CIFAR_with_Tensorflow_KERAS_CNN_and_DNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    },
    "colab": {
      "name": "CIFAR with Tensorflow/KERAS - CNN and DNN.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "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_with_Tensorflow_KERAS_CNN_and_DNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ij2ahOKYIQ1I"
      },
      "source": [
        "### Import the Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "opb2RYYyIQ1L"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "sns.set()\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GHjBdX16IQ1N"
      },
      "source": [
        "### Load the Dataset and Prepare testing and training data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HuXgk5EOIQ1O"
      },
      "source": [
        "from keras.datasets import cifar10\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,Conv2D,Flatten, MaxPool2D"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "17vp1CzIIQ1P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc606c3f-e7b4-4697-dac4-ba0a7ea0b3eb"
      },
      "source": [
        "(x_train,y_train),(x_test,y_test)=cifar10.load_data()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 6s 0us/step\n",
            "170508288/170498071 [==============================] - 6s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WPq4C628IQ1R",
        "outputId": "857ceb44-6bcb-415e-ed66-8b2fa9ff1a23"
      },
      "source": [
        "print(x_train.shape)\n",
        "print(x_test.shape)\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(50000, 32, 32, 3)\n",
            "(10000, 32, 32, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "id": "x1SRuGLcIaLD",
        "outputId": "d35c0223-4129-41e9-88ac-1fc1e71b42ce"
      },
      "source": [
        "print('Training data shape : ', x_train.shape, y_train.shape)\n",
        "\n",
        "print('Testing data shape : ', x_test.shape, y_test.shape)\n",
        "\n",
        "# Find the unique numbers from the train labels\n",
        "classes = np.unique(y_train)\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(x_train[0,:,:], cmap='gray')\n",
        "plt.title(\"Ground Truth : {}\".format(y_train[0]))\n",
        "\n",
        "# Display the first image in testing data\n",
        "plt.subplot(122)\n",
        "plt.imshow(x_test[0,:,:], cmap='gray')\n",
        "plt.title(\"Ground Truth : {}\".format(y_test[0]))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Ground Truth : [3]')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x144 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a5k1v-oCNmD0"
      },
      "source": [
        "## Display images for each category"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0f6Culv9NpTp",
        "outputId": "d45e7287-e85d-4cdd-fb23-36a0e7a33131"
      },
      "source": [
        "for category in np.unique(y_train):\n",
        "  counter=2\n",
        "  print(category)\n",
        "  for idx, label in enumerate(y_train):\n",
        "    if counter <1:\n",
        "      break\n",
        "    if label==category:\n",
        "      \n",
        "      counter-= 1\n",
        "      plt.imshow(x_train[idx])\n",
        "      plt.show()\n",
        "  print(\"*\"*50)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        },
        {
          "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",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**************************************************\n",
            "1\n"
          ]
        },
        {
          "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",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**************************************************\n",
            "2\n"
          ]
        },
        {
          "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",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**************************************************\n",
            "3\n"
          ]
        },
        {
          "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",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**************************************************\n",
            "4\n"
          ]
        },
        {
          "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",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**************************************************\n",
            "5\n"
          ]
        },
        {
          "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",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**************************************************\n",
            "6\n"
          ]
        },
        {
          "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",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**************************************************\n",
            "7\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EaXrgT1VAGyu/UuqLSqndAFYBeFAp9UxN9H4ANymlngdwU60tCMLLhMOu/FrrDwH4kKH/OQDnzB8hCMLLAXm9VxB8iii/IPgUUX5B8Cmi/ILgUzr+kk8YAXi1e1C46V4UCfCpOSQZZCrNX9TIV7jb48LLzqeyjadyt92Wf/6RsX98D48EHOnjL+T09XTTtuNw11zR4m7yXPNxF4sWH5vL3XkTk/zlIBjqxc1S8czRhdkM39f0TOsxTza13QCP4Axa3Kn7J8zu4BHLi1JI8oSgaUutvqJnqQEZ4Mk1Q0nzS1ZugLcDgfkv+Zj6mpGVXxB8iii/IPgUUX5B8Cmi/ILgU0T5BcGniPILgk/puKsvWkkgUKm6gWKVRpLK5Ut5/Prj7gFj/xR4VNmKjTwO/fxLTqWyk09ZQWVDSfPp+8l9P6NjUtPcHZnLds1pN1xbk+M8YtGxxIZXwub7e7rIo8oyDneLDhA3KwDEwBOhusQdOW2J3nTm1LpzmrYRifIox0KJz3+qYHYtRiyJRPMhnt8gDx7P74C7MXNlfh2EesxuzGRXnLZdQ1KeaJjnjgBk5RcE3yLKLwg+RZRfEHyKKL8g+BRRfkHwKR239ucz5XrZoVyqYaUNxnigRZHEWaxYu5qOef2151LZiWoJlUUT3Aq88UKzl6BsOatbvvp9Ktv64h9oO1DkG3XL3KqMqDmAZNJitR8csOQLTHALcj7Fg1zSJDtu1hJfFAq1HnM51DiWYpkPnCnwIKhc0Hw+fr+Hp5/cNc73lbYEQXmWtPhFWMq2Lekz9nd3JWl7MjPf6+BavA2ArPyC4FtE+QXBp4jyC4JPEeUXBJ8iyi8IPkWUXxB8SsddffunDiGXn8bZAHZPNMpfPfLUI3TM0vVmV8g1730rHXPCqdydFwjznHvFoiVwwzEHsrxi8yl0zM4nXqSyB+//eUt7W7aRLy/q8HJSpSIPqPEq5oCavjh3Na0eMRezBABY8sJlHO4+ZAE100VLLr457QP5xpwjET6PdITPI9KfNPaP7ebFOPen+faWrOEBY3t3c/dhucRz+AUDZndqaqrJlTrc2i6U588xGLH4UdGm8iulbgVwNapltTdprZ+u9e8AUKj9A4CPaq1/2s42BUHoLO2u/N8B8A8AfmWQvW32ZiAIwsuHtpRfa70FAJRSx3Y2giAcNwIVyyuIc6k95r9pzmP/DIAAgC0APq61nm5zc6MAtre9c0EQjpZ1AHbM7Vyowe8irfWYUioG4AsAbgNw3ZFs4Kc//3vk8tO46opP49s//FS9/0f3cYNfKWB2UrxUDH7JsNkgCQA//OoPqKzZ4PfTpw/gT16xrN4+nga/0zedRGXdXbwgxuQhbjSbnDIbzdo1+N395ON41xmb6+1IhL+3ni7PUBkz+G2zGPz2pRbf4FcscIPfjR9/h7H/wivX1j+/Xv13/ET/33o7YzjmZKQfb9zwYbqfBbn6tNZjtb9FAF8GcMFCticIwvHjqFd+pVQXgLDWekYpFQDwdgBbj3Q7S9cuR7FUzZG2fP2qen+5m7spTj/rlcb+E1+5nI5xKzxnWsnlUWAOKXcFAAiZV89oNz+tayyraubbv2htRxvbD5f4z7NUlq9MUZLD7/STT6BjRtdx2UyWn8fsQf4EtT9nPo8HcnwFD4Van2h2NW0jFOYRhN3L+ap6wRvNpdkOfP/f6Zi9pb1U9uY/fx2V/fLnv6GyRx/eSWV7yBNDqbhmTrtxTQQM5b8ClnJhQPuuvi8CeCuA5QAeVEpNALgSwLeUUiEAIQDPAvhgO9sTBKHztGvt/xCADxlEZyzudARBOF7I672C4FNE+QXBp4jyC4JPEeUXBJ/S8ai+3qX9KFWqUUz9I4P1/hs+/G46Jpow37NKQe7+CVpKSQUtpyGR6KGySsW8zbLHXW8r1nJ35IZTTqLt3U/xF0YqLt9fKGLOduqEeZLOrS9yN9TBaf4Czf5D3A14aMbsuk0ZXFSzBEOtrsM9zlT9c3ecu2DPufQiKnvVG84x9v/md/xl09wLY1TW1c8Tml751ldT2fPPfJvKtj5mDpW55MrW62PyUKOE2/LRgXn/Px7m1y4gK78g+BZRfkHwKaL8guBTRPkFwaeI8guCTxHlFwSf0nFXX87JwvGqLqJsseGq6xrkrigPZjcPc70BQCDE73PlIo8sq1Rs90dzpJ1T4lGC/cu4++XKq99A29/Y/z06Ljdtq8lmdqVNBHnU5JJhno/AFDc+S9GSlDLcZY6jT4TM+QYAYHjpspb26tMa7XPOM9dJBIBzX7eZygL95u9zxbpBYz8AeB7PYfDCC9xFeOUVr6IypUao7PEntLF/9459tL32xBXz/n8sxHMlALLyC4JvEeUXBJ8iyi8IPkWUXxB8iii/IPiUjlv7XddB2a1ansvlhgXamn6MWPXDFmtz2ZKivGI5DZUKl5XKZqt+Jcit72VLKanVp43SdmJ5Lx038/s9VBYImy3Vq89ZR8f86TWXU9m+A/uo7OBBnrU9nTV7aMoBbu1fOdKacfnd//Vt9c9rLFlznTAP+pnKm7P0rlrLrf3hIM+c/Ifn+bnv+jN+HZx15olU9uQT24z9+axD225p/r7cw6Tll5VfEHyKKL8g+BRRfkHwKaL8guBTRPkFwaeI8guCT+m4qw8IIIBA/dMs5RJ314TDZpeeZ4lvyeW4i83mzgP4Rt2yeY6ROA8EcSy320R/63EFm9rdK/rpuP1Znruwr8/sIhxePz/nW33MaDeVxVespbITA1xWypsDiTIF/r14bqsbcPVJQ/XPwaAliKvCvzMW7LJk6ZCxHwB6enmQWTTC3YDJHh4g9cpX8bJtA99+2NjvlXg7EZt/DcdC3PUNtKH8SqkhAPcAWA/AAbANwPu01oeUUucCuBNAAtUSwNdprQ8ebpuCIHSedh77KwBu0VorrfUmAC8CuFkpFQRwL4AbtdYbAPwSwM3HbqqCICwmh1V+rfWk1vqhpq5HAawFsBlAQWu9pdZ/B4BrFn2GgiAcEwKVw7wC2ExttX8AwPcA7AHwX7TWVzTJcwBWaa0n29jcKACeCUEQhMViHao/y1s4UoPflwBkANwG4KqFzwl48sB9cNwMzlnxF/i3vV+t97vcnkMNftEIf5ApEOMccDiDH5e5ZbOxKhIPGPsBoGQz+FUahrbNS67F4+P319tf+z/fouPu/8oDVMYMfjfdfDUd8ydXX0plRYdnAArxw16wwe+S0b/AQzsa14clMROKFR4vECKZg3Y/eYCO+dRHeYGNU8/kMQaf/Aw/xy8+zdfHT3zsLmP/Nf/5NfXPn/3IP+Hjn393vf2may6Y9/9joW5sXvEOup+2XX1KqVsBnATgWq21B2AXqo//s/IlALw2V31BEDpMWyu/UuqzqP7Gv0JrPXurfhxAQil1Ye13//sBfPNIJ1AsVVAoV3965J3GT5CQ5dYeDZunXSY59QAgV+QrVr5gKfMVPPIcfl0h7ipzA3x7wWBrlGDRbbT7R7hrrhzirsVgxOzaGhzk2yu5fOV0SP5EAAiSJyEACLBxFpedU2r9zhyvUb4rUOGPGRXLdRANmctrdfdyV9/AEn5+R1bOz503i2uJBhxaw+e4Zr15LhU3QNvhwPzzETL0NdOOq28jgI8BeB7AI0opANiutb5KKXU9gDuVUnHUXH2H254gCC8NDqv8WutnABhvIVrrRwBsWuxJCYJw7JHXewXBp4jyC4JPEeUXBJ8iyi8IPqXjUX3FElCoeZYKTd6goCVErwSz265UsriaAhb3T8zs/gEAt8xdUZ5n3mbB4lYsOJbjav42lgGZTKre7Onj7sNQlEdvReIJY38sssTYDwDFnCUBadDyUk4xR2Vhj0RiWl7mqsyxM1daoj65OzKX5/MoBs3f9eRklo7JO3x7yS7z+QWA8Ule2qxc4gfeRaIBs1mXtnO5+a5UL8zPESArvyD4FlF+QfApovyC4FNE+QXBp4jyC4JPEeUXBJ/ScVdfruQiV3PbZJ2Ga6Jc4u6yMInbT6d5rbieLp6EcekQj+iqRCw1/kgilHzBEkGYy1OZG2p1sWWzDfeT63G3TTDKo7emm9yFzezcPkXHDIz0UFkokaGyissj/jxSRzFd4Oej