TF2_0_CIFAR_Improved.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TF2.0 CIFAR Improved.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyO73YtkEc1F2gYL7OA2erY1",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/melvin-02/Colab-CNN/blob/master/TF2_0_CIFAR_Improved.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ECQ80IUN4mrP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.layers import Input, Conv2D, Dense, Flatten, Dropout, GlobalMaxPooling2D, MaxPooling2D, BatchNormalization\n",
        "from tensorflow.keras.models import Model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KwWHREU57JkP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cifar10 = tf.keras.datasets.cifar10\n",
        "\n",
        "(X_train, y_train), (X_test, y_test) = cifar10.load_data()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tSDdvezu7gcM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train , X_test = X_train/255.0, X_test/255.0\n",
        "y_train, y_test = y_train.flatten(), y_test.flatten()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y3V84KmU72g3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "b7aba2f5-7645-456d-b7f3-90572c115c0b"
      },
      "source": [
        "print(X_train.shape)\n",
        "print(y_train.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(50000, 32, 32, 3)\n",
            "(50000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wIKsTGZq75pa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "34b6f5f1-50b9-4074-9426-05fd1974f9b2"
      },
      "source": [
        "K = len(set(y_train))\n",
        "K"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kl-VnTUx9ow_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "i = Input(shape=X_train[0].shape)\n",
        "x = Conv2D(32, (3,3), padding='same', activation='relu')(i)\n",
        "x = BatchNormalization()(x)\n",
        "x = Conv2D(32, (3,3), padding='same', activation='relu')(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = MaxPooling2D((2, 2))(x)\n",
        "\n",
        "x = Conv2D(64, (3,3), padding='same', activation='relu')(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = Conv2D(64, (3,3), padding='same', activation='relu')(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = MaxPooling2D((2, 2))(x)\n",
        "\n",
        "x = Conv2D(128, (3,3), padding='same', activation='relu')(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = Conv2D(128, (3,3), padding='same', activation='relu')(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = MaxPooling2D((2, 2))(x)\n",
        "\n",
        "x = Flatten()(x)\n",
        "x = Dropout(0.2)(x)\n",
        "x = Dense(1024, activation='relu')(x)\n",
        "x = Dropout(0.2)(x)\n",
        "x = Dense(K, activation='softmax')(x)\n",
        "\n",
        "model = Model(i, x)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZLLvhXqWAu9X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v-YwCgE0BgzW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# model.fit(X_train, y_train, epochs =50 , validation_data=(X_test, y_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UU-74ydABwGe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "70ebced7-44e8-4e2f-ee79-3570daf1b48f"
