CIFAR_Image_Classification_Using_Keras(Template_All_Models).ipynb

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  "metadata": {
    "colab": {
      "name": "Image_Classification_Using_Keras(Template_All_Models).ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aayushkubb/Deep_Learning_Tutorial/blob/master/CIFAR_Image_Classification_Using_Keras(Template_All_Models).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fWq3OJ_G-UX7"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from __future__ import print_function\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jY2Y-QcN-WO-"
      },
      "source": [
        "from keras.datasets import cifar10\n",
        "(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DVQsq4c1-aKg"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sgggCbEY-cK4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "outputId": "9def2056-c655-4963-c51d-b55473f2be23"
      },
      "source": [
        "print('Training data shape : ', train_images.shape, train_labels.shape)\n",
        "\n",
        "print('Testing data shape : ', test_images.shape, test_labels.shape)\n",
        "\n",
        "# Find the unique numbers from the train labels\n",
        "classes = np.unique(train_labels)\n",
        "nClasses = len(classes)\n",
        "print('Total number of outputs : ', nClasses)\n",
        "print('Output classes : ', classes)\n",
        "\n",
        "plt.figure(figsize=[4,2])\n",
        "\n",
        "# Display the first image in training data\n",
        "plt.subplot(121)\n",
        "plt.imshow(train_images[0,:,:], cmap='gray')\n",
        "plt.title(\"Ground Truth : {}\".format(train_labels[0]))\n",
        "\n",
        "# Display the first image in testing data\n",
        "plt.subplot(122)\n",
        "plt.imshow(test_images[0,:,:], cmap='gray')\n",
        "plt.title(\"Ground Truth : {}\".format(test_labels[0]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training data shape :  (50000, 32, 32, 3) (50000, 1)\n",
            "Testing data shape :  (10000, 32, 32, 3) (10000, 1)\n",
            "Total number of outputs :  10\n",
            "Output classes :  [0 1 2 3 4 5 6 7 8 9]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Ground Truth : [3]')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x144 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DbwUvTqo-euS"
      },
      "source": [
        "# Find the shape of input images and create the variable input_shape\n",
        "nRows,nCols,nDims = train_images.shape[1:]\n",
        "train_data = train_images.reshape(train_images.shape[0], nRows, nCols, nDims)\n",
        "test_data = test_images.reshape(test_images.shape[0], nRows, nCols, nDims)\n",
        "input_shape = (nRows, nCols, nDims)\n",
        "\n",
        "# Change to float datatype\n",
        "train_data = train_data.astype('float32')\n",
        "test_data = test_data.astype('float32')\n",
        "\n",
        "# Scale the data to lie between 0 to 1\n",
        "train_data /= 255\n",
        "test_data /= 255\n",
        "\n",
        "# Change the labels from integer to categorical data\n",
        "train_labels_one_hot = tf.keras.utils.to_categorical(train_labels)\n",
        "test_labels_one_hot = tf.keras.utils.to_categorical(test_labels)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OfLfswHqKpyn"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uHt2mm-O_CAn"
      },
      "source": [
        "https://github.com/spmallick/learnopencv/blob/master/KerasCNN-CIFAR/keras-cnn-cifar10.ipynb"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tQjJs11Z-nBq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9723f7f5-0e67-47ca-d75b-6e00e115b8f5"
      },
      "source": [
        "# Display the change for category label using one-hot encoding\n",
        "print('Original label 0 : ', train_labels[0])\n",
        "print('After conversion to categorical ( one-hot ) : ', train_labels_one_hot[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Original label 0 :  [6]\n",
            "After conversion to categorical ( one-hot ) :  [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OnFdWWAo-rGs"
      },
      "source": [
        "def createModel():\n",
        "    model = Sequential()\n",
        "    # The first two layers with 32 filters of window size 3x3\n",
        "    model.add(Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=input_shape))\n",
        "    model.add(Conv2D(32, (3, 3), activation='relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "    model.add(Dropout(0.25))\n",
        "\n",
        "    model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
        "    model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "    model.add(Dropout(0.25))\n",
        "\n",
        "    model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
        "    model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "    model.add(Dropout(0.25))\n",
        "\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(512, activation='relu'))\n",
        "    model.add(Dropout(0.5))\n",
        "    model.add(Dense(nClasses, activation='softmax'))\n",
        "    \n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MYnXNZh2-u-a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77f9bdb3-1284-4ae9-f6e9-924f4caf105d"
      },
      "source": [
        "model1 = createModel()\n",
        "batch_size = 256\n",
        "epochs = 5\n",
        "model1.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "model1.summary()\n",
        "\n",
        "history = model1.fit(train_data, train_labels_one_hot, batch_size=batch_size, epochs=epochs, verbose=1, \n",
        "                   validation_data=(test_data, test_labels_one_hot))\n",
        "model1.evaluate(test_data, test_labels_one_hot)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 30, 30, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 15, 15, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 15, 15, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 15, 15, 64)        18496     \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 13, 13, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 6, 6, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 6, 6, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 6, 6, 64)          36928     \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 4, 4, 64)          36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 2, 2, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 2, 2, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 512)               131584    \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                5130      \n",
            "=================================================================\n",
            "Total params: 276,138\n",
            "Trainable params: 276,138\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/5\n",
            "196/196 [==============================] - 49s 26ms/step - loss: 2.1576 - accuracy: 0.1910 - val_loss: 1.7708 - val_accuracy: 0.3525\n",
            "Epoch 2/5\n",
            "196/196 [==============================] - 4s 21ms/step - loss: 1.7550 - accuracy: 0.3635 - val_loss: 1.4910 - val_accuracy: 0.4535\n",
            "Epoch 3/5\n",
            "196/196 [==============================] - 4s 21ms/step - loss: 1.5309 - accuracy: 0.4438 - val_loss: 1.3911 - val_accuracy: 0.4911\n",
            "Epoch 4/5\n",