4DS5FU8Axg815mxLQlOyJH8tk+9z1xivQTiTNp9DAAhackikMvxcBSvcvZwvmOe47YU9tD2Tmn/MTpSfB0BWfkHwLaL8guBTRPkFwaeI8guCTxHlFwSf0nFrfzabRcapWkXTTdbRaIRbQ2Nhc061aNScrw4AggF+qAGLzHEKVJbLmQM+SpagDUt6uXmi5nbJlpE2zu/h09Nmq/4Pf/QgHdM79EYqGz3Bkp/Qkt+vTPIC5vI8UCg9x1o+M91ol8v8fESilpyGnlm278AEHeNYgrvChjJZ7YxzHX7cZRLUtnfX3pb2zqb2xMR8z0J33F6uS1Z+QfApovyC4FNE+QXBp4jyC4JPEeUXBJ8iyi8IPqXjrr5YLAo3UHXrJZpy6cXj3NXHCnLGB8y5zwAgFrYEUuS5O29mmudhy5Nccd3d5uKYAFCxJK2b6zpsaVtu0119SSo74+wzjf07xrbRMV+9/R4qu/jVr6Kyk09bTWV9y8xu2EqFu6PCoThtB8DPY9nhbsBDM+bgrxde3EHH2M69a3HBuh4PuMpbCp4mus07jKRb1TUSb7SzhkKowYA9sKedcl1DAO4BsB6AA2AbgPdprQ8ppSoAngIw65i8Xmv91OG2KQhC52ln5a8AuEVr/RAAKKU+B+BmAO+pyc/XWvPYRUEQXpK0U6tvEsBDTV2PAvjAsZqQIAjHh4AtKcJclFJBAA8A+J7W+ou1x/7HUb2J/BjA3zaV8D4cowC2H9l0BUE4CtahWkW7hSM1+H0JQAbAbbX2Gq31mFKqF1W7wCcA/M2RbPD7z34NOSeFa0//S9y/9Qv1fqvBjxSpsBXmsBn8bJlfFtvg59iyzJQbmXuuO/fvcO+jn6y3X3iav3t+z50PUNmGDScb+20Gv2SUn6vFNvgVXW64KxQa68h7X/d3+MqDjfPhWsbZ3vsvGgxjAPCjf/kVHfPU73ZS2fU3XEVlp56+isryhiIb9bl885fG/r37Juuff/evT+GVlzUKZN9w49Xz/n9Psh/vvvwv6X7advUppW4FcBKAa7XWHgBorcdqf1MA7gJwQbvbEwShs7S18iulPgtgM4ArZh/rlVIDAApa67xSKgzgbQC2HukEInARqbltIk3um6DLXSHxkLlEUsUSMlexlP/yXMsTQ4zn/ouSFTKR6KJj0mluG3Xd1ieJQKDhKoon+TzK4CvderXW2L9h0zI65of3P0xl3/5/v6ayy7NmtyIAnPVa8zy8IL8E55a0ao6WDAT4ulWpcBfbwYPmJ6h0hrt7V69dQ2XpTJrK9h88RGVhy3H3DZllwchwS3vVmkY7k51fbiwE/gQHtOfq2wjgYwCeB/CIUgqo/la/BcCdtd/9EQCPoPrYLwjCy4B2rP3PAGC30tMWdzqCIBwv5PVeQfApovyC4FNE+QXBp4jyC4JP6XhUX9kpolxLklluSpZZdrj7LUwCwZJJswsQACKWhKAhi9vFlkiUvR1ZLPCXHD2Hv5wSdCO0XS7ycaUS39/klNm1dd6rT6FjzrnwLCp79OFnqGz7zt1UtnzM/JJPrJsnBO3rG2ztCDTOh2Mp55ZKzXd7zZLOmF/MOunU9XRMf/9yKusd4FGJ0zO8zFcoyMetOWmlsb+Qa12r15/SeKkq58w/ZltCW0BWfkHwLaL8guBTRPkFwaeI8guCTxHlFwSfIsovCD6l466+fKGMbKEa25xtinEulXm8c6lsvmc5Do/mSia469AWGw5LhFgoZD59rsWdV8rz48plWqPzslON9oE9PJ5/2dIlVDbQ12/el8U9uHbTUiqbKnBZNMzXkgzxepWCPCIxmmiV5ZsSc7pliys4xhOaLltpjrEfPYG7xRxLQlBLcCGcEnfnzaR4noiubrPLOhFvPebEYMMNHU7Or0EYivOahYCs/ILgW0T5BcGniPILgk8R5RcEnyLKLwg+RZRfEHxKx119qVQBqXweADA9k29rjEuSe+by3MUW8CzpnAt8v8ydBwCxuDmppi2aKpPjieH+8MIAAAZySURBVCJLc9xXhXIj6WjPYA8dd97Fm6lszeiIsT8Y4eejZ5AnID397FOpLBnlLrbeXnM68yIs535OtGVzO2BxK8YsEXMsx2vBsXwvlnTr8QSPJO3p4d9ZNMavkVDUfM05xVb3bLKr8T2Zthe1JJ8FZOUXBN8iyi8IPkWUXxB8iii/IPgUUX5B8Ckdt/Z7iMCrlRXymsoLRcKWoISgWZbJcsux6/BAlmyG53wLWazKA/1mq3IobLGyWqy88TnBGfH+hrV4ObEAA0DXEl4CLNFjnr/r8eMKe3yO4QH+vXTFuJcgEjbPv5Tn30vQDdD23FJezaTSPGimSK4Dm/cgbDn3FV4FDrG45TxG+HnM5sxzDAZjtJ1Jz/dWhD17wex2a/V9B9Uyvx6qVXpv0lpvVUptAHA3gCEAEwDeqbXm5V8FQXjJ0O5j/7u01q/UWp8B4FYAX6v13wHgdq31BgC3A7jzGMxREIRjQFvKr7Vufo7qA+AppYYBnAngvlr/fQDOVErxgG9BEF4yBFju+bkope4CcDmqRTtfDyAO4Ota641N/+dZANdprZ9oY5OjqFb7FQTh2LIOwI65nW0b/LTWNwCAUup6AJ/DIpXj/uoDn0cqP42PvPkz+Px3G5u0GfwiUbOskD9agx83mNkNfuYsOT29/LXOfJm/VhsKNY7rhov/Cnc9/Ll6O5fjx5ZO8/n39Jhfq3UtrzvDYsQqF7jwaAx+qTw3zoWbqrP8tzd/Cv/w3U/X27aiHRnL+Vhsg1/UYrjr6zefewBwXX4emcGveZn+xJ/djM9883/W24XCfINff9cg/uqtn6T7OWJXn9b6HgCXAtgNYKVSKgQAtb8rAIwd6TYFQTj+HHblV0p1AxjQWo/V2lcCmARwEMBWAO8AcG/t75Na60NHMoFSuQKnVL2nzf4FgLIlmCJP8uBls+ZSTAAQs5XrCvMVyxLXg0rA7Oorlrkbqmi545fmlFyayTfaFfBtxnr5JMsBc8CKU+Dbc4t8jsUsf4JyQnw1Zk9y45MH6ZjBgdYnq3yq8f16lp+r4/v4JVhwzHNcMsJLcrkBnsdxMjVFZTSKCEDQcmHt22vepue1bm/v7vH6Z9eb/316lusCaO+xvwvAN5VSXQBcVBX/Sq11RSn1fgB3K6U+CWAKwDvb2J4gCC8BDqv8WusDAM4lsucAnLPYkxIE4dgjr/cKgk8R5RcEnyLKLwg+pZOBPSEA6Ek0fKF9yYZ1NwBuYWVEwAMpbP7Yiset2zZrfxdJ4RSNc89C2FIdqDzHYtubaJyPSsVSVSjAZUFyfy9ZvAdumFupnQC36EdDlkAWVt3IEqDT19U7pz1Q/2yz9uf7eEquIvEiDXQP0jHBCE8LFg/z73ru/Fu2GeQXVoEEY8095sHeoYbMYO3v766fL+MBtP2G3zHgQgC/6tTOBcFHXARgy9zOTip/DMDZAPYBlmVIEISjJQRgBMBvAcx7QaOTyi8IQgcRg58g+BRRfkHwKaL8guBTRPkFwaeI8guCTxHlFwSfIsovCD5FlF8QfErHi3b4Pfe/UupWAFejmtB0k9b66Vq/L8+LUmoIwD0A1gNwAGwD8D6t9SGl1LmopodPoJqQ8jqtNU8F9EfCsaqb8VJY+f2e+/87AF4NYOecfr+elwqAW7TWSmu9CcCLAG5WSgVRTRd3Y+2c/BLAzR2c5/HkmNTN6KjyS+5/QGu9ZTY/4ix+Pi9a60mt9UNNXY8CWAtgM4CC1no2QOUOANcc5+l1hGNVN6PTK/9qAHu01i4A1P7urfX7GTkvAGqr/QcAfA/AGjQ9HWmtxwEElVI8FvePCKXUXUqpXQD+N4B3YRGukU4rvyDY+BKqv3Fv6/REOo3W+gat9RoAH0e1bsaC6bTyj0Fy/5vw/XmpGUJPAnCt1toDsAvVx/9Z+RIAntZ6skNT7AiLWTejo8pfs9TO5v4HjjL3/x8bfj8vSqnPovob/y1a69k49McBJJRSF9ba7wfwzU7M73iilOpWSq1uapvqZgBHcY10PJ5fKXUyqu6KAdRy/2utdUcndRxRSn0RwFsBLAcwDmBCa73Rr+dFKbURwNMAngcwW6Nsu9b6KqXU+ahatONouPoOdGSixwml1DIA30W1fsZs3Yz/obV+YqHXSMeVXxCEztDp3/yCIHQIUX5B8Cmi/ILgU0T5BcGniPILgk8R5RcEnyLKLwg+5f8DkYTM3Vtsf1sAAAAASUVORK5CYII=\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": "stream",
          "name": "stdout",
          "text": [
            "**************************************************\n",
            "8\n"
          ]
        },
        {
          "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",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**************************************************\n",
            "9\n"
          ]
        },
        {
          "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",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**************************************************\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uhMex-_KOMSl"
      },
      "source": [
        "# Approach -1 - Using simple DNN or FC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5BV8jl6COP3g"
      },
      "source": [
        "# Load the data\n",
        "(x_train,y_train),(x_test,y_test)=cifar10.load_data()\n",
        "\n",
        "# train set / data \n",
        "x_train = x_train.reshape(-1, 32*32*3)\n",
        "x_train = x_train.astype('float32') / 255\n",
        "\n",
        "\n",
        "\n",
        "# test set / data \n",
        "x_test = x_test.reshape(-1, 32*32*3)\n",
        "x_test = x_test.astype('float32') / 255\n",
        "\n",
        "# train set / target \n",
        "y_train = tf.keras.utils.to_categorical(y_train , num_classes=10)\n",
        "\n",
        "# test set / target \n",
        "y_test = tf.keras.utils.to_categorical(y_test , num_classes=10)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7eZQ1BQ3QzPz",
        "outputId": "e2163a23-263f-470b-ad3c-6fc657931565"
      },
      "source": [
        "x_train.shape,x_test.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((50000, 3072), (10000, 3072))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qdoz13C0Omds",
        "outputId": "e1a29362-04c7-47f1-c6d8-2ed9099d4fcf"
      },
      "source": [
        "#Models\n",
        "model = Sequential()\n",
        "model.add(Dense(800, input_dim=3072, activation=\"relu\"))\n",
        "model.add(Dense(10, activation=\"softmax\"))\n",
        "\n",
        "# For using custom metrics\n",
        "# https://keras.io/api/metrics/#custom-metrics\n",
        "\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"SGD\", metrics=[\"accuracy\",tf.keras.metrics.AUC()])\n",
        "history = model.fit(x_train, y_train, validation_data=(x_test,y_test),\n",
        "                    batch_size=200, \n",
        "                    epochs=5,  \n",
        "                    verbose=1)\n",
        "# https://keras.io/api/models/model_training_apis/"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "250/250 [==============================] - 5s 9ms/step - loss: 1.9968 - accuracy: 0.2895 - auc: 0.7426 - val_loss: 1.8724 - val_accuracy: 0.3621 - val_auc: 0.7865\n",
            "Epoch 2/5\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 1.8424 - accuracy: 0.3575 - auc: 0.7931 - val_loss: 1.8003 - val_accuracy: 0.3742 - val_auc: 0.8029\n",
            "Epoch 3/5\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 1.7855 - accuracy: 0.3807 - auc: 0.8081 - val_loss: 1.7664 - val_accuracy: 0.3822 - val_auc: 0.8128\n",
            "Epoch 4/5\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 1.7452 - accuracy: 0.3945 - auc: 0.8182 - val_loss: 1.7249 - val_accuracy: 0.4040 - val_auc: 0.8221\n",
            "Epoch 5/5\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 1.7135 - accuracy: 0.4074 - auc: 0.8256 - val_loss: 1.7037 - val_accuracy: 0.4111 - val_auc: 0.8271\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f88OJHR6RGEz",
        "outputId": "cb292181-fdc8-4a77-f299-2ab3c59cd47b"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 800)               2458400   \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                8010      \n",
            "=================================================================\n",
            "Total params: 2,466,410\n",
            "Trainable params: 2,466,410\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZGNPCWwHPKU2",
        "outputId": "f9c14833-be67-4919-e533-0fbb176fb3a0"
      },
      "source": [
        "history.history"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': [0.28953999280929565,\n",
              "  0.3575200140476227,\n",
              "  0.38071998953819275,\n",
              "  0.39447999000549316,\n",
              "  0.4074400067329407],\n",
              " 'auc': [0.7426010370254517,\n",
              "  0.7931068539619446,\n",
              "  0.8081432580947876,\n",
              "  0.8181830644607544,\n",
              "  0.8256331086158752],\n",
              " 'loss': [1.9968160390853882,\n",
              "  1.8423787355422974,\n",
