63xcffdd3d9+OGHo+t77XfeeSd8xYoVhz6cd955Z9dPP/00pPFn4VxbXtZdE4zWrLLUmpTHywuumQ/dh8M3D8KrZ8LuZZ6OTqmGVdtg9Wx4cQS8fRnsXgwj74Y718KEN6H7KTruAKyrYca9Cv0ugK/uhVXveTqiVmXChAkHZ8+efcT1x/PmzYu4+uqrna4HUtfPP/+8JTIysknf4J9++mn4mjVrAmufP/PMM/suv/xytyy/XndZd3fU6YzN5pkfpZpgtFbGWBPyZK2Hca9BrzNg8kcw4S0oOQivj4bP74BSl/59qfYqf7d1pcLcq2HtR2CrbPgYd7BVwsq34fmh8OnN1sDNy16Eu9LhnIfbxxiLxvLxs5KuXmfB/Ntg3TxPR9RqTJkyJe+HH34IKysrE4CNGzf6ZWdn+5533nnFkydP7j5gwICkPn369L/rrru6Ojs+NjZ2YO2S5A888ECX+Pj4Aaeeemq/zZs3+9eWeeqppyIHDBiQlJCQkHz++ef3Lioq8lqwYEGH77//Pvzvf/97XGJiYvL69ev9HZdR/+yzz0KSkpKS+/XrlzxhwoT42vhiY2MH3nXXXV2Tk5OT+vXrl7xq1SqnzXNtfVl3HYPRWi2fBWmz4cy/Qt9zrW0i0P9y6HMOLJxhNTNnfA6j/wWDJ7XML8Gs9VYcB7dB0sWQdCn4B7v/dZT7VBRZn4u02bD9F8BAhyhrW3AXa7KnoVMhuLP7X9tWAavehV+fgYJd1hTZE9+3fpnrhFIN8w2w3q93x8PH08EnABIv8nRUR/r0z93ITnfrcu10Ti7l8heOuYhaly5dqlNSUkrmzZsXdvXVV+e/9dZbEZdeemmel5cXTz/99N7o6Ohqm83GqaeemrBkyZLA4cOHlzmr55dffgn65JNPItauXZteVVXF4MGDk4cMGVIKMHny5Lx77rnnAMBf/vKXrs8991zk3/72t+xzzz03/+KLLy647rrr8hzrKi0tlZtuuqnnd999t3HQoEEVY8eOja9dZwQgMjLSlp6enjFjxoyoGTNmRM+dO3dn3Xja+rLuLv2LF5ExIrJRRLaIyINO9ncUkU9EZI2ILBWRAQ77dojIWhFZLSLLHbZHiMgCEdlsv+9Yt952a89y+PoB6DMaRt1/9H7/EDj/MbjpZ2v64s9uhTcutFZvdIfibPjjBZh5Orx0qpXIZK621lF4si98fBNs/cmaelm1DjXVsPVH60vpyX7W36pgD5z1ENyRBvdsslrAugyAnx6D//S3/o57V7rn9avKYMnL1viKL++2kpdJH8L0n60vSE0uXOcXBJPmWsnZh1Nhy/eejqhVuPLKK3Pnzp3bEeDjjz+OmDJlSi7AW2+9FZGcnJyUnJycvHnz5oC0tLRjDub56aefgi+88ML8kJCQmoiIiJrzzjsvv3bfihUrAocOHZrQr1+/5Hnz5nVav359vYOC0tLSAuLi4ioGDRpUATB16tSDv/7666FujkmTJuUBpKamlu7evdu/7vHtYVn3BlswRMQbeAEYDewBlonIfGNMukOxh4DVxpixIpJoL3+Ow/6zjDF1g3sQ+MEYM8OetDwIPNDYE2hzSg5Y4y5CY2DcK/X/x9xloDVIbtU78P0/rIRgxJ/hjAcav0ZCVZk1B0HaHNjyg7UwVNeT4IInYMA46xLC3UusX8XrPoE1cyCkKwy6ElKusqZsVsdfdob1N1nzARRlWotzDfqT9Tfplnpkq1bf0dbtwGZr0PDq962/Y1wqDL/Jap1q7AJclSWw/A34/TkozoLuI+DyF6xmfh1b0XQBodbS7m9dAnMmW8lhz5GejspST0tDS5o8eXL+3//+926//vprUHl5udfpp59eumHDBr/nn38+esWKFRlRUVHV48ePjy8vL683m3W2hDnA9OnTe3700UdbRowYUfbcc891+vnnn+sdE9HQLNgBAQEGrGXXna2E2h6WdXflZ0UqsMUYs80YUwnMAS6rUyYZ+AHAGLMBiBeRekfm2ut4y/74LeByl6Nuq2qqYd4NVpJx5TvHXlPBkZcXDL0WblsBKRPht2fh+VTI+MIax1EfY2Dn7zD/dutX70fXW10ip90Bf14K03+C4dOttRtErAF5lzwL926y+opjBsHv/4UXh8PLZ8DimVbsrUVlCWz4Ej79M7xxEax40xo4e6IrzrFalV4eBS+eYrU2xQy2xufcswkuecYaEHysL/jIvnDhE3B3Boz5N5QesD53zwyEnx+3WrAaUlFkzd3wzED47m8QlQDXfgHXfW1dHaLJRfMFdoQpn0J4D3j/T+1+YHdYWFjNKaecUjRt2rT4cePG5QLk5eV5BwYG1kRERFTv3r3bZ+HChWH11XH22WcXf/nll+HFxcWSl5fntWDBgvDafaWlpV7du3evqqiokDlz5hz6zzc4OLi6sLDwqO/KwYMHl+/du9dv3bp1/gBvv/12p5EjR7o8+LN2Wfe9e/eu3bt379odO3as/eWXX0Idl3UHa4Bmbm6u15gxYwrnz58fsX//fm+A2i6S2mXdAZq6rPuSJUtCNmzY4OdYLxxe1v3SSy/Nbcqy7q4cEQs4Zqx7gOF1yqQB44BfRSQV6AHEAVmAAb6zZ14vG2NesR8TbYzJBDDGZIqI0w5hEZkOTAfo3v2YVyS1DT89Zl3Kd+nz0HVw447t0Mm61G3w1VYT9dzJ0G8MXPA4dOxxZNncbZA21/rlm7/TmuAn+TIrQYk/3VoUqj6+AdB/rHUrzoF1H1l1ffOA9WXTZ7RVV78xx//Sw6Is2PSN1RqzbaG1sJR/mNVk//kdsOAfVkJ28jQIP4E+T1XlsOlrq4Vp8wKrhSlmsJUgDBgPwVGNrzMgFE65GVKnw9YfYMlM6zO46Alrau7hN0HsSUceU5ZvtX4sfhHK8qD3OXDG/VbyqdyvQyRcOx8+ucn6N97OTZw4Mffaa6/tPXv27G0AI0aMKBswYEBp3759+3fv3r1i6NChxfUdf/rpp5eOHTs2d8CAAf1jY2MrUlNTD5V/8MEH96WmpibFxsZWJiUllRYXF3sDTJ48OfeWW26JnzlzZvRHH320tbZ8UFCQmTlz5o4JEyb0rq6uJiUlpfTee+/Ncfa6dbWXZd0bXOxMRCYA5xtjptmfTwFSjTG3O5QJBZ4FhgBrgURgmjEmTUS6GmP22ROIBcDtxphFIpJvjAl3qCPPGFPvOIw2vdjZhq9gzlVw0jVw6X+bV1d1lfULd+EMMDUw6l5rUaUNX1hfULsXAwK9zrSa0pMubnyXijNZ6VaTu2Nzff9xzpvr3cUYa/nujV/Cxq+t8SsYCOsOiRdCwgXQ4zTw8rFaa5bMtN4HsMYGpN5kJVXH6xe3rdJhSfD8I5cEd7ZMeO32/F3W/aFuqYnWcuHu5th9Ull8uPuk5yhrfoYlL0NFgZU8jrof4oa6PwbldrrYmWoKV5Z1r2+xM1cSjBHAI8aY8+3P/wpgjPm/Y5QXYDswyBhTWGffI0CxMeZJEdkInGlvvYgBFhpjEuqLpc0mGAe3witnQURPa0yFu371F+yx5s3I+PzwtqhE68tp4JUtd6lgTbW1tkTaHOu1q0ohJAY69YGwblbLQXi3w49DYxvX919ts5KkDV9ZLRV59lWHuw6BhIuspCK6/7GThvzdsPx1q8ukLA8697e+RAdOsAbYuUO1DTLTYOevVmKTmWb9+rc5HdzuQKyWhYAwq+UlwH4L7my1MvUc1XALkzuUF1pJxtKXrRavWokXWwt1NbaFTXmUJhiqsRyXdT///POP2TLU3ATDB9iENWhzL7AMmGSMWe9QJhwoNcZUisiNwEhjzDUi0gHwMsYU2R8vAP5pjPlGRJ4ADjoM8owwxji5ZOKwNplgVJZac1oU7rVG3NftznCHzQusAZqJF1nN6sezf7z2ksmtP1q/wvN3W60bOH7uxEpAahOP8O725KOb1f8cFgc1Nmvw6cavYfO3VmLg7Qc9z7ASioQLINTpJfDHVlUGaz+01sLIWmv1eZ90TdO6T2yVsG8l7PgVdv4Gu5daLQBgJVZxJ1sDZQPC7UlD6OHkwd/hsV9w67rioqbGuoph1+9WAhbd39MRqSbQBEO1lGYlGAAiciHwDOANzDLGPCYiNwMYY2baWzneBqqBdOAGY0yeiPQCPrFX4wO8b4x5zF5nJ+ADoDuwC5hgjKl31qg2l2AYY11OmDbHGiVeO99FW2erhMI9VrKRvwsKdjs83gUFe60xBo7E29oW2NFqnk+4wBpM6O+Gye9qB7vW7T4ZfrPVveIsIasqs7pjdv5mJRV7llnjPQCikiD+NOvYHqdBSEPjnZVqWW5IMLYNHDgwz8vLq+EvDNVu1NTUyNq1azumpKT0crbfpWGhxpivgK/qbJvp8PgPoK+T47YBKceo8yBHXsraeq161xQ3c+YAACAASURBVLpCYvh0GDSx8ZfyHYuzybTaAx8/iOhl3ZyptlmtHI6Jh63cSii6DbemVXYnESshiD/tyO6TjM8Pd58kXgT718CO36ykYu8KqK4ExLpceNj10ONU6H6qDsZTbdG6nJyc5KioqAJNMhRYyUVOTk4YsO5YZVxqwWgtPNaC8f5EawQ/WE33p98FQ64Gn6PmTnHdnhXwxhiriX/SB62rWVwd3X1SS7yt8Qe1rRPdT4HA8GPXo1Qr0NwWjBUrVnT28fF5DRiALjGhLDXAOpvNNm3o0KFOr23XBMMVzwyE2GHW1RCLHreaw0O6wul3Wn32voEN1+Go5KA1h4GXlzXuwpX5LpRn1Haf7PrDGkTaLdU93TJKHUfNTTCUagrNRBtSUWQ10UcnQ7/z4IYF1uQ3HePh6/vh2RT4/XlrUidX1FTDvOuhJMf1ybSU59R2n4y611oDRpMLpZRyiSYYDcneYN13to+eF4HeZ8H1X8PUL60ZDL/7m9XK8et/rISkPj/9rzUB1EVP6aV+Siml2ixNMBqSbV9yxdmkRvGnw7Wfw/XfWc3n3z9yeLrlsvyjy2/8Gn550upWOWlKi4atlFJKeZImGA3JTrem0g6vZ36K7sOthYmm/QjdTrGmW35mEPz4GJTar7zN3WatXhkz2FpATCmllGrD3Hy9XxuUnW6tFOrKVR5xQ2HSHGvWxkVPWANCF78IqTfC5u+tOq58+/ivz6GUUkodZ5pgNCQr3ZrUqTFiUuBP71orky56En59xto++aOWmalTKaWUamU0wahPcY61lHXn5KYdH90fJrwBZz0ERfuh50j3xqeUUkq1Uppg1Kd2gGd0ExOMWpF9rZtSSinVTuggz/ocuoKkmQmGUkop1c5oglGf7HQIirSWylZKKaWUyzTBqE9WuvP5L5RSSilVL00wjqWmBnI2WAM1lVJKKdUommAcS8EuqCzWFgyllFKqCVxKMERkjIhsFJEtIvKgk/0dReQTEVkjIktFZIB9ezcR+UlEMkRkvYjc4XDMIyKyV0RW228Xuu+03CA7w7rvrC0YSimlVGM1eJmqiHgDLwCjgT3AMhGZb4xJdyj2ELDaGDNWRBLt5c8BbMA9xpiVIhICrBCRBQ7H/scY86Q7T8htstZb950TPRuHUkopdQJypQUjFdhijNlmjKkE5gCX1SmTDPwAYIzZAMSLSLQxJtMYs9K+vQjIAGLdFn1Lys6A8O66PLdSSinVBK4kGLHAbofnezg6SUgDxgGISCrQA4hzLCAi8cAQYInD5tvs3SqzRKSjsxcXkekislxElufk5LgQrptkp+v8F0oppVQTuZJgiJNtps7zGUBHEVkN3A6swuoesSoQCQbmAXcaYwrtm18CegODgUzgKWcvbox5xRgzzBgzLCoqyoVw3cBWCQc26QBPpZRSqolcmSp8D9DN4XkcsM+xgD1puA5ARATYbr8hIr5YycV7xpiPHY7Jqn0sIq8CXzTtFFrAwS1QY9MBnkoppVQTudKCsQzoKyI9RcQPmAjMdywgIuH2fQDTgEXGmEJ7svE6kGGMebrOMTEOT8cC65p6Em53aIpwbcFQSimlmqLBFgxjjE1EbgO+BbyBWcaY9SJys33/TCAJeFtEqoF04Ab74acBU4C19u4TgIeMMV8Bj4vIYKzulh3ATe47rWbKTgcvH4js5+lIlFJKqROSS6up2hOCr+psm+nw+A/gqOVCjTG/4nwMB8aYKY2K9HjKzoBOfcDHr+GySimllDqKzuTpTNZ6vYJEKaWUagZNMOqqKIb8nZpgKKWUUs2gCUZdORus+2hNMJRSSqmm0gSjLr2CRCmllGo2TTDqys4A3yAIj/d0JEoppdQJSxOMurLWQ1QieOlbo5RSSjWVfovWlZ2h4y+UUkqpZtIEw1HJASjJ1itIlFJKqWbSBMPRoQGemmAopZRSzaEJhqMsTTCUUkopd9AEw1F2OgR1guDOno5EKeUGJRU2Pk/bxz8/Tye7qNzT4SjVrri0Fkm7kZ1utV6I0+VTlFIngNJKGz9tyOHLtfv4cUM25VU1APy0MZt3pw0nNjzQwxEq1T5oglHLGOsKksGTPR2JUqqRyquqWbgxmy/WZPJDRjZlVdVEBvvzp2HduGhQV0Tg+jeXceXMP3hv2nDiIzsct9h2HSzlzd93kNItjPOSuxDo533cXlspT9IEo1b+Lqgs1hk8lTpBlFdVs2hTjj2pyKKksppOHfwYPzSWiwZ2JbVnBN5eh1sjZ994ClNeX8KEl//g3RuGk9AlpMVj/GPrQW59bwV5pVUABPv7cMGALowfGkdqfAReXtpaqtouTTBqZWdY9zrAU6lWq8JWzS+bDvDl2kwWpGdRXGGjY5Avlw6O5eJBMQzvGYGPt/OhZQNiw/jgphFMfm0Jf3rlD96+PpVBceEtFuv7S3bx8Gfr6NEpiHm3nEp2UQUfr9zDV2v38+GKPcSGBzLupFjGDomlV1Rwi8WhlKeIMcbTMbhs2LBhZvny5S1T+S9PwQ//hAd3Q0Boy7yGUu2QMYb1+wopr6puch25JZV8s34/C9ZnUVRhIyzQlzH9u3DRoBhG9O6E7zGSCmd2HSxl0muLyS+t4o3rTubk+Igmx+WMrbqGf32Rzlt/7OTMhCieu2oIoQG+h/aXVVbzXfp+Pl65l18251BjYEj3cMadFMclg2IID/JzazwAIrLCGDPM7RUrVQ+XEgwRGQM8C3gDrxljZtTZ3xGYBfQGyoHrjTHr6jtWRCKAuUA8sAO40hiTV18cLZpgzJsGuxbDXetapn6l2qHqGsNfP17DB8v3NLuukAAfzrcnFaf3iWxUUlFXZkEZk19bwr78Ml69Zhgj+0Y1Oz6A/NJK/vz+Sn7bcpAbR/bkwQuSjuimqSu7sJzPVu9j3so9bNhfhK+3cE5iNONOiuXMhM74+bjnQj9NMJQnNJhgiIg3sAkYDewBlgFXGWPSHco8ARQbY/5HRBKBF4wx59R3rIg8DuQaY2aIyINAR2PMA/XF0qIJxounQlgcTP6gZepXqp2prjHc92EaH6/ay/RRvRjZN7LJdfn7eJPSLQx/H/cNkDxQXMGU15eyNbuY/04awvn9uzSrvi3ZxUx7axn78st5bOwAJgzr1qjj0/cV8vHKPXy6eh8Hiiusrp+Urow7KY5BcWFIM65u0wRDeYIrYzBSgS3GmG0AIjIHuAxIdyiTDPwfgDFmg4jEi0g00KueYy8DzrQf/xawEKg3wWgx1VVwYBP0He2Rl1eqrbFV13D3B2nMT9vH3aP78Zdz+no6pKNEBvsz58ZTuPaNpdz63kqempDC5UNim1TXwo3Z3D57Ff4+Xrx/43CGNaHbJblrKMldk3nwgkR+2XyAeSv3MHvZbt76Yye9ozrwv2MHMrxXpybFp5QnuJJgxAK7HZ7vAYbXKZMGjAN+FZFUoAcQ18Cx0caYTABjTKaIOJ3dSkSmA9MBunfv7kK4TXBwC9RUQXT/lqlfqXakqrqGO+es5su1mTwwJpFbzuzt6ZCOKSzIl3enDWfaW8u464PVlFZWM2m46//PGGN4/dft/O9