      },
      "source": [
        "batch_size = 32\n",
        "data_generator = tf.keras.preprocessing.image.ImageDataGenerator(width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True)\n",
        "train_generator = data_generator.flow(X_train, y_train, batch_size)\n",
        "steps_per_epoch = 1562\n",
        "r = model.fit_generator(train_generator, steps_per_epoch=steps_per_epoch, validation_data=(X_test, y_test), epochs=50)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-9-56398a1eddc7>:5: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.fit, which supports generators.\n",
            "Epoch 1/50\n",
            "1562/1562 [==============================] - 46s 29ms/step - loss: 1.3821 - accuracy: 0.5181 - val_loss: 1.0179 - val_accuracy: 0.6438\n",
            "Epoch 2/50\n",
            "1562/1562 [==============================] - 46s 29ms/step - loss: 0.9944 - accuracy: 0.6536 - val_loss: 0.9744 - val_accuracy: 0.6765\n",
            "Epoch 3/50\n",
            "1562/1562 [==============================] - 46s 29ms/step - loss: 0.8431 - accuracy: 0.7096 - val_loss: 0.7679 - val_accuracy: 0.7361\n",
            "Epoch 4/50\n",
            "1562/1562 [==============================] - 46s 29ms/step - loss: 0.7548 - accuracy: 0.7426 - val_loss: 0.7443 - val_accuracy: 0.7511\n",
            "Epoch 5/50\n",
            "1562/1562 [==============================] - 46s 29ms/step - loss: 0.6816 - accuracy: 0.7670 - val_loss: 0.6723 - val_accuracy: 0.7773\n",
            "Epoch 6/50\n",
            "1562/1562 [==============================] - 46s 29ms/step - loss: 0.6306 - accuracy: 0.7849 - val_loss: 0.5706 - val_accuracy: 0.8075\n",
            "Epoch 7/50\n",
            "1562/1562 [==============================] - 46s 29ms/step - loss: 0.5868 - accuracy: 0.7992 - val_loss: 0.7023 - val_accuracy: 0.7771\n",
            "Epoch 8/50\n",
            "1562/1562 [==============================] - 46s 29ms/step - loss: 0.5476 - accuracy: 0.8130 - val_loss: 0.5158 - val_accuracy: 0.8302\n",
            "Epoch 9/50\n",
            "1562/1562 [==============================] - 47s 30ms/step - loss: 0.5141 - accuracy: 0.8246 - val_loss: 0.5580 - val_accuracy: 0.8146\n",
            "Epoch 10/50\n",
            "1562/1562 [==============================] - 46s 29ms/step - loss: 0.4897 - accuracy: 0.8333 - val_loss: 0.5659 - val_accuracy: 0.8130\n",
            "Epoch 11/50\n",
            "1562/1562 [==============================] - 46s 30ms/step - loss: 0.4616 - accuracy: 0.8428 - val_loss: 0.5563 - val_accuracy: 0.8145\n",
            "Epoch 12/50\n",
            "1562/1562 [==============================] - 46s 30ms/step - loss: 0.4394 - accuracy: 0.8498 - val_loss: 0.4812 - val_accuracy: 0.8419\n",
            "Epoch 13/50\n",
            "1562/1562 [==============================] - 46s 30ms/step - loss: 0.4187 - accuracy: 0.8571 - val_loss: 0.4379 - val_accuracy: 0.8525\n",