              "  1.7854588031768799,\n",
              "  1.7451637983322144,\n",
              "  1.7135138511657715],\n",
              " 'val_accuracy': [0.3621000051498413,\n",
              "  0.3741999864578247,\n",
              "  0.3822000026702881,\n",
              "  0.40400001406669617,\n",
              "  0.41110000014305115],\n",
              " 'val_auc': [0.7865101099014282,\n",
              "  0.802906334400177,\n",
              "  0.8127662539482117,\n",
              "  0.8220681548118591,\n",
              "  0.8270743489265442],\n",
              " 'val_loss': [1.8724199533462524,\n",
              "  1.8002921342849731,\n",
              "  1.7664192914962769,\n",
              "  1.724945306777954,\n",
              "  1.7036998271942139]}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "XhZg7hTqOmVI",
        "outputId": "d6c59781-e4b7-497f-d718-8dae9447612d"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "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": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Loss Curves')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "7atA3EVuTG1I",
        "outputId": "94b0bae8-fc83-4060-8cbd-ea7d9fd957f4"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "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": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy Curves')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "Y-j_GIG_TVNT",
        "outputId": "c41927e0-055b-4744-e6bd-c1bc70d00975"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=[8,6])\n",
        "plt.plot(history.history['auc'],'r',linewidth=3.0)\n",
        "plt.plot(history.history['val_auc'],'b',linewidth=3.0)\n",
        "plt.legend(['Training AUC', 'Validation AUC'],fontsize=18)\n",
        "plt.xlabel('Epochs ',fontsize=16)\n",
        "plt.ylabel('AUC',fontsize=16)\n",
        "plt.title('AUC Curves',fontsize=16)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'AUC Curves')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TdLCjMNAAl1U"
      },
      "source": [
        "# Approach -2 : Using CNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aHl7atlQIQ1S"
      },
      "source": [
        "## Change the Datatype to Float and Normalize it"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k9QWdrxZIQ1S"
      },
      "source": [
        "# Load the data\n",
        "(x_train,y_train),(x_test,y_test)=cifar10.load_data()\n",
        "\n",
        "#Normalize\n",
        "x_train=x_train.astype('float64')/255\n",
        "x_test=x_test.astype('float64')/255"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HfKVxkGNIQ1T",
        "outputId": "e8812d72-6450-4b17-931c-48fec94c465f"
      },
      "source": [
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(50000, 1)\n",
            "(10000, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PIwm67YzIQ1U"
      },
      "source": [
        "## Do the one hot Encoding of the Dependent Variable using Keras.utils"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nxfOBs_WIQ1U"
      },
      "source": [
        "from keras.utils import np_utils\n",
        "from tensorflow.keras.utils import to_categorical"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nfq2PcoBIQ1V"
      },
      "source": [
        "num_classes= len(np.unique(y_train))"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HUkpy3LzIQ1W"
      },
      "source": [
        "y_train= to_categorical(y_train,num_classes)\n",
        "y_test=to_categorical(y_test,num_classes)\n"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lIf4vb8QIQ1W"
      },
      "source": [
        "### Divide the Training Dataset into  validation, Training and Test."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MkmQQQ3hIQ1W"
      },
      "source": [
        "(y_train, y_valid) = y_train[5000:], y_train[:5000]\n",
        "(x_train, x_valid) = x_train[5000:], x_train[:5000]"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ANfGVuusIQ1X",
        "outputId": "3509200c-5bac-4942-a5b4-4c2a71831a49"
      },
      "source": [
        "print(y_train.shape)\n",
        "print(x_train.shape)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(45000, 10)\n",
            "(45000, 32, 32, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bpBahc2IIQ1X",
        "outputId": "d338ce2a-2140-45d3-b2f5-aafe8bb0f154"
      },
      "source": [
        "print(y_valid.shape)\n",
        "print(x_valid.shape)\n"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5000, 10)\n",
            "(5000, 32, 32, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tDX2vNT2UeWH"
      },
      "source": [
        "#  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fSeDSbnCUiFE"
      },
      "source": [
        "## Build a CNN Architecture with KERAS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sor7CUOWdDdT"
      },
      "source": [
        "#### CNN using just the CONV layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_EtQm-aOUdm0",
        "outputId": "57f8c760-b6c7-49f0-dc39-fc9db4df94ab"
      },
      "source": [
        "model=Sequential()\n",
        "model.add(Conv2D(filters=9,kernel_size=3,activation='relu',input_shape=(32, 32, 3)))\n",
        "model.add(Conv2D(filters=6,kernel_size=3,activation='relu'))\n",
        "model.add(Conv2D(filters=3,kernel_size=3,activation='relu'))\n",
        "model.summary()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 30, 30, 9)         252       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 28, 28, 6)         492       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 26, 26, 3)         165       \n",
            "=================================================================\n",
            "Total params: 909\n",
            "Trainable params: 909\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A5CrFezxaFCi",
        "outputId": "5c7341ab-5ff5-475c-a98e-088d39c1ffdf"
      },
      "source": [
        "model.add(Flatten())\n",
        "model.add(Dense(units=256,activation='relu'))\n",
        "model.add(Dense(units=10,activation='softmax'))\n",
        "model.summary()\n"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 30, 30, 9)         252       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 28, 28, 6)         492       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 26, 26, 3)         165       \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 2028)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 256)               519424    \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                2570      \n",
            "=================================================================\n",
            "Total params: 522,903\n",
            "Trainable params: 522,903\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C30k3SRLbFQM",
        "outputId": "2c40abaf-a501-4541-a12f-243d739e2edb"
      },
      "source": [
        "#Compile & Train the model\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"SGD\", metrics=[\"accuracy\"])\n",
        "history = model.fit(x_train, y_train, validation_data=(x_valid,y_valid),\n",
        "                    batch_size=200, \n",
        "                    epochs=5,  \n",
        "                    verbose=1)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "225/225 [==============================] - 30s 13ms/step - loss: 2.2909 - accuracy: 0.1080 - val_loss: 2.2496 - val_accuracy: 0.1366\n",
            "Epoch 2/5\n",
            "225/225 [==============================] - 3s 11ms/step - loss: 2.1495 - accuracy: 0.2148 - val_loss: 2.0538 - val_accuracy: 0.2702\n",
            "Epoch 3/5\n",
            "225/225 [==============================] - 3s 11ms/step - loss: 2.0202 - accuracy: 0.2780 - val_loss: 1.9644 - val_accuracy: 0.3064\n",
            "Epoch 4/5\n",
            "225/225 [==============================] - 3s 12ms/step - loss: 1.9496 - accuracy: 0.3097 - val_loss: 1.9002 - val_accuracy: 0.3350\n",
            "Epoch 5/5\n",
            "225/225 [==============================] - 3s 12ms/step - loss: 1.8992 - accuracy: 0.3281 - val_loss: 1.8562 - val_accuracy: 0.3438\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "oDo6CUVic0yN",
        "outputId": "f0f54d82-f430-4bef-ccf1-f1e459937682"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "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": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy Curves')"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "aS7jEsnBc2rA",
        "outputId": "ed494960-716e-401d-d6e8-8bc85e6c692e"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "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": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Loss Curves')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WkN168nwdK0a"
      },
      "source": [
        "#### CNN using CONV+ POOLING Layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RPKgVzWMdOlr",
        "outputId": "3d5e5e6e-b114-43f1-c917-c0ce36b8bb87"
      },
      "source": [