            "196/196 [==============================] - 4s 21ms/step - loss: 1.3814 - accuracy: 0.5073 - val_loss: 1.2762 - val_accuracy: 0.5363\n",
            "Epoch 5/5\n",
            "196/196 [==============================] - 4s 21ms/step - loss: 1.2642 - accuracy: 0.5477 - val_loss: 1.1006 - val_accuracy: 0.6056\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 1.1006 - accuracy: 0.6056\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.1005927324295044, 0.6055999994277954]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7UWNr7cr-xEq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "outputId": "b43a59f1-9428-493f-ab06-514d20c6b275"
      },
      "source": [
        "plt.figure(figsize=[8,6])\n",
        "plt.plot(history.history['loss'],'r',linewidth=3.0)\n",
        "plt.plot(history.history['val_loss'],'b',linewidth=3.0)\n",
        "plt.legend(['Training loss', 'Validation Loss'],fontsize=18)\n",
        "plt.xlabel('Epochs ',fontsize=16)\n",
        "plt.ylabel('Loss',fontsize=16)\n",
        "plt.title('Loss Curves',fontsize=16)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Loss Curves')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/hFat4MABZ+NSKhC4V7L43pdDFixYQJkyZfj4448ZNGgQHTp0oFWrVhQuXNixmK6kTJkyHD58OM3w/uXLl5N63dfDNVqwc+fONPd27dqV4hmQDyHNmjXjueee4/PPP2fbtm2cP3+eCRMmpHndhx56iKVLl3L48GGaNGnCpEmT/HZ4XhP79apbFz76SLbEARw8CC1bwhHnlwMopQJLSEgIxpgUveK4uDhefPFFB6NK3x133EF8fDxTpkxJ0T5r1izOnDlz3a/bqVMnjDH897//5fLly0ntR44cYe7cuZQuXZratWsDcPz48TTfX7lyZSIjIzl58iQAZ86cSfE6ABEREVSpUgW4+ikOX+PIdreAceutsGIFdOwIly7Br7/KsPzGjVCokNPRKaUCRLdu3Xj88cdp3749Xbp04ezZsyxevNhni8gMGjSIGTNmMHbsWH755Zek7W5Lly6lQoUK6S5m+yeVKlVi1KhRTJo0iSZNmtCjR4+k7W7nz59n0aJFSVMFgwcP5o8//qBNmzZJ1fiio6M5d+4cffv2BWTR3JAhQ+jatSuVKlUiV65cbNmyhdmzZ9OwYUMqVaqUae+JN2liz6hWreCdd6BLF9njvnOnLKhbvx4ysPpTKaVcRo0ahbWWt956i4cffpgiRYrQo0cP7rvvPqpWrep0eGmEh4ezfv16Ro0axYoVK1i6dCkNGzZk/fr1DBo0KE1RnWsxceJEKlSowLRp03jssccICwujYcOGLF68OEWJ3j59+jBv3jzefvttYmJiyJMnD1WrVuXdd9+la9euANSsWZMuXbqwceNGFi1aRHx8PKVKleKJJ57g0UcfzfD74BTjawserke9evXs5s0Or7FbsgR693bPw916q8zD58zpbFxK+ajdu3cnDXmq4BAfH0+hQoVo2LAhq1evdjocn3Y1/30YY7ZYa+ulbtc59sxyzz0wc6b7+ssv5YS4ixedi0kppRxy4cKFNG3Tp0/n9OnTtG7d2oGIgocOxWemQYPg/HlwFXBYuxZ69IB33wUfnQtTSqmsMHjwYC5evEijRo0IDw/n66+/ZvHixVSoUIEhekpmltIee2Z75BF49ln39QcfQL9+Mv+ulFJBok2bNhw8eJBnn32WRx55hI0bNzJo0CC++OKLaz6xTl0b7bFnhSeflJ77xIlyvWSJzLXPnOmVspRKKeW0vn37Jq0+V96lPfasYAy88AIMH+5umz0bRo50tMiFUkqpwKeJPasYA6+9JsPwLq++CuPGOReTUkqpgKeJPStlyyY99W7d3G3PPguTJjkXk1JKqYCmiT2rhYbCokXQoYO7bcwYmDbNuZiUUkoFLE3s3hAWJlveEo9YBGT+/e23HQtJKaVUYNLE7i2RkbL1rWFDd9uAAZLwlVJKqUyiid2bcueGjz+GmjXlOiEBevWSU+KUUkqpTKCJ3dvy54dPPgHXqUGXL0PXrnIinFJKKZVBmtidcMMNsG4dlCkj1xcvwh13wLffOhqWUsq/7N+/H2MM48ePT9FujKF///5X9Rrjx4/HGMP+/fszPb558+ZhjGGjdly8ShO7U0qUkKNdixWT6/Pn5bjX7dudjUsplanuvvtujDH88MMP6T5jraVs2bLky5fP4+Epvmzjxo2MHz+e06dPOx2KR64PPw8++KDToXiNJnYnlSsnPfdCheT69Glo3Rr27HE2LqVUphk4cCAAc+fOTfeZDRs2sH//fnr27ElkZGSGf+aFCxeYNWtWhl/namzcuJFnnnnGY2Lv06cPFy5coEmTJl6JRQlN7E6rUkXm3PPmleuYGGjVCvbtczYupVSmaNOmDSVLlmTRokVcunTJ4zOupO/6EJBRERERZPeBEyVDQkKIiIggWzZNNd6k77YvqF1bVsvnzCnXhw5Jcj90yNm4lFIZli1bNvr378+JEyf44IMP0tw/e/Ys7733HtWrV6d+/fqcO3eOsWPH0rBhQwoVKkR4eDgVKlTgscce4++//76qn+lpjj0hIYEXXniBsmXLEhERQfXq1Vm0aJHH79+zZw/Dhg2jWrVq5M6dmxw5clC3bl1mz56d4rn+/fvzzDPPAFC2bFmMMSnm/NObYz9+/DjDhw+nZMmShIWFUbJkSYYPH86JEydSPOf6/k8//ZSXXnqJ8uXLEx4eTsWKFXk7C+qA7Nixg86dO1OwYEEiIiKoWrUqkyZNIj7V6ZwHDx5kwIABlC5dmvDwcG644QYaNWqUIqaEhAReffVVatSoQe7cucmTJw+VKlVi4MCBXL58OdNjT05Pd/MVt9wi+9w7dIDYWPjtN0nun30GUVFOR6eUyoD77ruPCRMmMHfuXLolLzEN/N///R8XLlxI6q0fOnSI2bNn07VrV3r16kVoaCibNm1i0qRJbNu2jTVr1lxXDCNHjmTKlCk0adKEESNGcOzYMYYPH065cuXSPLtx40Y+++wzbr/9dsqWLctff/3FO++8w+DBg4mJieHxxx8H4P777+fs2bMsX76cV155hUKJ04o1atRIN44zZ87QqFEjfvnlFwYMGECdOnXYtm0bb775Jp9++infffddmmNdn3jiCS5cuMD9999PeHg4b775Jv3796dChQrceuut1/V+pLZ582aaNm1K9uzZGT58OEWKFGHlypWMGTOG7du3J30IiouLo3Xr1hw6dIhhw4ZRsWJFzpw5w44dO/j888/pl3g+yHPPPcfTTz/NHXfcwdChQwkJCWHfvn188MEHxMbGZu2IirXW77/q1q1rA8aqVdaGhlor58BZW6uWtadOOR2VUplu165dTofgVS1atLAhISH28OHDKdpvvvlmGxYWZmNiYqy11sbGxtpLly6l+f6xY8dawH777bdJbfv27bOAHTduXIpnAduvX7+k6z179lhjjG3RooWNi4tLat+yZYs1xljA7tu3L6n9/PnzaX5+fHy8bdq0qc2TJ0+K+MaNG5fm+13mzp1rAbthw4aktieeeMIC9o033kjx7NSpUy1gx44dm+b7a9WqZWNjY5Pa//jjDxsWFmZ79uyZ5mem5nqPhg8ffsXnGjVqZENCQuz27duT2hISEuzdd99tAbtu3TprrbXbt2+3gJ04ceIVX6927dq2SpUq/xhfeq7mvw9gs/WQE3Uo3td07Ci15V1zUj/8AO3by6p5pYKAMb77lREDBw4kPj6e+fPnJ7Xt2bOHb775hk6dOiX1dsPCwpJ6c3FxcZw6dYrjx4/TqlUrAL69jm2xK1aswFrLyJEjCQkJSWqvU6cOrVu3TvN8Tte0IHDx4kVOnDjByZMnadOmDWfPnmVPBhb4Ll++nKioKIYMGZKi/f777ycqKorly5en+Z5hw4YRFhaWdF28eHEqVqzI3r17rzuO5I4dO8ZXX31Fp06dUow2GGN48sknk+IGyJu4HmrDhg0cO3Ys3dfMmzcvhw4d4osvvsiUGK+FJnZf1L07vPWW+/qbb+DOO2W/u1LKL3Xp0oV8+fKlWB0/Z84cAAYMGJDi2WnTplGjRg3Cw8MpUKAAUVFRNEs8a+LUqVPX/LN/++03ACpXrpzmXtWqVdO0nT9/nn//+9+UKlWKyMhIChUqRFRUVFKSu54YXPbt20elSpUIDU05ExwaGkrFihWTYk3O03RBwYIF08zJZyQmgGrVqqW5V6VKFbJly5YUV+nSpXnyySf55JNPKFq0KHXr1mX06NF8//33Kb7v+eefJyIigsaNG1O8eHF69+7N4sWL011AmZk0sfuq/v3h9dfd159+Kse/euH/FEqpzBcREUGvXr34+eef+eqrr4iPj2fBggWUKFGCtm3bJj03efJkhg8fTtGiRZkxYwYffvgha9euZd68eYAsyspqvXr1YvLkyXTo0IFFixaxevVq1q5dy4gRI7wWQ3LJRxmSk9Fo75swYQJ79+7l1VdfpXz58syePZsGDRowZsyYpGduueUWfv31V9599106d+7MDz/8QO/evalVqxYnT57M0vg0sfuyBx+EF15wX3/4IfTpA6lWaCoVSNwLTHzvK6OS72n/+OOPOXr0KP369UuxHWzBggWUKVOGjz/+mEGDBtGhQwdatWpF4cKFr/vnunq8nobQd+3aleL69OnTrFq1ij59+jB9+nR69epF27ZtadWqVYrhcBdzjXMU5cqV4+effyYuLi5Fe1xcHP/73/889s6zWtmyZQHYuXNnmnt79uwhISEhTVzlypXjoYceYunSpRw+fJgmTZowadKkFMPzuXLlomvXrkydOpWdO3fyxhtvsHv3bt5KPiKbBTSx+7rHHoMnnnBfL10KgwfLATJKKb9Sp04datWqRXR0NG+88QbGmDTD8CEhIRhjUvRG4+LiePHFF6/753bq1AljDJMnT06xdWvr1q2sW7cuzc+HtL3hI0eOpNnuBpK8gKvuhd51113ExMSkea1Zs2YRExND586dr+p1MpNru9rKlSv56aefktqttbyQ2LlyxXXmzJk029UiIiKoUqUK4J6mOH78eJqfU6dOHeDq36vrpdvd/MGECbJ47rXX5HruXMiVC6ZMyfiKHqWUVw0cOJCHHnqI1atX06xZszQ9wW7duvH444/Tvn17unTpwtmzZ1m8eHGGtkdVrlyZ4cOHM3XqVFq0aEHXrl05duwYU6dOpWbNmmzbti3p2dy5c9OmTRsWLlxIZGQk9evX58CBA8yYMYOyZcummde++eabARgzZgy9e/dO2iNfvXp1j7GMHj2ad955h+HDh7N161Zq167Ntm3beOutt6hUqRKjR4++7t/zSjZv3syECRPStIeGhvLYY48xZcoUmjZtSuPGjZO2u61atYo1a9bQq1cvWrZsCciiuSFDhtC1a1cqVapErly52LJlC7Nnz6Zhw4ZUSjzgq0qVKtx88800bNiQYsWKceTIEWbOnElYWBg9e/bMkt8xiael8v72FVDb3dITH2/tgAEpRwYff9zpqJS6bsG23c3l5MmTNiIiwgJ2/vz5ae7HxcXZ559/3pYvX96GhYXZUqVK2VGjRtldu3al2dp2tdvdrJXtahMmTLClSpWyYWFhtlq1anbhwoUet6vFxMTYgQMH2qJFi9rw8HBbvXp1O3PmTI/b16y1duLEibZs2bI2NDQ0RTzpPX/s2DH7wAMP2OLFi9vQ0FBbvHhxO2zYsKQtfy7pfb+11jZt2tSWLl3awzuckus9Su8rPDw86dkffvjB3nnnnTZ//vw2LCzMVq5c2U6cODHFFsHffvvN3n///bZy5co2d+7cNkeOHLZy5cr2qaeesqdPn0567oUXXrCNGze2UVFRNiwszJYoUcJ269bNbtmy5R9jtjZj292MdWjxQWaqV6+e3bx5s9NhZL34eOjdG6Kj3W3PPZdyqF4pP7F79+6k4UulVEpX89+HMWaLtbZe6nadY/cnISGwYIEc8ery5JPuIXqllFJBTxO7v8meXRbQJc73APDww5C4H1YppVRw08TujyIi4P33oVEjd9vgwSmH6JVSSgUlTez+Klcu2ddeu7ZcJyTAvffCypXOxqWUUspRmtj9Wb58sGaNnOkOEBcHd98N69c7G5dSSinHaGL3d1FRsG4duPbCxsZCp07w1VfOxqWUUsoRmtgDQbFi0ksvUUKu//5bznXfutXZuJT6B4Gw3VapzJbR/y40sQeKMmWk537DDXJ95gy0bQup6kAr5StCQkLSlOZUSsHly5fTPfjmamhiDySVKsHatZA/v1wfPw6tWsGvvzobl1Ie5M6dm7NnzzodhlI+5+zZs+TOnfu6v18Te6CpUQNWr5ZV8wBHjsie9z/+cDYupVIpUKAAp06d4vjx41y6dEmH5VVQs9Zy6dIljh8/zqlTpyhQoMB1v5aWlA1UmzZBu3Zw8aJcV6wIn30GGTj6UanMFhsby8mTJzl37lyKU8eUCkYhISHkzp2bAgUKEB4e/o/Pp1dSVhN7IPv4Y7jzTnDNY9aoARs2QAY+CSqllPINWis+GLVvD0uWQLbEv+YdO6Tt3Dln41JKKZVlNLEHuq5d5fx2l+++k0Nk/v7buZiUUkplGU3swaBvX5g2zX29aRN06waXLjkXk1JKqSyhiT1YPPAATJrkvv74Y+jVS8rQKqWUChia2IPJqFHw9NPu6/fegwED5AAZpfbH8ocAACAASURBVJRSAUETe7AZPx5GjHBfL1gADz4IAbA7QimllCb24GMMvPyynN/u8uabMGaMJnellAoAmtiDkTGSzHv1crf9978wYYJzMSmllMoUmtiDVUgIzJsnBWxcnn4aXnnFsZCUUkplnCb2YJY9O0RHQ+vW7raRI2HWLOdiUkoplSGa2INdeDgsXw633eZuu/9+WLzYuZiUUkpdN03sCnLmhFWroG5dubZWitqsWOFsXEoppa6ZVxO7MWaOMeaYMeandO7nNcasNMZsN8bsNMbc5834glrevLBmDVSvLtfx8dC9u5zvrpRSym94u8c+D2h3hfvDgV3W2ppAM+BlY0yYF+JSAAULSiKvUEGuL12SxXWff+5sXEoppa6aVxO7tfYz4OSVHgFyG2MMkCvxWa156k1FisC6dVCypFxfuAAdO4Iei6uUUn7B1+bYpwJVgMPAj8DD1lqP9U6NMUOMMZuNMZtjYmK8GWPgK10a1q+HwoXl+tw5aNsWfvI4g6KUUsqH+Fpibwv8ABQDagFTjTF5PD1orZ1pra1nra0XFRXlzRiDw403Ss+9QAG5PnlStsXt3etsXEoppa7I1xL7fcAyK34B9gGVHY4peFWvLgvqcueW66NHoWVL+P13Z+NSSimVLl9L7L8DLQGMMYWBSsBvjkYU7OrVgw8/hMhIuT54UJL7kSPOxqWUUsojb293WwJ8DVQyxvxhjBlojBlqjBma+MizQCNjzI/AemCMtfa4N2NUHjRuDO+/D2GJGxR++UWG5U+ccDYupZRSaRgbACd61atXz27WVdtZ7/33oVs32eMO0ptfvx7yeFwGoZRSKgsZY7ZYa+ulbve1oXjly+66C+bPl9PhQLbAdewIf/3lbFxKKaWSaGJPxVrYscPpKHxYr14wfbr7+osvoHNniI11LiallFJJNLGn8vLLULu2HFeu0jFkCEye7L5euxZ69IDLl52LSSmlFKCJPYXly2HUKEhIgGHD4MknpQevPBgxAp55xn29YgX07++ef1dKKeUITezJ3HYbNGjgvn7+eclV2hFNx1NPwb//7b5evBgeeEA/DSmllIM0sScTFQWffirrwVzmz4fbb5eqqioVY2DSJBg61N02axY8+qgmd6WUcogm9lRy5pRdXYMGuds++QSaNZPCayoVY+CNN6BPH3fbK6/A+PGOhaSUUsFME7sHoaEwc2bK3LR1K9xyC/z8s2Nh+a5s2WDOHOjSxd32n//ASy85F5NSSgUpTezpMAbGjZOR5ZAQadu/H269Fb75xtHQfFNoKCxZAu3audtGjdLtBUop5WWa2P/BoEGy4DtHDrk+cQJatIAPPnA2Lp8UFgbvvQdNmrjbhg2DBQuci0kppYKMJvar0LEjbNgAhQrJ9YULUpNlxgxn4/JJOXLAqlUptxf07y8JXymlVJbTxH6VGjSAr7+GcuXkOiFBFoM/9ZQuAE8jd274+GOoUUOuExLgnnukTSmlVJbSxH4NKlSAr76Ss09cJkyAgQN1r3saBQrIdoKKFeX68mVZXLdpk7NxKaVUgNPEfo0KF5Zh+fbt3W1z50KnTnD+vHNx+aTChWHdOihdWq4vXpSiAN9952xcSikVwDSxX4dcuWRB3YAB7rbVq2Wv+59/OhaWbypZUo52LVpUrs+fl5XzetKOUkplCU3s1yl7dpg9W+bYXbZsgUaNYO9e5+LySeXLS8+9YEG5PnUKWrfWogBKKZUFNLFngDFSh2XGDKnRAvDbb5LcdbQ5lapVZc49Tx65PnYMWrWS4gBKKaUyjSb2TDBkiJwMFxkp18ePQ/PmsutLJVOnjqyMdxUF+OMPaNkSDh92Ni6llAogmtgzSadOcoCMa7T577/hzjulcp1KplEjqe4THi7Xv/0mw/IxMc7GpZRSAUITeya6+WbZDle2rFwnJEhvfvx43eueQsuW8M47UoYWYNcuaNsWTp92Ni6llAoAmtgzWcWKktzr1HG3PfMMDB4McXHOxeVz7rhDSs0aI9fbtkmJv7/+cjYupZTyc5rYs0CRIrBxo3RCXd56S4bmNW8l07OnbC1w+eoreZMuXnQuJqWU8nOa2LNI7tywciX06+du++gjWVR37JhzcfmcAQNgyhT39fr10L27lvJTSqnrpIk9C2XPLlXpnnzS3fb997J+7JdfnIvL5/zrX/Dcc+7rlSuhTx+Ij3cuJqWU8lOa2LOYMVJP/s033Xvdf/1Vkvv33zsbm0954gl47DH3dXS0rDxMSHAuJqWU8kOa2L1k6FBYtgwiIuQ6JkZK0H70kaNh+Zbnn4cHH3Rfz5kDI0bolgKllLoGmti96M47ZQq5QAG5/vtv2f8+Z46zcfkMY2S+vX9/d9trr6Ws26uUUuqKNLF7WaNGsvi7TBm5jo+XY1//8x/tmAIyXzF7Ntx9t7vtuefgxRedi0kppfyIJnYHVKokyb1WLXfbuHFw//261x2AkBBYuFD2tbs8/jhMnepcTEop5Sc0sTukaFHYtEmqqbrMmgWdO+tedwDCwqQ6XfPm7raHHpL9g7pfUCml0qWJ3UF58shBMX36uNtWrZKKq1o6HTlV54MPpFavy/z5ULmyHKmnK+aVUioNTewOCwuDt9+WkWaXb7+FW2+V81GCXq5cciJct27utlOnZJtBo0ZSilYppVQSTew+wBjZ6TV1qrt0+t69cMstsGWLs7H5hHz5ZFj+o4+gXDl3+7ffQr168MgjcPasc/EppZQP0cTuQ4YPh/fec59oeuwYNG0Kq1c7G5fPaN8efvoJxo6Vsn4gw/FTpkCVKrB0qW4tUEoFPU3sPqZzZ1i3DvLnl+u//pKD0ObNczQs3xEZCc8+Cz/+CC1auNsPH4YePST5a71epVQQ08Tug267Db78EkqVkuu4OLjvPtnOrR3SRJUqySegRYugcGF3+5o1UL26FAaIjXUuPqWUcogmdh9VpQp8/TXUrOluGzsWhg3Ts1GSGAO9esGePTKP4VqgEBsrhQFuukmSv1JKBRFN7D6sWDHZ696ypbtt+nTo2lXK0apE+fLJysPvvoO6dd3te/dKoYB77oEjR5yLTymlvEgTu4/Lm1cWg/fu7W5bsUKS/fHjzsXlk+rVk5XyU6dKkQCX//s/2fv++us63KGUCnia2P1AWJjUZRk92t32zTey133fPufi8kkhITIs//PPMkzvcvasnPveoIGel6uUCmia2P1EtmwwcaLs7HJNJf/vf7LXfetWZ2PzSUWKyMK6deugYkV3+9at0LChLFY4fdq5+JRSKotoYvcz//qX1Gpx7XX/80/Z6/7JJ87G5bNatoQdO2SVvOtNsxbefFNW1i9cqFsNlFIBRRO7H+raFdaulTVjAOfPy0FoCxY4G5fPCg+XM9137oR27dztx45Jof6WLWVlvVJKBQBN7H6qcWP44gsoWVKu4+Kgb1944QXtgKarfHlZifjOO1C8uLt9wwaoUUP2E1644Fx8SimVCTSx+7Fq1WSv+003udueeAIefFAXf6fLGDlQZvduGDFCFtsBXL4sFYCqVZPkr5RSfkoTu58rXhw+/zzlseXTpsHdd2vn84py54bJk+WUneTHwu7bJ/MaXbvCwYPOxaeUUtdJE3sAyJtXTjbt2dPdtnw5tGoFJ044F5dfqFlT6vfOnOku0A+wbJmU/3v5ZenNK6WUn9DEHiDCw2V317//7W776iupO3/ggHNx+YVs2WDwYNn73r+/u/2vv+QNrVtX3kyllPIDmtgDSLZs8N//wiuvuPe679kje91/+MHZ2PxCVBTMnQuffSZz7S4//ijVgAYN0iEQpZTP08QegB55RKqohoXJ9ZEj0KSJnody1Ro3hm3bpCJQjhzu9rfekr3vc+bIOfBKKeWDNLEHqO7dpWhN3rxyfe6cHFW+aJGzcfmN7Nmlhu+uXdCpk7v9xAkYOFA+Kf34o3PxKaVUOjSxB7CmTWWvu2vLdlwc3HsvTJqke92vWunScurOihVQqpS7/csvoXZtSf7nzzsXn1JKpaKJPcBVry4HxlSv7m4bMwYeflj3ul+TTp2k9z5mDISGSlt8vCxqqFoV3n9fPy0ppXyCJvYgUKKE7HVv2tTd9vrr0KMHXLzoXFx+J2dOePFFWYnYpIm7/eBB6NxZkv/+/Y6Fp5RSoIk9aOTLB2vWyNy7y3vvQZs2cPKkc3H5pWrVYONGmDcPChVyt69aJb33F16AS5ecik4pFeS8mtiNMXOMMceMMT9d4ZlmxpgfjDE7jTGbvBlfoAsPhyVLpJKqy+efy1733393Li6/ZAz06yd734cMcbdfuCB1fWvVkuSvlFJe5u0e+zygXXo3jTH5gGlAJ2ttNeBuL8UVNLJlk0qqL7/sbtu9W/a679jhXFx+q0ABmDFDivbXrOlu371b6vz27SunyCmllJd4NbFbaz8DrjTw2wtYZq39PfF5/Rcxi4wcKb337Nnl+vBh2b796afOxuW3br4ZNm+W6kC5crnbFyyQve8zZujed6WUV2RKYjfGFMyM1wEqAvmNMRuNMVuMMX0z6XWVBz17yrx7njxyffasHFe+ZImzcfmt0FCpDrRnj5zC43L6NAwdKsMi27Y5F59SKihcU2I3xgw2xoxKdn2TMeYP4JgxZrMxpkgG4wkF6gIdgbbAU8aYiunEMiTxZ26OiYnJ4I8NXs2by173YsXk+vJl6NVLhup199Z1Kl4cli6Vk3nKl3e3f/cd1Ksnew3PnnUuPqVUQLvWHvtDQPLDQCcDp4FHgLzAfzIYzx/AGmvtX9ba48BnQE1PD1prZ1pr61lr60VFRWXwxwa3m26SKeKqVd1t//63LLLT0eMMaNdOqtM9/bS7vm9CArz2GlSuLMlfPz0ppTLZtSb20sAeAGNMXqApMNpa+zowDullZ8QK4DZjTKgxJgfQENidwddUV6FUKem5N27sbpsyRYbrda97BkRGwjPPSIJv2dLdfuSIFBJo1w5++cW5+JRSAedaE3s2wNWHuw2wwMbE64PADVf6ZmPMEuBroJIx5g9jzEBjzFBjzFAAa+1uYDWwA/gOmG2tTXdrnMpc+fNLfflu3dxt77wDbdvCqVPOxRUQKlaEtWth8WIokmzG6pNPpCzgM8/oJyilVKYw9hqGAo0xW4EN1tpHjTELgBLW2uaJ93oAk621xbMm1PTVq1fPbt682ds/NmDFx8uq+ddec7dVqyZTxiVLOhdXwDhzBsaOhWnTUs513HgjvPEGtG7tXGxKKb9hjNlira2Xuv1ae+wvAY8YY44jW9NeT3avOdLTVn4uJARefVXKoLvs3CmLuvVAs0yQN6/U9HUtpnPZu1dKAfbsKfsPlVLqOlxTYrfWLkbm1V8AmltrlyW7/ScpE73yY8bIArpFi9x73Q8dkjl4LaiWSerWlRN63njDfb4uQHS0LK577TU9qUcpdc2uaSjeV+lQfNZav17OODl3Tq7DwmD+fFn7pTLJ0aPuT1LJ1akD06dD/frOxKWU8lmZMhRvjGlkjLk92XVBY8wSY8yPxpiXjDEhmRGs8i0tW0pN+aJF5frSJRktfuUVZ+MKKEWKwMKF8imqUiV3+9at0LAhDBumKxiVUlflWufYX0QKyLj8F+gA/A94AHgik+JSPqZmTdnrXrmyu23kSHj0Ud3rnqlatIDt22HCBIiIkDZr4c035c1fuFD3viulruhaE3sVYDOAMSY70A0YYa3tCjyJLKhTAap0afjyS7j1Vnfb5MlSqS421rm4Ak54ODz5pKxYbN/e3X7sGPTpI0Moe/Y4F59Syqdda2LPBbhqYTYAcgKrEq+3AqUyKS7lowoUkO3YXbq426Kjpc7K6dPOxRWQypWDDz+Ed9+VMrUuGzZAjRqS/P/+27n4lFI+6VoT+yHcJV7bAz8lO4EtP6D/ygSByEiphjp8uLtt40ZZMf/HH46FFZiMga5d5RjYkSNlLyJIUf/nn5cCAx9+6GyMSimfcq2JfQnwvDHmXWAksDDZvTrA3swKTPm2kBDZiv3ii+62n36Sve47dzoXV8DKnVtO5tm6Vd5kl/374fbbZQjl4EHHwlNK+Y5rTezjgYlAOLKQLvm66JrAO5kTlvIHxsCYMbL1LTRU2v74A267DT77zNnYAlaNGlLUf9YsmRdxWb4cqlSBl16S3rxSKmjpPnaVKVzz7ufPy3VYmCzgTn4sucpkMTHyyWru3JTtN90kq+iTr3JUSgWczCop63qx6saY4caYpxL/rJbxEJU/a91aeumu800uXZICNsnrzatMFhUFc+ZIkYFqyf4T/PFHGTYZNAhOnHAuPqWUI661QE2oMWYhsB0pH/tM4p87jDELtEBNcKtdW/a6u+qrWAsPPwyjR+te9yx1222wbRtMmgQ5crjb33pL/jLmzNG/AKWCyLX22McB3YGngbJAZOKfTwM9Ev9UQaxMGdnrnnx913//C/feq3vds1T27DBqlKyev+sud/uJEzBwIDRpoif4KBUkrjWx3wtMsNY+Z609YK2NTfzzOWAC0DfzQ1T+pmBBqYyaPL8sWQIdOsiJpSoLlSolC+lWrpSKQi5ffilDKqNGuRdCKKUC0rUm9mLAV+nc+yrxvlJERkpdlQcecLd9+ql0HPVEUi+4/XbYtQsef9y9ZSE+XlbNV60qyT8AFs4qpdK61sR+GEhvqW2jxPtKAbLX/Y034Lnn3G07dsgw/a5dzsUVNHLkkCI227dD06bu9oMHZQvDHXfAvn3OxaeUyhLXmtgXAU8mroYvZ4yJNMaUNcY8jtSKX5D5ISp/Zgw88QTMm+fuOP7+u+zE+uILR0MLHlWrShnat9+WlfQuH34oq+mff162MSilAsI17WM3xoQC84GeQPJvNMBioL+1Ni5TI7wKuo/dP6xZI9VR//pLrsPDYfHilHXnVRY7dUqG52fOTDkUX7my7H1v1syx0JRS1yZT9rFba+Ostb2Am4AHkVXwDyZez0MOglHKo7ZtYdMmKFxYrmNjoVs3mDrV2biCSv78MH267EusVcvdvmcPNG8OffvKKXJKKb91XQVqrLU7rbVvJq6Of9NauxPIC2ihGnVFdevCV1/BjTfKtbXw0EPw2GO61dqrGjaE77+HV1+VOvQuCxbI3vfp0/UvRCk/dV2JXamMKFdOknvDhu62iROhXz+d6vWq0FCpILR7N3Tv7m4/fVq2M9xyixS+UUr5FU3syhGFCsn2t06d3G0LF0LHjnD2rHNxBaXixSE6WhZBlC/vbv/uO6hXT5K//qUo5Tc0sSvH5MgB770HQ4a429atk51ZR444F1fQatNGzt4dN05O8QEZjn/tNVlcFx2te9+V8gP/mNgTt7X94xdQxAvxqgATGirTuc8+62774QcZBd6zx7m4glZEBIwfL+VnW7Vytx85Aj17Qrt2sHevY+Eppf7ZP253M8YkkHJrW7qPAtZa6/WDYHS7W2CYOxcGD5YCaSDHja9cCY0aORtX0LIWli6FESNSDqGEh8tqx8cekw8CSilHpLfd7WoSe79r+UHW2revMbYM08QeOD7+WM5wd+11j4iQOvPJ684rLztzBp5+WvYlJl8pX6GClBZs08a52JQKYted2P2BJvbAsnmzLKJzbafOlg1efx2GDXM2rqC3dSsMHSrb5JJr3Vp69W3byl+WUsorMqVAjVLeUK+ebIerUEGuExJg+HB49FFdVOeoOnWksM20aZA3r7t97Vo