            "Epoch 14/50\n",
            "1562/1562 [==============================] - 46s 30ms/step - loss: 0.4034 - accuracy: 0.8626 - val_loss: 0.4944 - val_accuracy: 0.8360\n",
            "Epoch 15/50\n",
            "1562/1562 [==============================] - 46s 30ms/step - loss: 0.3872 - accuracy: 0.8676 - val_loss: 0.5076 - val_accuracy: 0.8349\n",
            "Epoch 16/50\n",
            "1562/1562 [==============================] - 46s 30ms/step - loss: 0.3705 - accuracy: 0.8739 - val_loss: 0.5734 - val_accuracy: 0.8170\n",
            "Epoch 17/50\n",
            "1562/1562 [==============================] - 46s 30ms/step - loss: 0.3566 - accuracy: 0.8778 - val_loss: 0.4567 - val_accuracy: 0.8528\n",
            "Epoch 18/50\n",
            "1562/1562 [==============================] - 46s 30ms/step - loss: 0.3468 - accuracy: 0.8829 - val_loss: 0.4168 - val_accuracy: 0.8626\n",
            "Epoch 19/50\n",
            "1562/1562 [==============================] - 46s 30ms/step - loss: 0.3346 - accuracy: 0.8843 - val_loss: 0.4473 - val_accuracy: 0.8556\n",
            "Epoch 20/50\n",
            "1562/1562 [==============================] - 46s 30ms/step - loss: 0.3248 - accuracy: 0.8878 - val_loss: 0.4274 - val_accuracy: 0.8598\n",
            "Epoch 21/50\n",
            "1562/1562 [==============================] - 46s 30ms/step - loss: 0.3181 - accuracy: 0.8907 - val_loss: 0.4286 - val_accuracy: 0.8603\n",
            "Epoch 22/50\n",
            "1562/1562 [==============================] - 47s 30ms/step - loss: 0.3069 - accuracy: 0.8953 - val_loss: 0.4144 - val_accuracy: 0.8677\n",
            "Epoch 23/50\n",
            "1562/1562 [==============================] - 46s 30ms/step - loss: 0.3025 - accuracy: 0.8961 - val_loss: 0.4549 - val_accuracy: 0.8594\n",
            "Epoch 24/50\n",
            "1562/1562 [==============================] - 46s 30ms/step - loss: 0.2875 - accuracy: 0.9009 - val_loss: 0.4170 - val_accuracy: 0.8651\n",
            "Epoch 25/50\n",
            "1562/1562 [==============================] - 46s 30ms/step - loss: 0.2798 - accuracy: 0.9036 - val_loss: 0.4291 - val_accuracy: 0.8651\n",
            "Epoch 26/50\n",
            "1562/1562 [==============================] - 46s 30ms/step - loss: 0.2791 - accuracy: 0.9037 - val_loss: 0.3825 - val_accuracy: 0.8827\n",
            "Epoch 27/50\n",
            "1562/1562 [==============================] - 46s 29ms/step - loss: 0.2681 - accuracy: 0.9071 - val_loss: 0.4243 - val_accuracy: 0.8654\n",
            "Epoch 28/50\n",
            "1562/1562 [==============================] - 46s 30ms/step - loss: 0.2671 - accuracy: 0.9080 - val_loss: 0.4020 - val_accuracy: 0.8759\n",
            "Epoch 29/50\n",
            "1562/1562 [==============================] - 47s 30ms/step - loss: 0.2552 - accuracy: 0.9112 - val_loss: 0.3911 - val_accuracy: 0.8738\n",
            "Epoch 30/50\n",
            "1562/1562 [==============================] - 47s 30ms/step - loss: 0.2575 - accuracy: 0.9120 - val_loss: 0.4136 - val_accuracy: 0.8730\n",
            "Epoch 31/50\n",