        "model=Sequential()\n",
        "model.add(Conv2D(filters=16,kernel_size=3,activation='relu',input_shape=(32, 32, 3)))\n",
        "model.add(MaxPool2D(pool_size=2))\n",
        "model.add(Conv2D(filters=16,kernel_size=3,activation='relu'))\n",
        "model.add(MaxPool2D(pool_size=2))\n",
        "model.add(Conv2D(filters=16,kernel_size=3,activation='relu'))\n",
        "model.add(MaxPool2D(pool_size=2))\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(units=512,activation='relu'))\n",
        "model.add(Dense(units=512,activation='relu'))\n",
        "model.add(Dense(units=10,activation='softmax'))\n",
        "model.summary()\n"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_3 (Conv2D)            (None, 30, 30, 16)        448       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 15, 15, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 13, 13, 16)        2320      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 6, 6, 16)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 4, 4, 16)          2320      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 2, 2, 16)          0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 512)               33280     \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 10)                5130      \n",
            "=================================================================\n",
            "Total params: 306,154\n",
            "Trainable params: 306,154\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v9zFhOCxdOdH",
        "outputId": "9a4531b7-fc96-4374-d636-bac160809969"
      },
      "source": [
        "#Compile & Train the model\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"SGD\", metrics=[\"accuracy\"])\n",
        "history = model.fit(x_train, y_train, validation_data=(x_valid,y_valid),\n",
        "                    batch_size=200, \n",
        "                    epochs=5,  \n",
        "                    verbose=1)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "225/225 [==============================] - 3s 9ms/step - loss: 2.2994 - accuracy: 0.1034 - val_loss: 2.2956 - val_accuracy: 0.1144\n",
            "Epoch 2/5\n",
            "225/225 [==============================] - 2s 8ms/step - loss: 2.2887 - accuracy: 0.1367 - val_loss: 2.2790 - val_accuracy: 0.1580\n",
            "Epoch 3/5\n",
            "225/225 [==============================] - 2s 9ms/step - loss: 2.2549 - accuracy: 0.1656 - val_loss: 2.2157 - val_accuracy: 0.1970\n",
            "Epoch 4/5\n",
            "225/225 [==============================] - 2s 8ms/step - loss: 2.1526 - accuracy: 0.2106 - val_loss: 2.0739 - val_accuracy: 0.2378\n",
            "Epoch 5/5\n",
            "225/225 [==============================] - 2s 8ms/step - loss: 2.0690 - accuracy: 0.2330 - val_loss: 2.0453 - val_accuracy: 0.2390\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "JK24Pkk2ebuq",
        "outputId": "3df6906d-e436-4bb7-b923-89aa0bc20e7c"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "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": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Loss Curves')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "aTR4tGoHeg_3",
        "outputId": "64eb6706-cc02-495c-88ab-e97a9dd9151f"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "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": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy Curves')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iKi7ba9QIQ1Y"
      },
      "source": [
        "## Build a CNN Architecture with Tensorflow"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4B_E1oGjCORZ"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aPCcsEepIQ1Y",
        "outputId": "12a6e1a2-5467-4feb-ddb3-75a067937fd2"
      },
      "source": [
        "del model\n",
        "model= tf.keras.Sequential()\n",
        "\n",
        "model.add(tf.keras.layers.Conv2D(filters=16,kernel_size=3,activation='relu',input_shape=(32,32,3)))\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=2))\n",
        "\n",
        "model.add(tf.keras.layers.Conv2D(filters=16,kernel_size=3,activation='relu'))\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=2))\n",
        "\n",
        "model.add(tf.keras.layers.Conv2D(filters=16,kernel_size=3,activation='relu'))\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=2))\n",
        "\n",
        "model.add(tf.keras.layers.Flatten())\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model.compile(optimizer='adam',loss=\"categorical_crossentropy\",metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_6 (Conv2D)            (None, 30, 30, 16)        448       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 15, 15, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 13, 13, 16)        2320      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 6, 6, 16)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 4, 4, 16)          2320      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 2, 2, 16)          0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 5,738\n",
            "Trainable params: 5,738\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qIuB3NXgC4-M",
        "outputId": "f18de48c-66ce-47bf-9e9e-7bcc82b023cf"
      },
      "source": [
        "x_train.shape,y_train.shape"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((45000, 32, 32, 3), (45000, 10))"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k8Uz0oGZBzKM",
        "outputId": "75279b13-05fb-4439-ab43-263034e87f9a"
      },
      "source": [
        "answers=model.fit(x_train,y_train,\n",
        "                  validation_data=(x_valid,y_valid),\n",
        "                  shuffle=True,epochs=2,verbose=2,batch_size=200)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "225/225 - 2s - loss: 1.9710 - accuracy: 0.2752 - val_loss: 1.7185 - val_accuracy: 0.3774\n",
            "Epoch 2/2\n",
            "225/225 - 1s - loss: 1.6511 - accuracy: 0.3930 - val_loss: 1.5943 - val_accuracy: 0.4230\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LOzbnSYIIQ1h"
      },
      "source": [
        "### Stack an ANN on top of CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5xx53QCzEgGb",
        "outputId": "ccdb7d60-3a2f-4d5b-f4c2-17ff3ec3cdc7"
      },
      "source": [
        "model= tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.Conv2D(filters=16,kernel_size=3,activation='relu',input_shape=(32,32,3)))\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=2))\n",
        "\n",
        "model.add(tf.keras.layers.Conv2D(filters=16,kernel_size=3,activation='relu'))\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=2))\n",
        "\n",
        "model.add(tf.keras.layers.Conv2D(filters=16,kernel_size=3,activation='relu'))\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=2))\n",
        "\n",
        "model.add(tf.keras.layers.Flatten())\n",
        "model.summary()\n",
        "\n",
        "### Stack an ANN on top of CNN\n",
        "\n",
        "model.add(tf.keras.layers.Dense(units=512,activation='relu',kernel_initializer='uniform'))\n",
        "model.add(tf.keras.layers.Dense(units=512,activation='relu',kernel_initializer='uniform'))\n",
        "model.add(tf.keras.layers.Dense(units=512,activation='relu',kernel_initializer='uniform'))\n",
        "\n",
        "model.add(tf.keras.layers.Dense(units=512,activation='relu',kernel_initializer='uniform'))\n",
        "model.add(tf.keras.layers.Dense(units=512,activation='relu',kernel_initializer='uniform'))\n",
        "model.add(tf.keras.layers.Dense(units=512,activation='relu',kernel_initializer='uniform'))\n",
        "\n",
        "model.add(tf.keras.layers.Dense(units=10,activation='softmax',kernel_initializer='uniform'))\n",
        "\n",
        "model.compile(optimizer='adam',loss=\"categorical_crossentropy\",metrics=['accuracy'])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_9 (Conv2D)            (None, 30, 30, 16)        448       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 15, 15, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_10 (Conv2D)           (None, 13, 13, 16)        2320      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 6, 6, 16)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_11 (Conv2D)           (None, 4, 4, 16)          2320      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2 (None, 2, 2, 16)          0         \n",
            "_________________________________________________________________\n",
            "flatten_3 (Flatten)          (None, 64)                0         \n",
            "=================================================================\n",
            "Total params: 5,088\n",
            "Trainable params: 5,088\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_9 (Conv2D)            (None, 30, 30, 16)        448       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 15, 15, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_10 (Conv2D)           (None, 13, 13, 16)        2320      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 6, 6, 16)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_11 (Conv2D)           (None, 4, 4, 16)          2320      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2 (None, 2, 2, 16)          0         \n",