5uq96dalod+GCczEqpTSxK99Uvrwk9wYN3G2TJ8vOrEaN5JCyX391Lr6gFRIie9x//lmq1CXvoe/eDfffL0fHPvUUHD3qXJxKBTEdilc+7a+/oEcPOa/Ek5tugs6dpd58jRpy6Izyon37ZDvcW2/BuXMp74WFwT33yDB9zZrOxKdUANM5duW34uJgzhxZRPfZZ+lXOi1b1p3kb7lFp3u96swZSe6vvQYHDqS936KFJPgOHfQvRqlMooldBYSYGNkCt3y5TO3Gxnp+rnBhuPNOSfLNm7vrragsFhcH778v8yZff532fsWK8MgjUj84Rw7vx6dUANHErgLOuXOwejUsWyZD9alHgl3y5pVV9l26SH2VnDm9G2fQ+uYbeOUVKS/oKk7gUqCAzMc/+CAUK+ZMfEr5OU3sKqDFxkrt+WXLYMUK6dl7EhEh2667dIE77pD8orLYgQOyX3HWrLQ157Nnl0UUI0bIqnul1FXTxK6CRny8rKhfvlwSvacpX5AF3k2byrz8XXdBiRLejTPonDsniyWmTJFFd6k1bSoJ/vbb5S9HKXVFmthVULJWas+7kvzOnek/26CBe/FdxYreizHoxMfDBx/IPPwXX6S9X6GCnCjXvz/kyuX18JTyF5rYlULOL3El+W+/Tf+5qlUlyXfuLCPEuo0ui3z/vczDL12adh4+Xz45+u+hh3Q4RSkPNLErlcqhQzIfv3w5bNiQNq+4lCrlTvK33aajxFni4EGpRT9zJpw+nfJeaKgcIDBiBNSv70x8SvkgTexKXcHJk7BqlST5NWvSr4oaFQWdOkmSb9VKDjpTmej8eXj7bXj1Vfjll7T3b7sNRo6UvwT9hKWCnCZ2pa7SX39Jcl++XPbMnznj+blcuWQbXefOUncld27vxhnQ4uNlD+PkybBpU9r75crBv/4FAwboG6+CliZ2pa7DpUuwcaMk+fffT7/8eViYHHLWubN0JqOivBpmYNu6Vebh/+//pABOcnnywODBMg9furQz8SnlEE3sSmVQQoLUXFm+XL7SO4QmWzZo3Ng9L1+qlHfjDFiHDsn57zNmyNxJciEh0LWrzMPffLMz8SnlZZrYlcpE1sKPP7qT/Pbt6T9bt647yVepoivsM+zvv2H+fOnF/+9/ae/fcosk+M6dZeGdUgFKE7tSWei339xJ/quvJPF7UrGi7JPv3FkWeGuSz4CEBPjoI0nwn36a9n7p0jIPP3BgyvPjlQoQmtiV8pKjR93b6D79FC5f9vxc8eLunnyTJtq5zJDt2yXBL16c9g3PnVuS+7/+JUcAKhUgNLEr5YDTp6VTuWwZfPyxjCJ7UqCA1K7v0kUW4UVGejfOgHH0KEybJl8nTqS8ly2bfIoaOVKG63W4RPk5TexKOezCBTlqdtky2UaXev2XS86ccgpdly6ynU5Hka/DhQuwcKH04nfvTnu/QQOZh+/aVQ6iUcoPaWJXyofExcFnn0mSf/99WfDtSfbs0KKFJPk775Rz5tU1SEiATz6R/fBr16a9X7KkbJUbPFhK2CrlRzSxK+WjEhJg82Z3DXtPC71BRo4bNXLPy5cr5904/d6PP0pFu0WL5Jzf5HLmlGI3Dz8M5cs7E59S10gTu1J+wFoZOXYl+a1b03+2Zk33aXTVq+uU8VU7dgzefFP2xMfEpLxnjAyNjBwp5Wv1TVU+TBO7Un7owAEZql++HD7/XHr3npQv707yDRvKOjH1Dy5elN77K694Ps+3bl2Zh+/eXefhlU/SxK6Un4uJkWPMly+X6eJLlzw/V7SodDq7dIFmzTQn/SNrYd06mYdfvTrt/eLF4cEH5QjZAgW8H59S6dDErlQAOXtWts8tXy5npZw/7/m5fPng9tulN9+2rUwlqyvYtQumTJHKdhcvpryXIwf07y/z8BUrOhKeUslpYlcqQF28COvXS5JfsQKOH/f8XGSkJPfOnSXZa+fzCmJipCb91Knw558p7xkjb+CIETIkovPwyiGa2JUKAnFx8OWX7vK2v//u+bmQEGjeXJL8XXdBsWLejdNvxMbKqXKTJ8OOHWnv16olCb5nTzniTykv8onEboyZA9wOHLPWVr/Cc/WBr4Ge1tp3/+l1NbErlZa1sqreleR37Ur/2Ztvdm+ju/FG78XoN6yFDRskwX/4Ydr7RYvC8OFw//1QqJD341NByVcSexPgPDA/vcRujAkB1gIXgTma2JXKHD//7E7y332X/nPVq7uTfK1aOtKcxs8/y374t9+WCnfJRURAv37wyCNQubIz8amg4ROJPTGQMsCqKyT2R4DLQP3E5zSxK5XJ/vjDvY1u0yaIj/f8XJky7iTfqJEM4atEJ0645+GPHEl7v0MHGaZv2VI/Haks4ReJ3RhTHFgMNAfmcIXEbowZAgwBKFWqVN0DBw5kVchKBbQTJ2DVKimI88knaReDu9xwg2yj69xZytyGh3s3Tp916RIsXSrD9Nu2pb1/002S4Hv10jdNZSp/SezvAC9ba78xxsxDe+xKedX587BmjST5VatkW50nefJIh9R1UE2OHN6N0ydZKwcATJ4sp/yk/re1cGEYNgweeACiopyJUQUUf0ns+wDXmFUh4G9giLX2/Su9piZ2pTLfpUuyXmzZMtlGl3rXl0vu3NCtG/TpA02batU7APbuhddegzlz0p7VGx4ub9Yjj0C1as7EpwKCXyT2VM/NQ3vsSvmE+Hj45htJ8suXw759np8rVQruvVfylq4dA06dgpkz4fXXPR/h17atDNO3aaPz8Oqa+URiN8YsAZohvfE/gXFAdgBr7fRUz85DE7tSPsda2dK9bJls8U7vNLr69SXB9+ypI89cvgzvvivD9J7+rapWTXrwvXtLJSGlroJPJPasooldKWdYC99/LxVYlyyBkyfTPhMaKvPxffpIwbaICO/H6TOslQpCkyfLtoTU//4WKiTz8MOGyZy8UlegiV0plaUuXZL69fPny9qxy5fTPpMvH/ToIUm+UaMgH33+9Vf3PHzqYv9hYdJ7HzFCVtUr5YEmdqWU15w8KTvA5s+Hr7/2/Ez58u75+PLlvRufTzl9Gt56S5K8pxrArVpJgm/XTlcmqhQ0sSulHLF3LyxcCAsWpL/o7tZbJcF37w7583s3Pp8RFycLFyZPhm+/TXu/cmWZh+/TR/cXKkATu1LKYa7p5fnzpTd/5kzaZ8LCoFMnyV3t2gXxuSpffy0JftkySEhIea9gQRg6VGrTFy3qTHzKJ2hiV0r5jIsXZR5+/nyZPOn7cwAAGERJREFUl/dU0rZQIVlR37cv1KsXpPPx+/fLEP3s2XDuXMp72bPDPffIMH2tWo6Ep5yliV0p5ZOOHZNtc/Pnw5Ytnp+pXFl68ffeK3vlg87Zs7LIbsoUSfapNW8uCb5jR52HDyKa2JVSPm/XLpmLX7hQDqrxpHlzSfJdu0pp26ASFydlACdPhq++Snv/xhtlHr5fP8iZ0/vxKa/SxK6U8hvx8XLq3Pz58N57aXeDgdRxuesuGapv1Ur2yweVb7+FV16Rwjep5zJy5ZITe3r0kKp2evhMQNLErpTyS3/9JbVc5s+HdevSriUDKFJEDk/r2xdq1vR+jI76/Xc5OnbmTM8rEvPmlSP5evSQI2SzZ/d+jCpLaGJXSvm9w4dh8WJJ8j/+6PmZm26SBN+rFxQr5t34HHXuHMybB2+8AT//7PmZggXlSL7u3aFZsyAc5ggsmtiVUgFl+3ZJ8IsXw9Gjae9nyyZD9H37ypB90Ew5WytvTnS0fKVXPOCGG2ShQo8e0LixLrrzQ5rYlVIBKS5Ohujnz5ch+wsX0j6TK5fksL59paMaNDnMWjl0JjpaigccPOj5uWLF4O67JcnffHOQ7i30P5rYlVIB7+xZWWw3fz5s3Oj5mZIlpQx7375QpYpXw3NWQoKcvRsdDe+8A0eOeH6uVCkZqu/ePYgLCPgHTexKqaBy4AAsWiRJPr0p53r1ZOvcPfcE2dGy8fHwxReS5N99F2JiPD9Xrpwk+B49ZFWiJnmfooldKRWUXKPRrqNlT5xI+0xoKLRvL0n+jjuC7GjZuDgZ3oiOlhK2ns7eBahUyZ3kq1XzaojKM03sSqmgd+kSrF7tPlr20qW0z+TNK/mrb185nCaoOqmXL8uChehoWL5c5jY8qVZNEnyPHlCxondjVEk0sSulVDKnTrmPlvVUxA1kJNp1tGyFCt6Nz3GxsbBmjST5Dz7wXCUIpE69K8mXLevdGIOcJnallErHL79IGdv589PfHdaokfto2QIFvBuf4y5cgI8+kiS/apXnrQcA9etLgu/eXVYpqiyliV0ppf6BtdJ7nz9fclh6R8vefrsM1bdvH4RHy54/L8k9OlqO5ouN9fxco0aS5O++W4+XzSKa2JVS6hpcvCj5y3W0bFxc2mcKFnQfLVu/fpDNx4PMwa9YIUn+k09kjj41Y6BJE0nyXbtKYRyVKTSxK6XUdYqJcR8tm94/NZUquY+WLV3au/H5hFOnZMFddDSsX5/2YBqQykAtWkiS79IlCOc0MpcmdqWUygS7d7uPlk2vkFvTptKL79YtCI+WBfkktGyZJPmNG2WOI7XQUGjdWpL8XXfJdgR1TTSxK6VUJkpIcB8t++67nheNR0S4j5Zt3TpIz1w5elTeoOhoKYrjSVgYtGsnSf6OOyB3bu/G6Kc0sSulVBb5+2/30bJr13o+WrZw4ZRHywbdfDzAH39IOdvoaDlP3pOICOjYUZJ8x46QI4d3Y/QjmtiVUsoLjhxxHy27Y4fnZ6pXlwTfu3eQHS2b3P79UkggOhq2bvX8TM6c0oPv0UN69EFVEvCfaWJXSikv275d5uMXLUr/aNmWLSXJd+4cREfLprZ3rzvJ//ij52fy5IE775Qk37p1EO4zTEsTu1JKOSQuThaKz58vC8c91XfJmTPl0bIhIV4P0zfs3u0+S37PHs/P5M8vn4R69JBV9kG5eEETu1JK+YRz5+Ro2QULYMMGzwvGixd3l7IN2vNWrJXeuyvJ//qr5+cKFZJPRD16yH75IPpEpIldKaV8zO+/u4+WTa9zWqeO9OLvuSeIa7tYK/Pw0dEyZH/ggOfnihSRPYY9ekjlu2zZvBunl2liV0opH2UtbNniPlr2+PG0z4SEyPqxvn1lPVlkpPfj9AnWyor66GhZYX/okOfnSpSQcrY9ekCDBgG5DUETu1JK+YHLl+Vo2QULpFqrp6Nl8+RJebRsgHdM05eQAF9+KUn+3Xfhzz89P1emjPss+dq1AybJa2JXSik/c+qUdErnz5f85UnZsu75+Btv9G58PiU+XioGRUfLIoYTJzw/V6GC+5jZ6tX9OslrYldKKT/266/uo2V/+83zMzffLL34Hj2CvAz75cvw6aeS5Jcvh9OnPT9XpYo7yVeu7N0YM4EmdqWUCgDWwtdfu4+W9ZSzcuWCMWNg5Egt3MalS3LyXHS0zG2cO+f5uRo13Em+fHnvxnidNLErpVSAuXgRPvxQkvxHH6U9WrZECXjuORmqD9p5+OQuXpQzeKOjYeVKqQXsSd26kuC7d/fpo/o0sSulVACLiZF8NW2a1HhJrk4dePllKXyjEv31l3wqWrpU/rx40fNzDRtCz56ywr54ce/G+A80sSulVBCIi4M5c+Cpp+DYsZT3OnWCSZPk7HiVzLlz0oOPjpYtCZ62IhgDt90mPflu3eRUH4dpYldKqSBy7hxMnCg99eSd0dBQGDoUxo2Tom0qldOn5ai+6GhYty7t/AbIvEazZpLku3Rx7I3UxK6UUkHo4EF48knZF59c3rzS/tBDemhauk6ckFX10dGyyt7TebwhIXKST48eUr8+f36vhaeJXSmlgtiWLfDoo7LVO7kyZeCFFyQv+fGW7qx37Jjsj4+Ohv9v786jpCrPPI5/H7BZFARFFA4YlwAuRHDBPhiMRHNUoiiOBsVtZI6O26gxo0FHEyYQT8aIIaOjjlGDuGDAwWUQ5WiMEowL0CIgi3Q3yBAVBUHEiLI+88d7K9UWVXRBV9etuvX7nNOHqntfqp+3L9yn73uf+74zZmSf5L+qCk45JfwwhwwJMwk1IyV2EZEK5w5TpsCIEVBb+819/fuHYfvvfjee2MrKRx+Fme4mTgzPHmbTujX88IchyZ9xRrOsyavELiIiQJi/5f77YdSo7SdoGzo0XMGXyaPc8VuxIkwPOGkSzJ6dvU3btjB4cDrJF2gteSV2ERH5hnXrwnPud9/9zULwqqpw7/1nPyvqLePyt2xZeHxu0iSYO3f7/W3ahCH99u0L8u1yJXZNWSAiUqE6doQxY8KSseeem96+eTOMHRumVb/rruxPf0kWBx8MN98M77wTfqijR0Pv3un9p51WsKS+I7piFxERINwuvuGG7W8b9+gRnn8/6ywV2O2ShQvDVfyAAXDqqQX7WA3Fi4hIo9xDXdhNN8H7739z3wknhAK7ftulEomDhuJFRKRRZqGAbvFiuPPO8Lx7yowZcOyxYe75FSvii1F2TIldRES207p1GJZfuhSuuy7MWJcyYUKYlvaWW2D9+vhilOyU2EVEJKdOnUIB3cKF4R57ytdfh8fievYMj85lm3lV4qHELiIijerVK8yuOn16WNU0ZdUquOoq6Ns3LB2bgLKtsqfELiIieRs4EGbNCnPPd++e3r5oEZx+ephRdd68+OITJXYREdlJLVqEArra2jDBTbt26X0vvwxHHQWXXhpmXpXiU2IXEZFd0rZtKKCrr4crrggJH8Jw/Lhx4f77qFHw5ZfxxllplNhFRKRJ9tsvFNDNnx/WPUnZsAF+8YuQ4MeNg61bYwuxoiixi4hIQfTuHQroXnoJjjgivX3lyjA0f8wxYahemldRE7uZjTOzVWa2IMf+C81svpm9a2ZvmFnfYsYnIiJNd/LJYbr0hx6CLl3S2+fNC/sGDw7FdtI8in3FPh4YtIP97wMD3f0I4JfAA8UISkRECqtly3CVXlcHI0eG+/Epzz8PffqEx+RWrYovxqQqamJ39xnA2h3sf8PdP4vevgV0z9VWRERKX7t2oYCurg6GD08vIrN1a7gv36NHmOjmq69iDTNRSvke+6XAtLiDEBGRpuvWDR5+GObMgZNOSm//4otQWX/ooWGq2m3b4osxKUoysZvZiYTEftMO2lxuZjVmVrN69eriBSciIrvsyCNDAd3UqSGZp6xYEZ6N798fXnstvviSoOQSu5n1AR4Chrj7mlzt3P0Bd+/n7v06d+5cvABFRKRJzMIsdfPnw333QcNT+OzZYXnYs88Ow/ey80oqsZvZt4CngYvdvTbueEREpPlUVYUCuro6uPnmsKJcyjPPwOGHw/XXw9qclVmSTbEfd/sD8CZwiJl9YGaXmtmVZnZl1GQk0Am4z8zmmllNMeMTEZHi69AhFNAtWQIXXJDevmVLWFnu29+GsWNh48b4Yiwn5glYiqdfv35eU6PfAUREkmDWrLAW/F/+8s3tBx8Mv/41nHNOurq+kpnZ2+7eL3N7SQ3Fi4iIVFfDjBnw1FPhaj1l2TIYOhS+9z2YOTO++EqdEruIiJQcs1BAt2gR/Pa3sNde6X2vvx6q54cNg+XLYwuxZCmxi4hIyWrVKhTQ1dfDT34SCu5SJk2CQw6BESNg3br4Yiw1SuwiIlLy9t47FNAtWhTusads2gRjxoQZ7O65BzZvji/GUqHELiIiZaNHD5g8OUxiU12d3r5mDVx7bVhVbsqUsCZ8pVJiFxGRsnP88fDmm/DEE3DAAentS5bAkCFh2to5c+KLL05K7CIiUpZatIDzz4f33oPbb4c990zvmz4d+vWDSy6BDz6ILcRYKLGLiEhZa9MGbropFNhdfXVYMhbCcPyjj0KvXvDzn4cFZyqBEruIiCRC585w773w7rtwxhnp7V99BbfdBj17woMPhiVjk0yJXUREEuWww0IB3Z/+FFaTS/nkE7j88rDtxRfji6+5KbGLiEginXQSvP02jB8f1oNPWbAABg0KXwsWxBZes1FiFxGRxGrRIhTQ1dbC6NGwxx7pfS++CH37hqv4jz+OL8ZCU2IXEZHE2333UEBXVweXXRYSPsC2beG+e48e4T78hg3xxlkISuwiIlIxunYNifydd+CUU9Lbv/wyJP5evUIl/bZt8cXYVErsIiJScfr0CUPx06ZB797p7R9+GIbujz02PAtfjpTYRUSkYg0aBHPnwu9+B/vum94+Zw6ceCKceWaYza6cKLGLiEhF2223UEBXXw+33homvEl57rlwRX/NNbB6dXwx7gwldhEREaB9+1BAV1sLF1+c3r51a5j4pkcPuOMO+Prr+GLMhxK7iIhIA/vvHwroampg4MD09vXrw9S1hx0GEyeW7gpySuwiIiJZHHMMvPoqPPtsqJZPWb48LD5z3HHwxhuxhZeTEruIiEgOZmEZ2AUL4O67oVOn9L6ZM2HAABg6FJYujS/GTErsIiIijaiqgmuvDQV2N94IrVql902eHIbnb7gBPvssvhhTlNhFRETy1LEjjBkT1oA/77z09s2bYezYUGB3112waVN8MSqxi4iI7KSDDgoFdG++Ge61p6xdC9dfHx6Re+aZeArslNhFRER2Uf/+8Prr8OSTIdmn1NfD2WeHqvqamuLGpMQuIiLSBGahgG7xYrjzTujQIb3vtdfC9LQXXQQrVhQnHiV2ERGRAmjdOhTQLV0K110XZrRLmTAhPDJXW9v8cSixi4iIFFCnTqGAbuFCOOus9Pbjj4eePZv/+yuxi4iININevUIB3fTpUF0Nv/lNGLZvbrs13kRERER21cCB8NZbxUnqoCt2ERGRZlespA5K7CIiIomixC4iIpIgSuwiIiIJosQuIiKSIErsIiIiCaLELiIikiBK7CIiIgmixC4iIpIgSuwiIiIJosQuIiKSIErsIiIiCWLuHncMTWZmq4H/K+BH7gN8WsDPi5P6UpqS0pek9APUl1KVlL40Rz8OcPfOmRsTkdgLzcxq3L1f3HEUgvpSmpLSl6T0A9SXUpWUvhSzHxqKFxERSRAldhERkQRRYs/ugbgDKCD1pTQlpS9J6QeoL6UqKX0pWj90j11ERCRBdMUuIiKSIBWd2M1skJktMbN6M7s5y/7WZjYp2j/TzA4sfpT5yaMvw81stZnNjb4uiyPOxpjZODNbZWYLcuw3M7s76ud8Mzu62DHmK4++fN/MPm9wTEYWO8Z8mNn+ZvaqmS0ys4Vm9uMsbcriuOTZl3I5Lm3MbJaZzYv6MipLm5I/h+XZj7I4f6WYWUsze8fMpmbZ1/zHxN0r8gtoCSwFDgZaAfOAwzPaXA3cH70eBkyKO+4m9GU4cE/csebRlxOAo4EFOfafBkwDDOgPzIw75ib05fvA1LjjzKMfXYGjo9ftgdos/77K4rjk2ZdyOS4GtIteVwEzgf4ZbUr+HJZnP8ri/NUg3n8Fnsj276gYx6SSr9irgXp3X+bum4CJwJCMNkOAR6LXk4EfmJkVMcZ85dOXsuDuM4C1O2gyBHjUg7eAjmbWtTjR7Zw8+lIW3H2lu8+JXn8BLAa6ZTQri+OSZ1/KQvSz/lv0tir6yiyaKvlzWJ79KBtm1h04HXgoR5NmPyaVnNi7AX9t8P4Dtv8P/vc27r4F+BzoVJTodk4+fQE4JxomnWxm+xcntILLt6/l4rhoCHKamfWOO5jGRMOGRxGuqhoqu+Oyg75AmRyXaMh3LrAK+KO75zwupXwOy6MfUD7nr/8ERgDbcuxv9mNSyYm90jwHHOjufYA/kv6NUeIzhzAlZF/gv4BnY45nh8ysHfAUcL27r487nqZopC9lc1zcfau7Hwl0B6rN7Dtxx7Qr8uhHWZy/zGwwsMrd344zjkpO7B8CDX/r6x5ty9rGzHYDOgBrihLdzmm0L+6+xt03Rm8fAo4pUmyFls9xKwvuvj41BOnuLwBVZrZPzGFlZWZVhEQ4wd2fztKkbI5LY30pp+OS4u7rgFeBQRm7yuUcBuTuRxmdvwYAZ5rZcsIt0ZPM7PGMNs1+TCo5sc8GeprZQWbWilDEMCWjzRTgkuj1j4BXPKp4KDGN9iXjfueZhHuL5WgK8I9RFXZ/4HN3Xxl3ULvCzLqk7q2ZWTXh/2PJnXSjGH8PLHb3sTmalcVxyacvZXRcOptZx+h1W+Bk4L2MZiV/DsunH+Vy/nL3f3P37u5+IOE8/Iq7X5TRrNmPyW6F/LBy4u5bzOwa4EVCVfk4d19oZqOBGnefQjgBPGZm9YQiqGHxRZxbnn25zszOBLYQ+jI8toB3wMz+QKhK3sfMPgD+nVBMg7vfD7xAqMCuBzYA/xRPpI3Loy8/Aq4ysy3AV8CwUjvpRgYAFwPvRvdBAW4BvgVld1zy6Uu5HJeuwCNm1pLwy8eT7j61DM9h+fSjLM5fuRT7mGjmORERkQSp5KF4ERGRxFFiFxERSRAldhERkQRRYhcREUkQJXYREZEEUWIXSYBo9SvP8bUu5tjGR4/7iUgRVOxz7CIJNZQwT3tDW+IIRETiocQukixz3b0+7iBEJD4aihepIA2G7E8ws2fN7G9mtsbM7o2m82zYtquZPWpmn5rZxmhlrczpMYmmMn7MzD6O2i0zs7uytDvKzF4zsw1mVmdmV2bs72Jmj5jZR9HnrDSzqWa2b+F/EiLJpSt2kWRpGS0s0dA2d89cQvJx4EngPqAaGAnsQTRVp5ntAfwZ2Isw5epfgYsIU2Hu7u4PRO0OAmYRppEdCdQRpmc9JeP77Qk8QVjScjRhytn/NrMl7v5q1OYx4ADgp9H32w/4AbD7rvwgRCqVErtIsmQuAgLwPDA4Y9sL7n5j9PolM3NgtJn9yt1rCYm3J3Ciu0+P2k0zs/2A28zs9+6+FRgFtAX6uvtHDT4/c1nN9sDVqSRuZjOAU4HzCat5ARwH3OLuExr8vf/Jq9ci8ndK7CLJ8g9sXzyXrSr+yYz3E4HbCFfvtcAJwIcNknrK48DDwOHAu4Qr86kZST2bDQ2uzHH3jWZWS7T4SmQ28NNoZbVXgAUluviKSElTYhdJlgV5Fs99kuN9t+jPvYFsy65+3GA/QCe2/0Uim8+ybNsItGnw/jzCCngjCEP2K83sfuC2LLcSRCQHFc+JVKb9crz/MPpzLdAly9/r0mA/wKekfxloEndf5e7/4u7dgEOB8YSh/isK8fkilUKJXaQynZvxfhiwDZgZvf8z0N3MBmS0uwBYBSyK3r8EDDazroUMzt2XuPsthCv97xTys0WSTkPxIslypJntk2V7jbs3nKjmNDMbQ0jM1YQh8EfdvS7aPx74MfC0md1KGG6/EDgZuCIqnCP6e6cBb5jZr4B6whX8IHff7tG4XMysA/AyMIFQALgZGEKoyn8p388RESV2kaTJVUXemTBsnnIRcANwFbAJeBBIVcnj7l+a2UDgDuB2QlX7EuBid3+8QbvlZtafUHj3H0A7wnD+/+5k3F8Dc4B/Jjzyti36fhe6+85+lkhFMxWdilQOMxtOqGrvqRnqRJJJ99hFREQSRIldREQkQTQULyIikiC6YhcREUkQJXYREZEEUWIXERFJECV2ERGRBFFiFxERSRAldhERkQT5fwp4a/yjGgfMAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xk-v0Fzy-zDx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "outputId": "257f1851-6de1-4e63-fd21-c9fe9cb7b995"
      },
      "source": [
        "plt.figure(figsize=[8,6])\n",
        "plt.plot(history.history['accuracy'],'r',linewidth=3.0)\n",
        "plt.plot(history.history['val_accuracy'],'b',linewidth=3.0)\n",
        "plt.legend(['Training Accuracy', 'Validation Accuracy'],fontsize=18)\n",
        "plt.xlabel('Epochs ',fontsize=16)\n",
        "plt.ylabel('Accuracy',fontsize=16)\n",
        "plt.title('Accuracy Curves',fontsize=16)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy Curves')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bljzeHJr-0oU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a1541c9-f9dd-472d-ca4d-5994e7a2d0b0"
      },
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "model2 = createModel()\n",
        "\n",
        "model2.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "batch_size = 256\n",
        "epochs = 5\n",
        "datagen = ImageDataGenerator(\n",
        "#         zoom_range=0.2, # randomly zoom into images\n",
        "#         rotation_range=10,  # randomly rotate images in the range (degrees, 0 to 180)\n",
        "        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n",
        "        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n",
        "        horizontal_flip=True,  # randomly flip images\n",
        "        vertical_flip=False)  # randomly flip images\n",
        "\n",
        "\n",
        "# datagen.fit(train_data)\n",
        "\n",
        "# Fit the model on the batches generated by datagen.flow().\n",
        "history2 = model2.fit_generator(datagen.flow(train_data, train_labels_one_hot, batch_size=batch_size),\n",
        "                              steps_per_epoch=int(np.ceil(train_data.shape[0] / float(batch_size))),\n",
        "                              epochs=epochs,\n",
        "                              validation_data=(test_data, test_labels_one_hot),\n",
        "                              workers=4)\n",
        "\n",
        "model2.evaluate(test_data, test_labels_one_hot)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "196/196 [==============================] - 28s 135ms/step - loss: 2.1951 - accuracy: 0.1727 - val_loss: 1.7756 - val_accuracy: 0.3512\n",
            "Epoch 2/5\n",
            "196/196 [==============================] - 27s 133ms/step - loss: 1.8313 - accuracy: 0.3313 - val_loss: 1.5027 - val_accuracy: 0.4411\n",
            "Epoch 3/5\n",
            "196/196 [==============================] - 26s 132ms/step - loss: 1.6167 - accuracy: 0.4112 - val_loss: 1.5406 - val_accuracy: 0.4388\n",
            "Epoch 4/5\n",
            "196/196 [==============================] - 26s 132ms/step - loss: 1.4911 - accuracy: 0.4626 - val_loss: 1.2577 - val_accuracy: 0.5492\n",
            "Epoch 5/5\n",
            "196/196 [==============================] - 26s 131ms/step - loss: 1.3911 - accuracy: 0.4985 - val_loss: 1.2760 - val_accuracy: 0.5408\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 1.2760 - accuracy: 0.5408\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.2760282754898071, 0.5407999753952026]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uJM11lQ_-5Nw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "35f2748c-a81b-412b-9325-195bfa097e12"