XGSR0CeXVa4YS1zGoWTH5eHtxVmJnzkrsTEFZFV+vzeTjlXuJDg1oVr1KHW+udPA5a5er268yA+goIquB24FVgM3FY+tljHnFGDPMGDMsKso9/aRHObQGiV6iqlRzVNpquO39lXy5NpO/X5TUqpOLWsH+Prx5XSpn9ovioU/W8uqibS4dV2Gr5oF5a3j0ywzOS+7CRzePaHZyUVdYoC8TU7vzwc0jjuvcHUq5gystGHsAx87EOGCfYwFjTCFwHYBYHYXb7begeo7NEpEYe+tFDJDdpDNwh6x0EG+I7OexEFTb9eOGLJ79YQsd/LyJCvEnMtifqBB/ooL9iTx070enDv71Dghs7Sps1fz5vZV8n5HNI5ckM/W0np4OyWUBvt68PGUYd81dzWNfZVBSaeOOc/oec9zDgeIKbnl3Bct25PGXs/tw57n9dE4LpepwJcFYBvQVkZ7AXmAiMMmxgIiEA6XGmEpgGrDIGFMoIvUdOx+4Fqv141rgMzecT9NkZ0CnPuDj77EQVNu0aFMON7+zkq7hAXiLH6t25ZNTVEGZk0s2vQQiOvg5TUA6h/rTNTyQ2PBAokMDWl0iUl5VzU3vrODnTTk8evkArj6lh6dDajQ/Hy+eu2oIgX7ePPP9ZkoqbDx0YdJRSUb6vkJufHs5B4or+O9VQ7gkpauHIlaqdWswwTDG2ETkNuBbrEtNZxlj1ovIzfb9M4Ek4G0RqcYawHlDfcfaq54BfCAiNwC7gAnuPbVGyE6HroM99vKqbVq87SDT31lO787BzL5x+BHzG5RU2MgpquBAccUR9znFlfb7CrbllJBTXEGlreaIen28hJjwAGLDA4kNDyKuYyCxHQOJCw8krmMQXcIC3HZ5oyvKKqu58e3l/Lb1ADPGDWRiaguNlToOvL2Ex8cPooOfN6/+sp2SymoevWzAodaJb9fv5665qwkN8OWjm09lYFyYhyNWqvVyaSZPY8xXwFd1ts10ePwH4HSYuLNj7dsPAuc0JtgWUVkCeTtg8KQGiyrlqpW78rjhzWXEdQzinRtSj5o8qYO/Dx38fRrsVzfGUFRhI7uwnD15ZezNL2NvXtmhx79tOUBWUTmOV5uLQHRIwKHEIzY8kF5RwZzfP5oQhwmf3KG00sYNby5n8faDPHFFClcMjXNr/Z7g5SU8cml/Ovj78OLCrZRW2HhiQgov/7yVJ7/bREq3cF6dMpTOOuhSqXrpVOE5GwCjU4Qrt1m3t4BrZy0lMsSf96YNJzK46V1vIkJogC+hAb706ex87YxKWw2ZBfbEI9+efOSVsTe/lBU78/hyTSa2GsMj8324Ymgc154aT083DBgsrrBx/RvLWL4zl/9cObjJl3i2RiLC/WMS6eDvwxPfbmTZjjz25pdx+eCuzBg/iABfXbBMqYZogpGlV5Ao99mUVcSU15cQGuDLe9OGH5dLC/18vOjRqQM9OjlPGqprDGv25PPOHzt5b8lO3vx9B2cmRDH11HhG9Y1q0uDEwvIqps5aStqeAp67aggXD2qb4xD+fFYfgv19eOyrDO4fk8AtZ/Ru1oRXSrUnuhbJNw/B8lnw0D7wOn791qrt2ZZTzJUvL8ZL4IObWudlhdlF5cxespt3l+wkp6iCXpEduGZED8YPjXO5+6SgrIprZi1l/d4C/nvVEC4YGNPCUXteVXVNs6Ym9zSdyVN5giYYb18G5QUwfaF761Xtyu7cUq58+Q8qbTXMvemUY3ZntBaVthq+XpfJm7/vYNWufIL9Xes+yS+t5OrXl7BxfxEvTh7K6OTo4xi1aipNMJQnaBdJdgb00SnCVdPtLyhn0muLKamwMWf6iFafXIDVrXLZ4FguGxzL6t35vPX7jga7T3JLKpn82hK25hTzypRhnJXodPJdpZQC2nuCUXIQirN0/IVqspyiCia9tpi8kirenTac5K6hng6p0QZ3C2fwnwbz1wsTD3WfTH1j2RHdJxW2Gia/uoQdB0t47ZphjOrXQrPqKqXajPadYNROER6tV5CoxssvrWTK60vIzC/n7RtSGdwt3NMhNUvnkADuOLcvt5zZ+1D3ySOfp/Pkd5sIC/Qlt6SSN6aezKl9mr4qqlKq/dAEA/QSVdVoheXWQMdtB0qYde3JnNyE1TNbK2fdJ0u2HeSN607mFF3NUynlIk0wAjtCsA5UU64rqbBx3RvLyMgs5OUpQzm9b9v9RV/bfaKUUo114l535Q5Z6dC5vzX1oVIuKK+ypsVetSuP5yYO4exETU6VUsqZ9ptgGGNdQaIDPJWLKmzV3PzuCv7YdpCnrkxpF/M/KKVUU7XfLpKC3VBZpAM827jyqmq+WpvJsh25hAX6WauUhvgTGexHZ/vS6WGBvg3OzmirruEvs1excGMO/zduIGOHnPhrbiilVEtqvwlGdoZ1rwM826SN+4uYvXQXH6/cQ2G5jdAAH8qqqqmqPnpiOV9vObREemSwtTx6bRISFRJAZLAf7y/dxbfrs/jHJclcdQKvFqqUUsdL+00wsuyrxmsXSZtRVlnNF2v2MXvpLlbuysfP24sxA7pwVWp3TullXeVRUFZ1aDn0nKLapdIrDy2Zvr+gnHV7CzhYUkl1zZHJyANjErnutJ6eODWllDrhtN8EIzsDQuMgIMzTkahmysgsZPbSXXyyai9F5TZ6