            "_________________________________________________________________\n",
            "flatten_3 (Flatten)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 512)               33280     \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 10)                5130      \n",
            "=================================================================\n",
            "Total params: 1,356,778\n",
            "Trainable params: 1,356,778\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2imNDJGJIQ1i"
      },
      "source": [
        "### Fit the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_jXI9_aPIQ1i",
        "outputId": "d650f60d-9972-46e7-995e-df9d9fe86014"
      },
      "source": [
        "answers=model.fit(x_train,y_train,validation_data=(x_valid,y_valid),shuffle=True,epochs=2,verbose=2,batch_size=500)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "90/90 - 3s - loss: 2.0820 - accuracy: 0.1594 - val_loss: 1.9494 - val_accuracy: 0.1948\n",
            "Epoch 2/2\n",
            "90/90 - 1s - loss: 1.9056 - accuracy: 0.2185 - val_loss: 1.8394 - val_accuracy: 0.2794\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iiRWlQqDIQ1j"
      },
      "source": [
        "### Evaluate the Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LhuJ29CiIQ1j",
        "outputId": "d74b8c7c-e069-4553-9f54-372fe7ad058d"
      },
      "source": [
        "results=model.evaluate(x_test,y_test)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 2s 4ms/step - loss: 1.8304 - accuracy: 0.2819\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mcqb_EtKIQ1j",
        "outputId": "febeb8e6-0560-4a8e-92a4-c59707f33e1f"
      },
      "source": [
        "answers.history.keys()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "_rVwanJ-IQ1k",
        "outputId": "542c3a43-9a49-476f-8e8b-12afab5abbca"
      },
      "source": [
        "plt.plot(answers.history['val_loss'],label='Validation Loss');\n",
        "plt.plot(answers.history['loss'],label='Training Loss');\n",
        "plt.legend();"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "3aNT7bD2IQ1k",
        "outputId": "e30473a1-b074-45d8-8ffe-9d74bb904528"
      },
      "source": [
        "plt.plot(answers.history['val_accuracy'],label='Validation Accuracy');\n",
        "plt.plot(answers.history['accuracy'],label='Training Accuracy');\n",
        "plt.legend;"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BSMxpL1hg2f_"
      },
      "source": [
        "# Approch -3 CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0QS6JJGg55I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b940be9a-3692-447a-cbdc-9b18a791f7b7"
      },
      "source": [
        "from keras.layers import Conv2D, Flatten\n",
        "simple_cnn_model = Sequential()\n",
        "simple_cnn_model.add(Conv2D(32, (3,3), input_shape=(32,32,3), activation='relu'))\n",
        "simple_cnn_model.add(Conv2D(32, (3,3), activation='relu'))\n",
        "simple_cnn_model.add(Conv2D(32, (3,3), activation='relu'))\n",
        "simple_cnn_model.add(Flatten())\n",
        "simple_cnn_model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "simple_cnn_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "simple_cnn_model_history = simple_cnn_model.fit(x_train,y_train,validation_data=(x_valid,y_valid), batch_size=100, epochs=8)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/8\n",
            "450/450 [==============================] - 7s 14ms/step - loss: 1.5023 - accuracy: 0.4633 - val_loss: 1.2331 - val_accuracy: 0.5672\n",
            "Epoch 2/8\n",
            "450/450 [==============================] - 6s 14ms/step - loss: 1.1408 - accuracy: 0.5996 - val_loss: 1.0570 - val_accuracy: 0.6294\n",
            "Epoch 3/8\n",
            "450/450 [==============================] - 6s 14ms/step - loss: 0.9779 - accuracy: 0.6612 - val_loss: 1.0220 - val_accuracy: 0.6442\n",
            "Epoch 4/8\n",
            "450/450 [==============================] - 6s 14ms/step - loss: 0.8702 - accuracy: 0.6992 - val_loss: 0.9797 - val_accuracy: 0.6604\n",
            "Epoch 5/8\n",
            "450/450 [==============================] - 6s 14ms/step - loss: 0.7860 - accuracy: 0.7278 - val_loss: 0.9733 - val_accuracy: 0.6706\n",
            "Epoch 6/8\n",
            "450/450 [==============================] - 6s 14ms/step - loss: 0.6986 - accuracy: 0.7605 - val_loss: 0.9942 - val_accuracy: 0.6710\n",
            "Epoch 7/8\n",
            "450/450 [==============================] - 6s 14ms/step - loss: 0.6206 - accuracy: 0.7856 - val_loss: 1.0672 - val_accuracy: 0.6494\n",
            "Epoch 8/8\n",
            "450/450 [==============================] - 6s 14ms/step - loss: 0.5388 - accuracy: 0.8139 - val_loss: 1.1274 - val_accuracy: 0.6510\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DQYO6gFHiPBx"
      },
      "source": [
        "# CNN - Experiment - 4"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lr54kkR0hkjQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa4490f6-1ada-4edf-c24c-88c9d999750b"
      },
      "source": [
        "from keras.layers import Conv2D, Flatten\n",
        "simple_cnn_model = Sequential()\n",
        "simple_cnn_model.add(Conv2D(32, (3,3), input_shape=(32,32,3), activation='relu'))\n",
        "simple_cnn_model.add(MaxPool2D(pool_size=2))\n",
        "simple_cnn_model.add(Conv2D(32, (3,3), activation='relu'))\n",
        "simple_cnn_model.add(MaxPool2D(pool_size=2))\n",
        "simple_cnn_model.add(Conv2D(32, (3,3), activation='relu'))\n",
        "simple_cnn_model.add(Flatten())\n",
        "simple_cnn_model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "simple_cnn_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "simple_cnn_model_history = simple_cnn_model.fit(x_train,y_train,validation_data=(x_valid,y_valid), batch_size=100, epochs=8)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/8\n",
            "450/450 [==============================] - 4s 9ms/step - loss: 1.7455 - accuracy: 0.3622 - val_loss: 1.4833 - val_accuracy: 0.4572\n",
            "Epoch 2/8\n",
            "450/450 [==============================] - 4s 9ms/step - loss: 1.4485 - accuracy: 0.4811 - val_loss: 1.4065 - val_accuracy: 0.4882\n",
            "Epoch 3/8\n",
            "450/450 [==============================] - 4s 9ms/step - loss: 1.3349 - accuracy: 0.5229 - val_loss: 1.2985 - val_accuracy: 0.5306\n",
            "Epoch 4/8\n",
            "450/450 [==============================] - 4s 10ms/step - loss: 1.2605 - accuracy: 0.5513 - val_loss: 1.2250 - val_accuracy: 0.5748\n",
            "Epoch 5/8\n",
            "450/450 [==============================] - 4s 10ms/step - loss: 1.2017 - accuracy: 0.5741 - val_loss: 1.2015 - val_accuracy: 0.5844\n",
            "Epoch 6/8\n",
            "450/450 [==============================] - 4s 10ms/step - loss: 1.1475 - accuracy: 0.5937 - val_loss: 1.1151 - val_accuracy: 0.6134\n",
            "Epoch 7/8\n",
            "450/450 [==============================] - 4s 9ms/step - loss: 1.1027 - accuracy: 0.6116 - val_loss: 1.1054 - val_accuracy: 0.6132\n",
            "Epoch 8/8\n",
            "450/450 [==============================] - 4s 10ms/step - loss: 1.0636 - accuracy: 0.6285 - val_loss: 1.0498 - val_accuracy: 0.6306\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DUY2Pfq2ExAx"
      },
      "source": [
        "# Alternative Approach\n",
        "https://www.kaggle.com/aayushkubba/cifar-10-analysis-with-a-neural-network"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JjKiDfx1RHaW"
      },
      "source": [
        "## Data Augmentation \n",
        "To get a more generalised model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sH_vWoqKRHJv"
      },
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNOTqkxnT5Nz"
      },
      "source": [
        "datagen = ImageDataGenerator(\n",
        "    rotation_range=20,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    horizontal_flip=True, vertical_flip=True,\n",
        "    validation_split=0.2)"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y9OonWCnUmhg",
        "outputId": "c5c59dd8-dfa9-4cda-a47c-eb34aa6d4460"
      },
      "source": [
        "type(datagen)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "keras.preprocessing.image.ImageDataGenerator"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pX0qaJYMT8h-"
      },
      "source": [
        "from keras.layers import Conv2D, Flatten\n",
        "augmented_simple_cnn_model = Sequential()\n",
        "augmented_simple_cnn_model.add(Conv2D(32, (3,3), input_shape=(32,32,3), activation='relu'))\n",
        "augmented_simple_cnn_model.add(MaxPool2D(pool_size=2))\n",
        "augmented_simple_cnn_model.add(Conv2D(32, (3,3), activation='relu'))\n",