      },
      "source": [
        "plt.figure(figsize=[8,6])\n",
        "plt.plot(history2.history['loss'],'r',linewidth=3.0)\n",
        "plt.plot(history2.history['val_loss'],'b',linewidth=3.0)\n",
        "plt.legend(['Training loss', 'Validation Loss'],fontsize=18)\n",
        "plt.xlabel('Epochs ',fontsize=16)\n",
        "plt.ylabel('Loss',fontsize=16)\n",
        "plt.title('Loss Curves',fontsize=16)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Loss Curves')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eA5Za1ii-8uC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "a1182108-ee5e-4ae5-812c-e7a02afe6959"
      },
      "source": [
        "plt.figure(figsize=[8,6])\n",
        "plt.plot(history2.history['accuracy'],'r',linewidth=3.0)\n",
        "plt.plot(history2.history['val_accuracy'],'b',linewidth=3.0)\n",
        "plt.legend(['Training Accuracy', 'Validation Accuracy'],fontsize=18)\n",
        "plt.xlabel('Epochs ',fontsize=16)\n",
        "plt.ylabel('Accuracy',fontsize=16)\n",
        "plt.title('Accuracy Curves',fontsize=16)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy Curves')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GnKGEYSAKOdz"
      },
      "source": [
        "## Transfer Learning using Resnet50 model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HUCbmd2gK-uF"
      },
      "source": [
        "# batch, classes, epochs\n",
        "batch_size =64\n",
        "num_classes = 10\n",
        "epochs = 10\n",
        "\n",
        "# plotting some random 10 images\n",
        "class_names = ['airplane','automobile','bird','cat','deer',\n",
        "               'dog','frog','horse','ship','truck']\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "96R2dTtO--W_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "outputId": "6216b18d-7380-4b36-85b9-e31ad20a2043"
      },
      "source": [
        "## Transfer Learning using Resnet50 model\n",
        "\n",
        "from keras.models import Model, load_model\n",
        "# from keras.applications import ResNet50\n",
        "from keras.applications.resnet50 import preprocess_input\n",
        "from keras.layers import GlobalAveragePooling2D\n",
        "from keras.layers import Dropout\n",
        "\n",
        "# Number of classes in dataset\n",
        "num_classes = len(class_names)\n",
        "\n",
        "def get_model():\n",
        "    # Get base model \n",
        "    # Here we are using ResNet50 as base model\n",
        "    base_model = tf.keras.applications.ResNet50(weights='imagenet', include_top=False)\n",
        "    \n",
        "    # As we are using ResNet model only for feature extraction and not adjusting the weights\n",
        "    # we freeze the layers in base model\n",
        "    for layer in base_model.layers:\n",
        "        layer.trainable = False\n",
        "        \n",
        "    # Get base model output \n",
        "    base_model_ouput = base_model.output\n",
        "    \n",
        "    # Adding our own layer \n",
        "    x = GlobalAveragePooling2D()(base_model_ouput)\n",
        "    # Adding fully connected layer\n",
        "    x = Dense(2056, activation='relu')(x)\n",
        "    x = Dropout(0.5)(x)\n",
        "    x = Dense(num_classes, activation='softmax', name='fcnew')(x)\n",
        "    \n",
        "    model = Model(inputs=base_model.input, outputs=x)\n",
        "    return model\n",
        "\n",
        "# Get the model\n",
        "model = get_model()\n",
        "# Compile it\n",
        "model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n",
        "# Summary of model\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94773248/94765736 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-e3bd05e7de48>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;31m# Get the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m \u001b[0;31m# Compile it\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'categorical_crossentropy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sgd'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-20-e3bd05e7de48>\u001b[0m in \u001b[0;36mget_model\u001b[0;34m()\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGlobalAveragePooling2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase_model_ouput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0;31m# Adding fully connected layer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2056\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'relu'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_classes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'softmax'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'fcnew'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1004\u001b[0m         with autocast_variable.enable_auto_cast_variables(\n\u001b[1;32m   1005\u001b[0m             self._compute_dtype_object):\n\u001b[0;32m-> 1006\u001b[0;31m           \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcall_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1007\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1008\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_activity_regularizer\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/layers/core.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   1220\u001b[0m             self.kernel, ids, weights, combiner='sum')\n\u001b[1;32m   1221\u001b[0m       \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1222\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMatMul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1223\u001b[0m     \u001b[0;31m# Broadcast kernel to inputs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1224\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/tf_export.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    402\u001b[0m           \u001b[0;34m'Please pass these args as kwargs instead.'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    403\u001b[0m           .format(f=f.__name__, kwargs=f_argspec.args))\n\u001b[0;32m--> 404\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    406\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mtf_decorator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_decorator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecorator_argspec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mf_argspec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/gen_math_ops.py\u001b[0m in \u001b[0;36mmat_mul\u001b[0;34m(a, b, transpose_a, transpose_b, name)\u001b[0m\n\u001b[1;32m   5704\u001b[0m       return mat_mul_eager_fallback(\n\u001b[1;32m   5705\u001b[0m           \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtranspose_a\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtranspose_a\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtranspose_b\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtranspose_b\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5706\u001b[0;31m           ctx=_ctx)\n\u001b[0m\u001b[1;32m   5707\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_SymbolicException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5708\u001b[0m       \u001b[0;32mpass\u001b[0m  \u001b[0;31m# Add nodes to the TensorFlow graph.