RXXg7xclMe6kOCI6HLlMeniQH+FBfvSNrn+2zZoaQ15p5aFEJMDXu01diqqUUi3NpQRDRMYAzwLewGvGmBl19ocB7wLd7XU+aYx5Q0QSgLkORXsBDxtjnhGRR4AbgRz7voeMMV8152QaJTtdx1+cwEorbXyRlsn7S3exenc+fj5eXGhvrUjtGdHsFS+9vIROwf50CvYnsYubglZKqXakwQRDRLyBF4DRwB5gmYjMN8akOxT7M5BujLlERKKAjSLynjFmIzDYoZ69wCcOx/3HGPOkm87FddVVcGAT9DnnuL+0ap71+wqYvXQXn63aR1GFjT6dg3n44mTGnRRLeJBfwxUopZQ6LlxpwUgFthhjtgGIyBzgMsAxwTBAiFg/G4OBXMBWp55zgK3GmJ3Njrq5Dm6F6kprDgzV6hWUVfH12kxmL91F2p4C/H28uGhQDJNSuzO0R8dmt1YopZRyP1cSjFhgt8PzPcDwOmWeB+YD+4AQ4E/GmJo6ZSYCs+tsu01ErgGWA/cYY/LqvriITAemA3Tv7qbR+4emCNcBnq3V7txSFqRn8X1GFku352KrMSREh/DIJcmMHRJHWJCvp0NUSilVD1cSDGc/D+te63c+sBo4G+gNLBCRX4wxhQAi4gdcCvzV4ZiXgH/Z6/oX8BRw/VEvZMwrwCsAw4YNO/oaw6bIzgDxhsh+bqlONV9NjWHt3oJDScWG/UUA9IsOZvqoXpzXvwspcWHaWqGUUicIVxKMPUA3h+dxWC0Vjq4DZhhjDLBFRLYDicBS+/4LgJXGmKzaAxwfi8irwBeND7+JstOhU2/wDThuL6mOVl5VzR9bD/JdehY/ZGSRXVSBt5dwcnxH/n5REqOTo+nRqYOnw1RKKdUEriQYy4C+ItITa5DmRGBSnTK7sMZY/CIi0UACsM1h/1XU6R4RkRhjTKb96VhgXePDb6LsdOgy6Li9nDost6SSHzdk8316Fos251BaWU0HP2/OSIhidHI0Z/brTMcOOlhTKaVOdA0mGMYYm4jcBnyLdZnqLGPMehG52b5/JlYXx5sisharS+UBY8wBABEJwroC5aY6VT8uIoOxukh2ONnfMipLIHc7DJp4XF5OQX5pJR8s382C9CxW7MyjxkCX0ADGnRTL6OQunNIrAn8fb0+HqZRSyo1cmgfDPj/FV3W2zXR4vA847xjHlgKdnGyf0qhI3SVnI2B0Dozj5JfNOdz7YRpZhRUkx4Ry29l9GZ0UzYDYUB1PoZRSbVj7m8nz0BUkmmC0pPKqah7/ZiOzfttOn87BvHbNyQyM01lTlVKqvWiHCUYG+ARCx3hPR9JmZWQWcuec1WzMKmLqqfE8eEEiAb7aBaKUUu1J+0swstZDVAJ46Reeu9XUGGb9tp3Hv9lIWJAvb153MmcmdPZ0WEoppTyg/SUY2RnQ+2xPR9HmZBaUcc8Hafy+9SCjk6OZMW4gnYL9PR2WUkopD2lfCUZpLhTv1wGebvbFmn089PFabDWGf48fyJXDuukATqWUaufaV4KhU4S7VVF5Ff/4bD0fr9pLSrdwnvnTYHpG6sRYSiml2luCkVWbYJw4i5wVV9h4+LN1fLkmk2B/H0ICfAgN9CU0wNd6HOBLaKAPIQG+hNr3HfnYug/288HLy32tCst25HLX3NXsyy/jjnP6ctvZffD19nJb/UoppU5s7SvByE6HgHAI6eLpSFyydk8Bt89eya7cUiYM7Yavj1BYZqOovIrCchtZheUUldsoLK+itLK63rr8fbxI6BJCUpdQkmJCSIoJJTEmlLDAxi0aVmmr4dkfNvHSwq3EdQziw5tPZWiPjs05TaWUUm1Q+0swovtDKx8fYIxh1m87mPF1BpHB/syZPoLUnhH1HlNVXUOxPdk4nIRYjwvLq9hfUM6G/UUsyMhi7vLDi+PGhgeSFBNKsj3pSIoJpXtEkNPWjq05xdw5ZzVr9xZw5bA4Hr6kP8H+7esjpJRSyjXt59vBGOsKkkFXejqSeuWWVHLfh2n8sCGb0cnRPHHFIMKDGl6bw9fbi44d/Bpcx8MYQ3ZRBemZhWRkFpKRWURGZiE/bsiixr5WbQc/bxK6hJBoTziSY0JIzyzisS/TCfD1ZubVJzFmQIw7TlcppVQb1X4SjII9UFHYqmfwXLztIHfMWUVeSRWPXJLMtafGu/1qDBEhOjSA6NAAznKYo6K8qppNWUWHko70zEI+T9vH+0t2HSozsm8kT05IITpUV6FVSilVv/aTYGRnWPetMMGorjE898Nm/vvjZuI7deD1a09mQOzxnVY7wNebQXHhDIoLP7TNGMPe/DIyMouorjGclxzt1oGiSiml2q52lGCst+5b2SWqmQVl3DFnNUu35zLupFj+ddkAOrSScQ0iQlzHIOI6Bnk6FKWUUieY1vFNdjxkZ0BoLASGN1z2OPkhI4t7P0yjwlbDUxNSGD80ztMhKaWUUm7RjhKM9FbTPVJhq+bfX1srjSbHhPL8pCH0igr2dFhKKaWU27SPBKPaBjmboNdZno6E7QdKuH32StbtLdSVRpVSSrVZLk29KCJjRGSjiGwRkQed7A8Tkc9FJE1E1ovIdQ77dojIWhFZLSLLHbZHiMgCEdlsv2+52Zpyt0F1hTUHhgd9umovFz/3C7tzy3hlylAeubS/JhdKKaXapAYTDBHxBl4ALgCSgatEpG5fw5+BdGNMCnAm8JSIOE7IcJYxZrAxZpjDtgeBH4wxfYEf7M9bhocHeJZW2rj3wzTunLua5K6hfH3HSM7rf2LMJqqUUko1hStdJKnAFmPMNgARmQNcBqQ7lDFAiFiTNgQDuYCtgXovw0pGAN4CFgIPuBp4o2RngHhBZEKLVF+fSlsN095azh/bDvKXs/vwl3P64qNrdiillGrjXPmmiwV2OzzfY9/m6HkgCdgHrAXuMMbU2PcZ4DsRWSEi0x2OiTbGZALY7zvjhIhMF5HlIrI8JyfHhXCd8A+BPueC7/GdIMoYw98+WcvvWw/y5BUp3H1egiYXSiml2gVXvu2czaxk6jw/H1gNdAUGA8+LSKh932nGmJOwulj+LCKjGhOgMeYVY8wwY8ywqKioxhx62Km3w+QPm3ZsM7y4cCsfrtjDHef01UtQlVJKtSuuJBh7gG4Oz+OwWiocXQd8bCxbgO1AIoAxZp/9Phv4BKvLBSBLRGIA7PfZTT2J1mh+2j6e+HYjY4fEcue5fT0djlJKKXVcuZJgLAP6ikhP+8DNicD8OmV2AecAiEg0kABsE5EOIhJi394BOA9YZz9mPnCt/fG1wGfNOZHWZPmOXO79MI3U+AhmjB/o9vVElFJKqdauwUGexhibiNwGfAt4A7OMMetF5Gb7/pnAv4A3RWQtVpfKA8aYAyLSC/jE/gXrA7xvjPnGXvUM4AMRuQErQZng5nPziB0HSrjx7eXEhgfy8pSh+PvoZahKKaXaHzGm7nCK1mvYsGFm+fLlDRf0kPzSSsa9+Dt5pZV8cutpxEd28HRISimFiKyoM02AUi2ufczkeRxU2KqZ/s4K9uSV8d6NwzW5UEop1a5pguEGxhj+Om8tS7fn8uzEwZwcH+HpkJRSSimP0kkZ3ODZHzbz8aq93DO6H5cNrjtFiFJKKdX+aILRTJ+s2sMz329m/Elx3HZ2H0+Ho5RSSrUKmmA0w5JtB7n/ozWc0iuC/xunl6MqpZRStTTBaKKtOcVMf2cF3SKCePnqYfj56FuplFJK1dJvxSbILank+jeX4eMlvDk1lbAgX0+HpJRSSrUqehVJI5VXVTP97eVkFpQz+8ZT6N4pyNMhKaWUUq2OtmA0Qk2N4b6P1rB8Zx5PX5nC0B4dPR2SUkop1SppgtEI//l+E5+n7eP+MQlcPKirp8NRSimlWi1NMFz04fLd/PfHLfxpWDduOaO3p8NRSimlWjVNMFywdHsuf/14Laf16cSjYwfo5ahKKaVUAzTBcMGbv2+nYwc/Xpw8FF9vfcuUUkqphui3ZQOMMSzZlsvIvpGEBerlqEoppZQrNMFowJbsYg6WVHJKz06eDkUppZQ6YbiUYIjIGBHZKCJbRORBJ/vDRORzEUkTkfUicp19ezcR+UlEMuzb73A45hER2Ssiq+23C913Wu6zeHsuAKf00gRDKaWUclWDE22JiDfwAjAa2AMsE5H5xph0h2J/BtKNMZeISBSwUUTeA2zAPcaYlSISAqwQkQUOx/7HGPOkW8/IzRZvO0hMWADdIgI9HYpSSil1wnClBSMV2GKM2WaMqQTmAJfVKWOAELEurwgGcgGbMSbTGLMSwBhTBGQAJ8x65rXjL4b3jNArR5RSSqlGcCXBiAV2Ozzfw9FJwvNAErAPWAvcYYypcSwgIvHAEGCJw+bbRGSNiMwSkVY3Lea2AyUcKK7Q7hGllFKqkVxJMJz9dDd1np8PrAa6AoOB50Uk9FAFIsHAPOBOY0yhffNLQG97+UzgKacvLjJdRJaLyPKcnBwXwnWfxdsOAjBcEwyllFKqUVxJMPYA3Ryex2G1VDi6DvjYWLYA24FEABHxxUou3jPGfFx7gDEmyxhTbW/peBWrK+YoxphXjDHDjDHDoqKiXD0vt1iyLZfOIf7E64JmSimlVKO4kmAsA/qKSE8R8QMmAvPrlNkFnAMgItFAArDNPibjdSDDGPO04wEiEuPwdCywrmmn0DKMMSzZfpBTenXS8RdKKaVUIzV4FYkxxiYitwHfAt7ALGPMC5ewrQAADIZJREFUehG52b5/JvAv4E0RWYvVpfKAMeaAiJwOTAHWishqe5UPGWO+Ah4XkcFY3S07gJvcfG7NsuNgKVmFFQzvFeHpUJRSSqkTToMJBoA9IfiqzraZDo/3Aec5Oe5XnI/hwBgzpVGRHmdLasdf6ARbSimlVKPpTJ7HsGR7LpHB/vSO6uDpUJRSSqkTjiYYThhjWLztIMN76fwXSimlVFNoguHE7twyMgvKOaWnjr9QSimlmkITDCcWb7fGX+gEW0oppVTTaILhxOJtB+nUwY8+nYM9HYpSSil1QtIEw4kl23JJ1fVHlFJKqSbTBKOO3bml7M0v0+4RpZRSqhk0wahjyfZcAJ1gSymllGoGTTDqWLLtIOFBvvTrHOLpUJRSSqkTliYYdSzZnsvwnhF4een4C6WUUqqpNMFwsC+/jF25pTo9uFJKKdVMmmA4WGKf/0LHXyillFLNowmGgyXbcgkL9CWpS6inQ1FKKaVOaJpgOFi87SAnx+v4C6WUUqq5NMH4/+3df4wU5R3H8feHOw5KpfyoaBFOREOsP1oRT6W/rInVAqmiTayYpiXWxJJIok3aSGJiTPpP1domrUaiKYE2Rq1RK20wakyTJk3u5Er4qSK4B/LLA+8UEIoIfPvHDulm2eUGdnbnvP28ksnOzPM8O999dnL73XmenUv07jvElr6DzPTwiJmZWc2cYCQ6C/7/I2ZmZllJlWBImiVpo6TNkhZVKB8j6e+S1kjaIOmOgdpKGi/pdUmbksdx2byk09NZ6Gf0yFYumuj5F2ZmZrUaMMGQ1AI8DswGLgZul3RxWbW7gbci4jLgWuBRSW0DtF0EvBER04A3ku3cdPUU51+0eP6FmZlZzdJcwbgK2BwRhYg4DDwLzC2rE8BoFf872BlAP3BkgLZzgWXJ+jLg5ppeSQ127z9EYc8Bz78wMzPLSJoEYxKwrWR7e7Kv1GPARcBOYB1wT0QcG6Dt2RGxCyB5PKvSwSXdJalbUveePXtShHvqugrJ/x/xDbbMzMwykSbBqDRmEGXb3wdWA+cA04HHJH0pZduTiognI6IjIjomTJhwKk1T6+rp44wRrVxyjudfmJmZZSFNgrEdaC/ZnkzxSkWpO4AXo2gz0AN8dYC2vZImAiSPu089/Gx0FfrpOG8crS3+UY2ZmVkW0nyirgSmSZoqqQ2YBywvq/M+cB2ApLOBC4HCAG2XA/OT9fnAy7W8kNP14Sefsmn3Jx4eMTMzy1DrQBUi4oikhcCrQAuwJCI2SFqQlC8Gfg0slbSO4rDIfRHxIUCltslT/wb4q6Q7KSYot2b70tJ5s6c4/8ITPM3MzLIzYIIBEBErgBVl+xaXrO8EbkjbNtnfR3LVI09dhT5GtbVw6aQxeYdiZmY2ZDT9pIPOQj9XTBnHcM+/MDMzy0xTf6r2HzjMxt79vj24mZlZxpo6wfD8CzMzs/po6gSjs9DHyOHD+NqksXmHYmZmNqQ0dYLR1dNPx5TxtLU2dTeYmZllrmk/WT8+eJh3PtjH1VM9PGJmZpa1pk0w3uzpJwKu9gRPMzOzzDVtgtHV08+I1mFc1u77X5iZmWWtiROMPmacO44RrS15h2JmZjbkNGWCsfe/n7Fh5z6u9s9TzczM6qIpE4zuLcX5F77BlpmZWX00ZYLR1dNPW+swprf7/hdmZmb10JQJRmehj+ntYxk53PMvzMzM6qHpEoz9hz5j/Y69Hh4xMzOro6ZLMLq3fsSxgJm+wZaZmVndpEowJM2StFHSZkmLKpT/StLqZFkv6aik8ZIuLNm/WtI+SfcmbR6UtKOkbE7WL66SzkIfw1vE5eeOa8ThzMzMmlLrQBUktQCPA9cD24GVkpZHxFvH60TEI8AjSf0bgV9ERD/QD0wveZ4dwEslT//7iPhtRq8lla5CP9Pbx/KFNs+/MDMzq5c0VzCuAjZHRCEiDgPPAnNPUv924JkK+68D3ouIraceZjYOfHqEdTv2cvVUz78wMzOrpzQJxiRgW8n29mTfCSSNAmYBL1QonseJicdCSWslLZFU9zGL7q0fcfRY+AZbZmZmdZYmwVCFfVGl7o3Av5Phkf8/gdQG3AQ8X7L7CeACikMou4BHKx5cuktSt6TuPXv2pAi3uq5CH63DxBVTPP/CzMysntIkGNuB9pLtycDOKnUrXaUAmA2sioje4zsiojcijkbEMeApikMxJ4iIJyOiIyI6JkyYkCLc6rp6+vn65DGMahtw6omZmZnVIE2CsRKYJmlqciViHrC8vJKkMcB3gZcrPMcJ8zIkTSzZvAVYnzbo03Hw8BHWbPvY/57dzMysAQb8Kh8RRyQtBF4FWoAlEbFB0oKkfHFS9RbgtYg4UNo+mZdxPfDzsqd+WNJ0isMtWyqUZ2rV1o85cix8gy0zM7MGSDVWEBErgBVl+xaXbS8FllZoexA44VM9In5yCnHWrKunjxbPvzAzM2uIprmTZ2ehj0snjeGMEZ5/YWZmVm9NkWAc+uwoa7btZaZ/nmpmZtYQTZFgrHr/Iw4fPcZM32DLzMysIZoiwegs9DNM0HGe51+YmZk1QlMkGJPGjuTWK9oZPXJ43qGYmZk1haaY8Xjbledy25Xn5h2GmZlZ02iKKxhmZmbWWE4wzMzMLHNOMMzMzCxzTjDMzMwsc04wzMzMLHNOMMzMzCxzTjDMzMwsc04wzMzMLHOKiLxjSE3SHmDraTY/E/gww3Cy5vhq4/hq4/hqN5hjnBIRE/IOwprL5yrBqIWk7ojoyDuOahxfbRxfbRxf7T4PMZo1kodIzMzMLHNOMMzMzCxzzZRgPJl3AANwfLVxfLVxfLX7PMRo1jBNMwfDzMzMGqeZrmCYmZlZgzjBMDMzs8wNuQRD0ixJGyVtlrSoQrkk/SEpXytpRgNja5f0T0lvS9og6Z4Kda6VtFfS6mR5oFHxJcffImldcuzuCuV59t+FJf2yWtI+SfeW1Wlo/0laImm3pPUl+8ZLel3SpuRxXJW2Jz1X6xjfI5LeSd6/lySNrdL2pOdCHeN7UNKOkvdwTpW2efXfcyWxbZG0ukrbuvef2aAWEUNmAVqA94DzgTZgDXBxWZ05wCuAgJlAVwPjmwjMSNZHA+9WiO9a4B859uEW4MyTlOfWfxXe6w8o3kAot/4DrgFmAOtL9j0MLErWFwEPVYn/pOdqHeO7AWhN1h+qFF+ac6GO8T0I/DLF+59L/5WVPwo8kFf/efEymJehdgXjKmBzRBQi4jDwLDC3rM5c4M9R1AmMlTSxEcFFxK6IWJWs7wfeBiY14tgZyq3/ylwHvBcRp3tn10xExL+A/rLdc4Flyfoy4OYKTdOcq3WJLyJei4gjyWYnMDnr46ZVpf/SyK3/jpMk4EfAM1kf12woGGoJxiRgW8n2dk78AE9Tp+4knQdcDnRVKP6GpDWSXpF0SUMDgwBek/QfSXdVKB8U/QfMo/of9jz7D+DsiNgFxaQSOKtCncHSjz+jeEWqkoHOhXpamAzhLKkyxDQY+u87QG9EbKpSnmf/meVuqCUYqrCv/He4aerUlaQzgBeAeyNiX1nxKoqX/S8D/gj8rZGxAd+KiBnAbOBuSdeUlQ+G/msDbgKer1Ccd/+lNRj68X7gCPB0lSoDnQv18gRwATAd2EVxGKJc7v0H3M7Jr17k1X9mg8JQSzC2A+0l25OBnadRp24kDaeYXDwdES+Wl0fEvoj4JFlfAQyXdGaj4ouIncnjbuAlipeiS+Xaf4nZwKqI6C0vyLv/Er3Hh42Sx90V6uR9Hs4HfgD8OCIqfjCnOBfqIiJ6I+JoRBwDnqpy3Lz7rxX4IfBctTp59Z/ZYDHUEoyVwDRJU5NvufOA5WV1lgM/TX4NMRPYe/xydr0lY7Z/At6OiN9VqfOVpB6SrqL4HvU1KL4vShp9fJ3iZMD1ZdVy678SVb855tl/JZYD85P1+cDLFeqkOVfrQtIs4D7gpog4WKVOmnOhXvGVzum5pcpxc+u/xPeAdyJie6XCPPvPbNDIe5Zp1gvFXzm8S3GG+f3JvgXAgmRdwONJ+Tqgo4GxfZviZdy1wOpkmVMW30JgA8VZ8Z3ANxsY3/nJcdckMQyq/kuOP4piwjCmZF9u/Ucx0dkFfEbxW/WdwJeBN4BNyeP4pO45wIqTnasNim8zxfkLx8/BxeXxVTsXGhTfX5Jzay3FpGHiYOq/ZP/S4+dcSd2G958XL4N58a3CzczMLHNDbYjEzMzMBgEnGGZmZpY5JxhmZmaWOScYZmZmljknGGZmZpY5JxhmZmaWOScYZmZmlrn/AXVmLVRozx40AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "#-----------------------------------------------------------\n",
    "# Retrieve a list of list results on training and test data\n",
    "# sets for each training epoch\n",
    "#-----------------------------------------------------------\n",
    "acc      = history.history[     'accuracy' ]\n",
    "val_acc  = history.history[ 'val_accuracy' ]\n",
    "loss     = history.history[    'loss' ]\n",
    "val_loss = history.history['val_loss' ]\n",
    "\n",
    "epochs   = range(len(acc)) # Get number of epochs\n",
    "\n",
    "#------------------------------------------------\n",
    "# Plot training and validation accuracy per epoch\n",
    "#------------------------------------------------\n",
    "plt.plot  ( epochs,     acc, label='Training Accuracy' )\n",
    "plt.plot  ( epochs, val_acc, label='Validation Accuracy' )\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.title ('Training and validation accuracy')\n",
    "plt.figure()\n",
    "\n",
    "#------------------------------------------------\n",
    "# Plot training and validation loss per epoch\n",
    "#------------------------------------------------\n",
    "plt.plot  ( epochs,     loss, label='Training Loss' )\n",
    "plt.plot  ( epochs, val_loss, label='Validation Loss' )\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.title ('Training and validation loss'   )"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Course 2 - Part 6 - Lesson 3 - Notebook.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "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
}