        "augmented_simple_cnn_model.add(MaxPool2D(pool_size=2))\n",
        "augmented_simple_cnn_model.add(Conv2D(32, (3,3), activation='relu'))\n",
        "augmented_simple_cnn_model.add(Flatten())\n",
        "augmented_simple_cnn_model.add(Dense(10, activation='softmax'))\n"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QRyeNo5kT8YR",
        "outputId": "aa366bfc-6e5a-4229-8687-0cf33e6b90ae"
      },
      "source": [
        "augmented_simple_cnn_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "augmented_simple_cnn_model.summary()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_18 (Conv2D)           (None, 30, 30, 32)        896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_11 (MaxPooling (None, 15, 15, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_19 (Conv2D)           (None, 13, 13, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_12 (MaxPooling (None, 6, 6, 32)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_20 (Conv2D)           (None, 4, 4, 32)          9248      \n",
            "_________________________________________________________________\n",
            "flatten_6 (Flatten)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 10)                5130      \n",
            "=================================================================\n",
            "Total params: 24,522\n",
            "Trainable params: 24,522\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1t9_xnOVMNe"
      },
      "source": [
        ""
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TrpgpQuPUcj1",
        "outputId": "9549623c-d87a-4d20-f2cf-7c0a2a2712dc"
      },
      "source": [
        "augmented_simple_cnn_model_history=augmented_simple_cnn_model.fit_generator(datagen.flow(x_train,y_train,batch_size=batch_size),\n",
        "                                                                            validation_data=(x_valid,y_valid),\n",
        "                                                                            epochs=8)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1972: 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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/8\n",
            "176/176 [==============================] - 27s 148ms/step - loss: 2.0438 - accuracy: 0.2330 - val_loss: 1.8279 - val_accuracy: 0.3168\n",
            "Epoch 2/8\n",
            "176/176 [==============================] - 25s 143ms/step - loss: 1.8194 - accuracy: 0.3187 - val_loss: 1.7258 - val_accuracy: 0.3432\n",
            "Epoch 3/8\n",
            "176/176 [==============================] - 25s 142ms/step - loss: 1.7486 - accuracy: 0.3501 - val_loss: 1.6791 - val_accuracy: 0.3800\n",
            "Epoch 4/8\n",
            "176/176 [==============================] - 25s 141ms/step - loss: 1.7058 - accuracy: 0.3668 - val_loss: 1.6154 - val_accuracy: 0.4102\n",
            "Epoch 5/8\n",
            "176/176 [==============================] - 25s 142ms/step - loss: 1.6677 - accuracy: 0.3815 - val_loss: 1.6091 - val_accuracy: 0.4122\n",
            "Epoch 6/8\n",
            "176/176 [==============================] - 25s 141ms/step - loss: 1.6423 - accuracy: 0.3947 - val_loss: 1.5375 - val_accuracy: 0.4448\n",
            "Epoch 7/8\n",
            "176/176 [==============================] - 25s 141ms/step - loss: 1.6261 - accuracy: 0.4043 - val_loss: 1.5533 - val_accuracy: 0.4306\n",
            "Epoch 8/8\n",
            "176/176 [==============================] - 25s 141ms/step - loss: 1.5920 - accuracy: 0.4190 - val_loss: 1.5769 - val_accuracy: 0.4268\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K9Fx0rvdf1vy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "85131f9f-d587-434a-9ce1-4e329b5aa628"
      },
      "source": [
        "plt.plot(augmented_simple_cnn_model_history.history['val_loss'],label='Validation Loss');\n",
        "plt.plot(augmented_simple_cnn_model_history.history['loss'],label='Training Loss');\n",
        "plt.legend();"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "xt6eM7lTWNtm",
        "outputId": "ace4d883-9af8-4771-a302-d851afdff27d"
      },
      "source": [
        "plt.plot(augmented_simple_cnn_model_history.history['val_accuracy'],label='Validation Accuracy');\n",
        "plt.plot(augmented_simple_cnn_model_history.history['accuracy'],label='Training Accuracy');\n",
        "plt.legend;"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d9Q5v7QVW0Mc"
      },
      "source": [
        "# Transfer Learning\n",
        "\n",
        "## RESNET"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pDGxeAO1WSkP"
      },
      "source": [
        "from keras.models import Model, load_model\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
        "from keras.layers import GlobalAveragePooling2D, Dropout"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ofb6UpmMXVaW"
      },
      "source": [
        "# RESNET as the base model\n",
        "base_model=tf.keras.applications.ResNet50(weights='imagenet',include_top=False)\n",
        "\n",
        "# We will only use resnet for feature extarction, freeze all the layers that we dont need\n",
        "for layer in base_model.layers:\n",
        "  layer.trainable=False\n",
        "\n",
        "# Take the output output of the base layers\n",
        "base_model_output=base_model.output\n",
        "\n",
        "# Add our layers\n",
        "## Pooling\n",
        "x=GlobalAveragePooling2D()(base_model_output)\n",
        "## FC\n",
        "x=Dense(2056,activation='relu')(x)\n",
        "## Dropout\n",
        "x=Dropout(0.5)(x)\n",
        "## Output layer\n",
        "x=Dense(10,activation='softmax')(x)"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MWd7ZdXLYf0z"
      },
      "source": [
        "resnet_model=Model(inputs=base_model.input,outputs=x)"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ymI24TLPZqFk",
        "outputId": "e2f7eedb-ac77-47db-b185-35126ec64ce5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "resnet_model.summary()"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_6 (InputLayer)            [(None, None, None,  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1_pad (ZeroPadding2D)       (None, None, None, 3 0           input_6[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv1_conv (Conv2D)             (None, None, None, 6 9472        conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv1_bn (BatchNormalization)   (None, None, None, 6 256         conv1_conv[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1_relu (Activation)         (None, None, None, 6 0           conv1_bn[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pad (ZeroPadding2D)       (None, None, None, 6 0           conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pool (MaxPooling2D)       (None, None, None, 6 0           pool1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_conv (Conv2D)    (None, None, None, 6 4160        pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_bn (BatchNormali (None, None, None, 6 256         conv2_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_relu (Activation (None, None, None, 6 0           conv2_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_conv (Conv2D)    (None, None, None, 6 36928       conv2_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_bn (BatchNormali (None, None, None, 6 256         conv2_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_relu (Activation (None, None, None, 6 0           conv2_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_conv (Conv2D)    (None, None, None, 2 16640       pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_conv (Conv2D)    (None, None, None, 2 16640       conv2_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_bn (BatchNormali (None, None, None, 2 1024        conv2_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_bn (BatchNormali (None, None, None, 2 1024        conv2_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_add (Add)          (None, None, None, 2 0           conv2_block1_0_bn[0][0]          \n",
            "                                                                 conv2_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_out (Activation)   (None, None, None, 2 0           conv2_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_conv (Conv2D)    (None, None, None, 6 16448       conv2_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_bn (BatchNormali (None, None, None, 6 256         conv2_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_relu (Activation (None, None, None, 6 0           conv2_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_conv (Conv2D)    (None, None, None, 6 36928       conv2_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_bn (BatchNormali (None, None, None, 6 256         conv2_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_relu (Activation (None, None, None, 6 0           conv2_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_conv (Conv2D)    (None, None, None, 2 16640       conv2_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_bn (BatchNormali (None, None, None, 2 1024        conv2_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_add (Add)          (None, None, None, 2 0           conv2_block1_out[0][0]           \n",