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/gen_math_ops.py\u001b[0m in \u001b[0;36mmat_mul_eager_fallback\u001b[0;34m(a, b, transpose_a, transpose_b, name, ctx)\u001b[0m\n\u001b[1;32m   5737\u001b[0m     \u001b[0mtranspose_b\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5738\u001b[0m   \u001b[0mtranspose_b\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_execute\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_bool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtranspose_b\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"transpose_b\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5739\u001b[0;31m   \u001b[0m_attr_T\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_inputs_T\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_execute\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs_to_matching_eager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_dtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbfloat16\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_dtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhalf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_dtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_dtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_dtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_dtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_dtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomplex64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_dtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomplex128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5740\u001b[0m   \u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_inputs_T\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5741\u001b[0m   \u001b[0m_inputs_flat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36margs_to_matching_eager\u001b[0;34m(l, ctx, allowed_dtypes, default_dtype)\u001b[0m\n\u001b[1;32m    263\u001b[0m       \u001b[0;31m# not list allowed dtypes, in which case we should skip this.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mallowed_dtypes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 265\u001b[0;31m         \u001b[0mtensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    266\u001b[0m         \u001b[0;31m# If we did not match an allowed dtype, try again with the default\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m         \u001b[0;31m# dtype. This could be because we have an empty tensor and thus we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/profiler/trace.py\u001b[0m in \u001b[0;36mwrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    161\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrace_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mtrace_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m           \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mconvert_to_tensor\u001b[0;34m(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)\u001b[0m\n\u001b[1;32m   1564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1565\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1566\u001b[0;31m       \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconversion_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mas_ref\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1568\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36m_constant_tensor_conversion_function\u001b[0;34m(v, dtype, name, as_ref)\u001b[0m\n\u001b[1;32m    337\u001b[0m                                          as_ref=False):\n\u001b[1;32m    338\u001b[0m   \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 339\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mconstant\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    340\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    341\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36mconstant\u001b[0;34m(value, dtype, shape, name)\u001b[0m\n\u001b[1;32m    263\u001b[0m   \"\"\"\n\u001b[1;32m    264\u001b[0m   return _constant_impl(value, dtype, shape, name, verify_shape=False,\n\u001b[0;32m--> 265\u001b[0;31m                         allow_broadcast=True)\n\u001b[0m\u001b[1;32m    266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36m_constant_impl\u001b[0;34m(value, dtype, shape, name, verify_shape, allow_broadcast)\u001b[0m\n\u001b[1;32m    274\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"tf.constant\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_constant_eager_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 276\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_constant_eager_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m   \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36m_constant_eager_impl\u001b[0;34m(ctx, value, dtype, shape, verify_shape)\u001b[0m\n\u001b[1;32m    299\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_constant_eager_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    300\u001b[0m   \u001b[0;34m\"\"\"Implementation of eager constant.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 301\u001b[0;31m   \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_to_eager_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    302\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    303\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36mconvert_to_eager_tensor\u001b[0;34m(value, ctx, dtype)\u001b[0m\n\u001b[1;32m     96\u001b[0m       \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_datatype_enum\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m   \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 98\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEagerTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/keras_tensor.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    253\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m     raise TypeError(\n\u001b[0;32m--> 255\u001b[0;31m         \u001b[0;34m'Cannot convert a symbolic Keras input/output to a numpy array. '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    256\u001b[0m         \u001b[0;34m'This error may indicate that you\\'re trying to pass a symbolic value '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m         \u001b[0;34m'to a NumPy call, which is not supported. Or, '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: Cannot convert a symbolic Keras input/output to a numpy array. This error may indicate that you're trying to pass a symbolic value to a NumPy call, which is not supported. Or, you may be trying to pass Keras symbolic inputs/outputs to a TF API that does not register dispatching, preventing Keras from automatically converting the API call to a lambda layer in the Functional Model."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cVpy_UPvKvhR",
        "outputId": "af1a3838-3f4d-4df0-9e11-d7e4733d99a7"
      },
      "source": [
        "# Training the model\n",
        "history = model.fit(train_data, train_labels_one_hot,\n",
        "              batch_size=batch_size,\n",
        "              epochs=5,\n",
        "              validation_data=(test_data, test_labels_one_hot),\n",
        "              shuffle=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "782/782 [==============================] - 18s 20ms/step - loss: 2.6097 - accuracy: 0.1326 - val_loss: 2.3103 - val_accuracy: 0.1230\n",
            "Epoch 2/5\n",
            "782/782 [==============================] - 14s 18ms/step - loss: 2.1905 - accuracy: 0.1826 - val_loss: 2.1652 - val_accuracy: 0.1622\n",
            "Epoch 3/5\n",
            "782/782 [==============================] - 14s 17ms/step - loss: 2.1399 - accuracy: 0.2071 - val_loss: 2.1272 - val_accuracy: 0.1909\n",