            "1562/1562 [==============================] - 46s 30ms/step - loss: 0.2442 - accuracy: 0.9155 - val_loss: 0.4318 - val_accuracy: 0.8714\n",
            "Epoch 32/50\n",
            "1562/1562 [==============================] - 47s 30ms/step - loss: 0.2428 - accuracy: 0.9168 - val_loss: 0.4438 - val_accuracy: 0.8706\n",
            "Epoch 33/50\n",
            "1562/1562 [==============================] - 46s 30ms/step - loss: 0.2328 - accuracy: 0.9196 - val_loss: 0.4177 - val_accuracy: 0.8731\n",
            "Epoch 34/50\n",
            "1562/1562 [==============================] - 47s 30ms/step - loss: 0.2349 - accuracy: 0.9188 - val_loss: 0.4001 - val_accuracy: 0.8751\n",
            "Epoch 35/50\n",
            "1562/1562 [==============================] - 47s 30ms/step - loss: 0.2283 - accuracy: 0.9217 - val_loss: 0.3719 - val_accuracy: 0.8873\n",
            "Epoch 36/50\n",
            "1562/1562 [==============================] - 47s 30ms/step - loss: 0.2230 - accuracy: 0.9228 - val_loss: 0.3651 - val_accuracy: 0.8851\n",
            "Epoch 37/50\n",
            "1562/1562 [==============================] - 47s 30ms/step - loss: 0.2220 - accuracy: 0.9235 - val_loss: 0.3880 - val_accuracy: 0.8794\n",
            "Epoch 38/50\n",
            "1562/1562 [==============================] - 47s 30ms/step - loss: 0.2203 - accuracy: 0.9233 - val_loss: 0.4224 - val_accuracy: 0.8773\n",
            "Epoch 39/50\n",
            "1562/1562 [==============================] - 47s 30ms/step - loss: 0.2150 - accuracy: 0.9256 - val_loss: 0.3861 - val_accuracy: 0.8799\n",
            "Epoch 40/50\n",
            "1562/1562 [==============================] - 47s 30ms/step - loss: 0.2073 - accuracy: 0.9281 - val_loss: 0.4126 - val_accuracy: 0.8777\n",
            "Epoch 41/50\n",
            "1562/1562 [==============================] - 47s 30ms/step - loss: 0.2089 - accuracy: 0.9277 - val_loss: 0.4118 - val_accuracy: 0.8719\n",
            "Epoch 42/50\n",
            "1562/1562 [==============================] - 47s 30ms/step - loss: 0.2068 - accuracy: 0.9297 - val_loss: 0.4235 - val_accuracy: 0.8780\n",
            "Epoch 43/50\n",
            "1562/1562 [==============================] - 47s 30ms/step - loss: 0.2032 - accuracy: 0.9305 - val_loss: 0.4413 - val_accuracy: 0.8719\n",
            "Epoch 44/50\n",
            "1562/1562 [==============================] - 47s 30ms/step - loss: 0.1989 - accuracy: 0.9323 - val_loss: 0.4078 - val_accuracy: 0.8814\n",
            "Epoch 45/50\n",
            "1562/1562 [==============================] - 47s 30ms/step - loss: 0.1905 - accuracy: 0.9339 - val_loss: 0.4043 - val_accuracy: 0.8849\n",
            "Epoch 46/50\n",
            "1562/1562 [==============================] - 46s 30ms/step - loss: 0.1929 - accuracy: 0.9341 - val_loss: 0.4330 - val_accuracy: 0.8815\n",
            "Epoch 47/50\n",
            "1562/1562 [==============================] - 47s 30ms/step - loss: 0.1897 - accuracy: 0.9353 - val_loss: 0.4012 - val_accuracy: 0.8826\n",
            "Epoch 48/50\n",