            "                                                                 conv2_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_out (Activation)   (None, None, None, 2 0           conv2_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_conv (Conv2D)    (None, None, None, 6 16448       conv2_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_bn (BatchNormali (None, None, None, 6 256         conv2_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_relu (Activation (None, None, None, 6 0           conv2_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_conv (Conv2D)    (None, None, None, 6 36928       conv2_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_bn (BatchNormali (None, None, None, 6 256         conv2_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_relu (Activation (None, None, None, 6 0           conv2_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_conv (Conv2D)    (None, None, None, 2 16640       conv2_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_bn (BatchNormali (None, None, None, 2 1024        conv2_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_add (Add)          (None, None, None, 2 0           conv2_block2_out[0][0]           \n",
            "                                                                 conv2_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_out (Activation)   (None, None, None, 2 0           conv2_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_conv (Conv2D)    (None, None, None, 1 32896       conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_bn (BatchNormali (None, None, None, 1 512         conv3_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_relu (Activation (None, None, None, 1 0           conv3_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_conv (Conv2D)    (None, None, None, 1 147584      conv3_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_bn (BatchNormali (None, None, None, 1 512         conv3_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_relu (Activation (None, None, None, 1 0           conv3_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_conv (Conv2D)    (None, None, None, 5 131584      conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_conv (Conv2D)    (None, None, None, 5 66048       conv3_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_bn (BatchNormali (None, None, None, 5 2048        conv3_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_bn (BatchNormali (None, None, None, 5 2048        conv3_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_add (Add)          (None, None, None, 5 0           conv3_block1_0_bn[0][0]          \n",
            "                                                                 conv3_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_out (Activation)   (None, None, None, 5 0           conv3_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_conv (Conv2D)    (None, None, None, 1 65664       conv3_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_bn (BatchNormali (None, None, None, 1 512         conv3_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_relu (Activation (None, None, None, 1 0           conv3_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_conv (Conv2D)    (None, None, None, 1 147584      conv3_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_bn (BatchNormali (None, None, None, 1 512         conv3_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_relu (Activation (None, None, None, 1 0           conv3_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_conv (Conv2D)    (None, None, None, 5 66048       conv3_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_bn (BatchNormali (None, None, None, 5 2048        conv3_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_add (Add)          (None, None, None, 5 0           conv3_block1_out[0][0]           \n",
            "                                                                 conv3_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_out (Activation)   (None, None, None, 5 0           conv3_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_conv (Conv2D)    (None, None, None, 1 65664       conv3_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_bn (BatchNormali (None, None, None, 1 512         conv3_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_relu (Activation (None, None, None, 1 0           conv3_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_conv (Conv2D)    (None, None, None, 1 147584      conv3_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_bn (BatchNormali (None, None, None, 1 512         conv3_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_relu (Activation (None, None, None, 1 0           conv3_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_conv (Conv2D)    (None, None, None, 5 66048       conv3_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_bn (BatchNormali (None, None, None, 5 2048        conv3_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_add (Add)          (None, None, None, 5 0           conv3_block2_out[0][0]           \n",
            "                                                                 conv3_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_out (Activation)   (None, None, None, 5 0           conv3_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_conv (Conv2D)    (None, None, None, 1 65664       conv3_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_bn (BatchNormali (None, None, None, 1 512         conv3_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_relu (Activation (None, None, None, 1 0           conv3_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_conv (Conv2D)    (None, None, None, 1 147584      conv3_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_bn (BatchNormali (None, None, None, 1 512         conv3_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_relu (Activation (None, None, None, 1 0           conv3_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_conv (Conv2D)    (None, None, None, 5 66048       conv3_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_bn (BatchNormali (None, None, None, 5 2048        conv3_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_add (Add)          (None, None, None, 5 0           conv3_block3_out[0][0]           \n",
            "                                                                 conv3_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_out (Activation)   (None, None, None, 5 0           conv3_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_conv (Conv2D)    (None, None, None, 2 131328      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_relu (Activation (None, None, None, 2 0           conv4_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_relu (Activation (None, None, None, 2 0           conv4_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_conv (Conv2D)    (None, None, None, 1 525312      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_bn (BatchNormali (None, None, None, 1 4096        conv4_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_add (Add)          (None, None, None, 1 0           conv4_block1_0_bn[0][0]          \n",
            "                                                                 conv4_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_out (Activation)   (None, None, None, 1 0           conv4_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_relu (Activation (None, None, None, 2 0           conv4_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_relu (Activation (None, None, None, 2 0           conv4_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_add (Add)          (None, None, None, 1 0           conv4_block1_out[0][0]           \n",
            "                                                                 conv4_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_out (Activation)   (None, None, None, 1 0           conv4_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_relu (Activation (None, None, None, 2 0           conv4_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_relu (Activation (None, None, None, 2 0           conv4_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_add (Add)          (None, None, None, 1 0           conv4_block2_out[0][0]           \n",
            "                                                                 conv4_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_out (Activation)   (None, None, None, 1 0           conv4_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_relu (Activation (None, None, None, 2 0           conv4_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_relu (Activation (None, None, None, 2 0           conv4_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_add (Add)          (None, None, None, 1 0           conv4_block3_out[0][0]           \n",