            "Epoch 4/5\n",
            "782/782 [==============================] - 14s 17ms/step - loss: 2.1114 - accuracy: 0.2213 - val_loss: 2.1741 - val_accuracy: 0.1655\n",
            "Epoch 5/5\n",
            "782/782 [==============================] - 14s 17ms/step - loss: 2.0836 - accuracy: 0.2313 - val_loss: 2.1263 - val_accuracy: 0.2210\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "6mtqcXwbLplZ",
        "outputId": "280d542d-dc53-4ee2-be94-097908358d6c"
      },
      "source": [
        "def plot(history):\n",
        "    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15,4))\n",
        "    axes[0].plot(history.history['loss'])   \n",
        "    axes[0].plot(history.history['val_loss'])\n",
        "    axes[0].legend(['loss','val_loss'])\n",
        "\n",
        "    axes[1].plot(history.history['accuracy'])   \n",
        "    axes[1].plot(history.history['val_accuracy'])\n",
        "    axes[1].legend(['accuracy','val_accuracy'])\n",
        "\n",
        "plot(history)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2_Rtdd_oLYPV"
      },
      "source": [
        "## Train with VGG16 model now"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AQa_bHU9LSo4",
        "outputId": "bc067ffa-b528-4151-deae-16ce8c9b239c"
      },
      "source": [
        "from keras.models import Model, load_model\n",
        "from keras.applications import ResNet50,VGG16\n",
        "from keras.applications.resnet50 import preprocess_input\n",
        "from keras.layers import GlobalAveragePooling2D,BatchNormalization,Dropout\n",
        "import tensorflow as tf\n",
        "\n",
        "# Number of classes in dataset\n",
        "num_classes = len(class_names)\n",
        "\n",
        "def get_model():\n",
        "    # Get base model \n",
        "    # Here we are using ResNet50 as base model\n",
        "    base_model = VGG16(weights='imagenet', include_top=False)\n",
        "    \n",
        "    # As we are using ResNet model only for feature extraction and not adjusting the weights\n",
        "    # we freeze the layers in base model\n",
        "    for layer in base_model.layers:\n",
        "        layer.trainable = False\n",
        "        \n",
        "    # Get base model output \n",
        "    base_model_ouput = base_model.output\n",
        "    \n",
        "    # Adding our own layer \n",
        "    x = GlobalAveragePooling2D()(base_model_ouput)\n",
        "    # Adding fully connected layer\n",
        "    x = Dense(4096, activation='relu')(x)\n",
        "    x=  BatchNormalization()(x)\n",
        "    x = Dropout(0.5)(x)\n",
        "    x = Dense(num_classes, activation='softmax')(x)\n",
        "    \n",
        "    model = Model(inputs=base_model.input, outputs=x)\n",
        "    return model\n",
        "\n",
        "# Get the model\n",
        "model = get_model()\n",
        "# Compile it\n",
        "opt = tf.keras.optimizers.SGD(learning_rate=0.001)\n",
        "model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])\n",
        "# Summary of model\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58892288/58889256 [==============================] - 0s 0us/step\n",
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         [(None, None, None, 3)]   0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, None, None, 64)    1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, None, None, 64)    36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, None, None, 64)    0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, None, None, 128)   73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, None, None, 128)   147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, None, None, 128)   0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, None, None, 256)   295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, None, None, 256)   590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, None, None, 256)   590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, None, None, 256)   0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, None, None, 512)   1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, None, None, 512)   2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, None, None, 512)   2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, None, None, 512)   0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, None, None, 512)   2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, None, None, 512)   2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, None, None, 512)   2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, None, None, 512)   0         \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d_1 ( (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 4096)              2101248   \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 4096)              16384     \n",
            "_________________________________________________________________\n",
            "dropout_13 (Dropout)         (None, 4096)              0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 10)                40970     \n",
            "=================================================================\n",
            "Total params: 16,873,290\n",
            "Trainable params: 2,150,410\n",
            "Non-trainable params: 14,722,880\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aQH62FydLsgT",
        "outputId": "5f30de94-93ab-4c3f-ad8f-959064e86656"
      },
      "source": [
        "# Training the model\n",
        "history = model.fit(train_data, train_labels_one_hot,\n",
        "              batch_size=batch_size,\n",
        "              epochs=5,\n",
        "              validation_data=(test_data, test_labels_one_hot),\n",
        "              shuffle=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "782/782 [==============================] - 12s 14ms/step - loss: 2.0942 - accuracy: 0.3274 - val_loss: 1.3616 - val_accuracy: 0.5256\n",
            "Epoch 2/5\n",
            "782/782 [==============================] - 11s 14ms/step - loss: 1.5821 - accuracy: 0.4728 - val_loss: 1.2951 - val_accuracy: 0.5535\n",
            "Epoch 3/5\n",
            "782/782 [==============================] - 11s 14ms/step - loss: 1.4917 - accuracy: 0.5054 - val_loss: 1.2497 - val_accuracy: 0.5652\n",
            "Epoch 4/5\n",
            "782/782 [==============================] - 11s 14ms/step - loss: 1.4236 - accuracy: 0.5191 - val_loss: 1.2320 - val_accuracy: 0.5738\n",
            "Epoch 5/5\n",
            "782/782 [==============================] - 11s 14ms/step - loss: 1.3805 - accuracy: 0.5320 - val_loss: 1.2149 - val_accuracy: 0.5833\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "KCwVh4bzLxHn",
        "outputId": "28ba8e86-25da-4a27-8e0c-df79df998a37"
      },
      "source": [
        "def plot(history):\n",
        "    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15,4))\n",
        "    axes[0].plot(history.history['loss'])   \n",
        "    axes[0].plot(history.history['val_loss'])\n",
        "    axes[0].legend(['loss','val_loss'])\n",
        "\n",
        "    axes[1].plot(history.history['accuracy'])   \n",
        "    axes[1].plot(history.history['val_accuracy'])\n",
        "    axes[1].legend(['accuracy','val_accuracy'])\n",
        "\n",
        "plot(history)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2y8a1kUfLzeN"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}