            "                                                                 conv4_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_out (Activation)   (None, None, None, 1 0           conv4_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block5_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_relu (Activation (None, None, None, 2 0           conv4_block5_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block5_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block5_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_relu (Activation (None, None, None, 2 0           conv4_block5_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block5_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block5_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_add (Add)          (None, None, None, 1 0           conv4_block4_out[0][0]           \n",
            "                                                                 conv4_block5_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_out (Activation)   (None, None, None, 1 0           conv4_block5_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block5_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block6_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_relu (Activation (None, None, None, 2 0           conv4_block6_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block6_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block6_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_relu (Activation (None, None, None, 2 0           conv4_block6_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block6_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block6_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_add (Add)          (None, None, None, 1 0           conv4_block5_out[0][0]           \n",
            "                                                                 conv4_block6_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_out (Activation)   (None, None, None, 1 0           conv4_block6_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_conv (Conv2D)    (None, None, None, 5 524800      conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_bn (BatchNormali (None, None, None, 5 2048        conv5_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_relu (Activation (None, None, None, 5 0           conv5_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_conv (Conv2D)    (None, None, None, 5 2359808     conv5_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_bn (BatchNormali (None, None, None, 5 2048        conv5_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_relu (Activation (None, None, None, 5 0           conv5_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_conv (Conv2D)    (None, None, None, 2 2099200     conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_conv (Conv2D)    (None, None, None, 2 1050624     conv5_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_bn (BatchNormali (None, None, None, 2 8192        conv5_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_bn (BatchNormali (None, None, None, 2 8192        conv5_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_add (Add)          (None, None, None, 2 0           conv5_block1_0_bn[0][0]          \n",
            "                                                                 conv5_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_out (Activation)   (None, None, None, 2 0           conv5_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_conv (Conv2D)    (None, None, None, 5 1049088     conv5_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_bn (BatchNormali (None, None, None, 5 2048        conv5_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_relu (Activation (None, None, None, 5 0           conv5_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_conv (Conv2D)    (None, None, None, 5 2359808     conv5_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_bn (BatchNormali (None, None, None, 5 2048        conv5_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_relu (Activation (None, None, None, 5 0           conv5_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_conv (Conv2D)    (None, None, None, 2 1050624     conv5_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_bn (BatchNormali (None, None, None, 2 8192        conv5_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_add (Add)          (None, None, None, 2 0           conv5_block1_out[0][0]           \n",
            "                                                                 conv5_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_out (Activation)   (None, None, None, 2 0           conv5_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_conv (Conv2D)    (None, None, None, 5 1049088     conv5_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_bn (BatchNormali (None, None, None, 5 2048        conv5_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_relu (Activation (None, None, None, 5 0           conv5_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_conv (Conv2D)    (None, None, None, 5 2359808     conv5_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_bn (BatchNormali (None, None, None, 5 2048        conv5_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_relu (Activation (None, None, None, 5 0           conv5_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_conv (Conv2D)    (None, None, None, 2 1050624     conv5_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_bn (BatchNormali (None, None, None, 2 8192        conv5_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_add (Add)          (None, None, None, 2 0           conv5_block2_out[0][0]           \n",
            "                                                                 conv5_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_out (Activation)   (None, None, None, 2 0           conv5_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_4 (Glo (None, 2048)         0           conv5_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "dense_24 (Dense)                (None, 2056)         4212744     global_average_pooling2d_4[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "dropout_2 (Dropout)             (None, 2056)         0           dense_24[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_25 (Dense)                (None, 10)           20570       dropout_2[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 27,821,026\n",
            "Trainable params: 4,233,314\n",
            "Non-trainable params: 23,587,712\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XlTnOU_BaXUF"
      },
      "source": [
        "resnet_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oWrUEnk4bAF9"
      },
      "source": [
        "For reference\n",
        "\n",
        "Without freezing\n",
        "\n",
        "Epoch 1/8\n",
        "176/176 [==============================] - 50s 238ms/step - loss: 1.4530 - accuracy: 0.5499 - val_loss: 4.2816 - val_accuracy: 0.1038\n",
        "Epoch 2/8"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wiE8NFZ-ZvJd",
        "outputId": "633b8e94-8dbb-459b-8507-062420a20d82",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "resnet_model_history=resnet_model.fit(x_train,y_train,batch_size=batch_size,\n",
        "                                      validation_data=(x_valid,y_valid),epochs=8,shuffle=True)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/8\n",
            "176/176 [==============================] - 18s 83ms/step - loss: 2.3227 - accuracy: 0.1984 - val_loss: 2.0440 - val_accuracy: 0.2604\n",
            "Epoch 2/8\n",
            "176/176 [==============================] - 13s 75ms/step - loss: 2.0166 - accuracy: 0.2598 - val_loss: 1.9430 - val_accuracy: 0.2866\n",
            "Epoch 3/8\n",
            "176/176 [==============================] - 13s 75ms/step - loss: 1.9657 - accuracy: 0.2806 - val_loss: 1.9092 - val_accuracy: 0.3002\n",
            "Epoch 4/8\n",
            "176/176 [==============================] - 13s 74ms/step - loss: 1.9390 - accuracy: 0.2922 - val_loss: 1.8715 - val_accuracy: 0.3220\n",
            "Epoch 5/8\n",
            "176/176 [==============================] - 13s 75ms/step - loss: 1.9224 - accuracy: 0.2981 - val_loss: 1.8699 - val_accuracy: 0.3274\n",
            "Epoch 6/8\n",
            "176/176 [==============================] - 13s 75ms/step - loss: 1.9192 - accuracy: 0.3010 - val_loss: 1.8423 - val_accuracy: 0.3422\n",
            "Epoch 7/8\n",
            "176/176 [==============================] - 13s 75ms/step - loss: 1.9107 - accuracy: 0.3021 - val_loss: 1.8356 - val_accuracy: 0.3516\n",
            "Epoch 8/8\n",
            "176/176 [==============================] - 13s 75ms/step - loss: 1.9025 - accuracy: 0.3063 - val_loss: 1.8439 - val_accuracy: 0.3388\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oknJhnLbaRxC"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}