            "1562/1562 [==============================] - 47s 30ms/step - loss: 0.1850 - accuracy: 0.9362 - val_loss: 0.3984 - val_accuracy: 0.8899\n",
            "Epoch 49/50\n",
            "1562/1562 [==============================] - 47s 30ms/step - loss: 0.1838 - accuracy: 0.9381 - val_loss: 0.4751 - val_accuracy: 0.8720\n",
            "Epoch 50/50\n",
            "1562/1562 [==============================] - 47s 30ms/step - loss: 0.1857 - accuracy: 0.9361 - val_loss: 0.4073 - val_accuracy: 0.8833\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BxFPvc9tLa16",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "4c4009dd-ae8a-499e-b162-fdc63c527d28"
      },
      "source": [
        "plt.plot(r.history['loss'], label='loss')\n",
        "plt.plot(r.history['val_loss'], label='val_loss')\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fed075fe048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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l1iqlJni+qC2QNABqSuHQHm44Mw2n1kxftsfXpRJCCK9oNNCVUlbgReAioB8wRSnV77jdHgNmaa2HANcA//F0QVskMcPc5q0nNTaMsX0SmPFTFtUOp2/LJYQQXtCUGvpwYLvWeqfWugZ4D5h03D4aiHL/Hg3s81wRWyEhHVCwfz0AN56VRmFZDfPW5fq2XEII4QVNCfTOwN4697Pd2+p6HJiqlMoG5gH31PdCSqlpSqlMpVRmQUFBC4rbTEHhENsD8kygn90zju7x4bz5ozS7CCECj6cuik4B3tRapwATgHeUUie8ttb6Fa31UK310Pj4eA8duhGJA44EusWiuOmsNNbsPcTqvYfa5vhCCNFGmhLoOUCXOvdT3NvquhWYBaC1XgqEAHGeKGCrJQ6Ag7uhygz9v+K0FCKCbbwlXRiFEAGmKYG+AuillOqmlArCXPScc9w+WcB5AEqpvphAb4M2lSZwLxpN/iYAIoJt/OL0FD5fu4+C0mofFkwIITyr0UDXWjuAu4EFwCZMb5YNSqknlFIT3bs9ANyulFoDzARu0qfKOHv3YhfkrTuy6YYzu1Lr1Mz8KctHhRJCCM+zNWUnrfU8zMXOutv+WOf3jcBIzxbNQ6JTIDwevn8eolOh9zi6x0dwTu94pi/fwx1jemC3Bvb4KiFE+xD4SaYUXDPT9HiZcRXMuhFK93PTWWnklVQzf/1+X5dQCCE8IvADHaDLMPjVEjj3MdjyBfx7OOeUfEa3jiG8tmSXzMIohAgI7SPQAWxBcM6DcMeP0Gkglnn3Myvoz5Rlr+fbLafG9VshhGiN9hPoh8X1hBs/g8teJq56LzNC/sb/frlWaulCCL/X/gIdTLv64Cmoq98mQRcxJO8jFmzI83WphBCiVdpnoB/WbRS6+7ncGzSHl7/8GadLaulCCP/VvgMdUGP/QLQuZXTRh3y+9tSYU0wIIVqi3Qc6Kaej+1zML+3zeH3hShxOl69LJIQQLSKBDqixjxFGJRcVz+Kjn4+fpkYIIfyDBDpAYj8YeBU3275k+sLl1DhaUEvPzoSaCs+XTQghmkgC3U2NeQS7cnJl+XvMytzb+BPqOrgHXj0ffnrFO4UTQogmkEA/rGN31Gk3cK3tG2Z//T1Vtc1Ypm7bl4CGnEyvFU8IIRojgV6HOudBLFYbU6veY/ryZszEuP0rc7tvjXcKJoQQTSCBXldUMpYzpnG5dQnzv1lEUVkT5kuvrYJdi8EeDsVZUF7o/XIKIUQ9JNCPN/I3aHs4tztm8MAHa3A1Nthozw9QWwHDbjH39632fhmFEKIeEujHC4/FeuadjLOsYMfW9by6ZOfJ99/+FViD4Uz3uti5P3u/jEIIUQ8J9PqcfiMaxe+SVvPM/C38nHWw4X23LYS0kRCZCLE9pYYuhPAZCfT6RKeguo1mgv6OxMhg7pn5M8WVtSfud3A3FG2DXuPM/eQhsE9q6EII35BAb8igKVgP7ea185zkFlfx6EfrTpxid9tCc9vzAnObPARKcqAsv23LKoQQSKA3rO+lYA8nff9n/HZcH+auy2XG8YtKb1sIHdIgtoe5nzzE3EqzixDCByTQGxIcAf0mwYZP+OWZnRjdO54nPtvI5v0l5vHD3RV7XmDmVwdIGggoaXYRQviEBPrJDJ4C1SVYts7juasHERVq567pqyivdpjuio7Ko+3nYD4E4npLoAshfEIC/WS6ng3RXWDNTOIigvnn5MHsKizn3pk/49q20HRXTDv72OckD4FcaXIRQrQ9CfSTsVhg4GTY8Q2U5HJWzzj+PLE/X2/O58DquSbMg8KOfU7yYCjNhZJc35RZCNFuNSnQlVLjlVJblFLblVIPN7DP1UqpjUqpDUqpGZ4tpg8NmgLaBetmAXD9mWk8MCyIuOoslttOO3H/wxdGpZYuhGhjjQa6UsoKvAhcBPQDpiil+h23Ty/gEWCk1ro/8GsvlNU34npCyjBYPRPc3RbvStkFwKPrkvhq43GLSydlgLJITxchRJtrSg19OLBda71Ta10DvAdMOm6f24EXtdYHAbTWgdURe9AUKNgEuWY2Rcv2r3B16EZ4pz7cM/Nn1ucUH903KBzi+siFUSFEm2tKoHcG6q74kO3eVldvoLdS6gel1DKl1Pj6XkgpNU0plamUyiwoKGhZiX1hwBXmAuiamUe6K1p6XcCrNw2jY3gQt7y5gn2HKo/uf3jE6PEDkYQQwos8dVHUBvQCxgBTgP8qpWKO30lr/YrWeqjWemh8fLyHDt0GQjtAn4tg3Qew81vTXbHnBSREhvD6TcOorHFyy5srKK1yTw+QPATK86Fkn0+LLYRoX5oS6DlAlzr3U9zb6soG5mita7XWu4CtmIAPHIOmQEURLPzDMd0V+yRF8uJ1p7Etv4zb3sqkssYpF0aFED7RlEBfAfRSSnVTSgUB1wBzjtvnE0ztHKVUHKYJppF5Z/1Mz/MgPB4Kt0K3Ucd0VxzdO57nrh7ET7sPMO2dTKrj+oKyBlY7uqMJi30IIXyq0UDXWjuAu4EFwCZgltZ6g1LqCaXURPduC4AipdRGYBHwoNa6yFuF9gmrHTKuNr8fnoyrjkmDO/O3Kwfy/bZC7pq1GR2fHjiBvm81/DX16GRkQohTkq0pO2mt5wHzjtv2xzq/a+B+90/gGnarCel+x3fyMa4e2oXqWid/+HQDSxNSObNsBUrro3O9NKS8EHJWwb5VkLPSDExKGW6+CaSNgvA4L5xME2kN8x8GR5W5KNzrxA8zIcSpoUmBLtxie8AtX5x0l+vPTKOq1sW8BUmcZS/EdWgvlg6pJ+64+wdY8V8T5If2uDcqiE+HiARY+z5kvmY2J/Qzwd7zPDN3TGMfEJ604WPIWgqRybB1genlYw9pu+MLIZpMhv57we2ju5Mx7BwAZn4y58R51PeugHevNKGePAQueAJumguP7IW7lsGNc+Ch3XDrV3DeHyEiEVa9DTOuhp/fbbsTqa2EhX+CxAy49AWoKTO9fIQQpySpoXvJ1RePx7nGysEdP/HHT8fw+MT+WC0KCreZYI5MglsXQkQD3TetdugyzPyMegAcNfD6OFj8DAy6xjzubUv/DcVZcNln0GUEBEfDpjnQp95hBkIIH5MaupcoeyiWxP5MiN3PO8v2cM/MVVQd3AfvXmGmBpg6u+Ewr48tCMY8AoeyTFu2t5XkwvfPQ/ol0G20OX6f8bBlHjjrWY5PCOFzEuhepJIH071mK49NSGfxup3se/ESdHkhXDfr6CpHzdFrHCSfBoufNTV2b/r6CXDVwri/HN3WdyJUHoTdS7x7bCFEi0ige1PyEKg8yG39NIu6vEZq7S5+b3uQfeH9Gn9ufZRqm1p6zipYMwNG3AEdux/d3mMs2MNMs4sQ4pQjge5Nh0eMzpxMfMFSdo18ms8q+nPFf348upRdc/W6ADqfDt//3Tu1dK1h/iNmENWo3x77WFCYOf6mz8Hl9PyxhRCtIoHuTQn9wGKHou0w9jF6jfsVH9xxJhrNVS8v5ccdhc1/TW/X0tfPhr3LYOwfICTqxMf7TjTz1Oz9yfPHFkK0igS6N9mCTY+Ukb8+UttNT4rioztHkhQVwnWvLucvn2+kosbRvNfteT50HgqLPVxLry413RSTMmDI1Pr36TUOrEGw6TPPHVcI4RES6N426d9wwZ+PGQzUOSaUj+48i6lndOW1Jbu48IXFLNnWjNr64Vp6cZZp624NZ60Z0v/RNPhHOpRkw4VPg8Va//4hUaYtfdNnMj2wEKcYCXQfiQyx85fLBvD+tBHYLBamvrac3324huKKJnYJ7Hmeu5b+j+bX0l0u2LMUPr8f/tEHpv/CjAIdcCXc8qWZcuBk+l5qPkwCZa4aIQKEBLqPndE9li/uG8UdY3owe1UO5z//HfPXN2GB6bq19NXTm3fQuffDG+Nh9QzoPgaumQm/3QoT/wWpZzT+/D4TzGyS0uwixClFAv0UEGK38tD4dD69ayTxEcH86t1VPPLROqpqG+lJ0vM8s97p982opW9bCCvfgGG3w4Pb4RevQ/oE097fVGEdTS1+0xxpdhHiFCKBfgoZ0DmaT+8eyR1jejDzpywue/EHtueXNfwEpWDMw1C8F5b9p/EDVB6COfdCfF+48CkIjmh5YfteanrvFGxu+WsIITxKAv0UY7daeGh8Om/ePIz80mou/d8lzF6Z3fATepxnhud//WfYPK/h/QAW/B7K8uCy/zSvRl6f9EsABRtlkJEQJyjZB/89zwzSa0MS6KeoMX0SmHfvKDJSonnggzX89oM19XdvVAqu+K8ZxPThLWY+9fpsWwir34WR90Hn01pfwMgk6HKGjBoVoj5LnoecTPjid23aLCmBfgpLig5hxm1ncO/Ynsxelc3Ef//Ayj0HT9wxKAymvGfmUZ8xGQ7uPvbxI00t6aaJxlP6TYS89VC0w3OvKYS/K8mFlW9BhzTIXgEbP2mzQ0ugn+JsVgv3j+vDu7eeQVmVgytf+pH7Z60mv7Tq2B0jEuC6D02/8ulXmUm0DvvSg00tdfW91Nyuestzr+kJu5fAxk99XQrRXv34L3A5YOpHkNAfvnq8zdbklUD3EyN7xvH1A+dwx5gefL4ml7F//47/Lt5JjcN1dKf43nDNDFNDf2+qeRNtW2gWxRh5n5kDxpNiUmHgZPjhn5D5etOeU1UCuWs8W466diyCdy6HWTfAspe9dxwh6lOWb/4WBl1jZlQd9xfz97ji1TY5vAS6HwkPtvHQ+HQW/GY0w7t15Kl5m7jon4tZvLXg6E5pI2HSf2DPEjP60xtNLXVN/LeZDuDz+2HtByffN28j/N9o8/P2ZZ6/YJSdCe9dB7G9oM/FMP8hWP5/nj2GL9VWwZb58Nl9bTOFsr+oPNjmFx8b9OP/grPGLEoDpmtxj/Pgu2eg4oDXD69OWB6tjQwdOlRnZmb65NiB4utNeTzx+Ub2FFVwXnoCj0xIp2dCpHlw8d/hm7+YxTRu+8rztfO6aivh3V+YtUcnvwPpF5+4z6bP4KNfmq6Sp90AK16DygNmwe1zHzPfLlojbyO8cRGEdoBb5kNoR/jwZtj8OVz0LJwxrXWv7ytVJbDtS3Me2xaaZQDt4VBbbubcufwVSGzhdMz+rnQ/LH0RMt+AmlIz0O6ch9p2zd26ygvhhQzTA+zK/x7dnrcBXj4bzrgDxv9Pqw+jlFqptR5a72MS6P6t2uHktSW7eGnRDipqnUwe1oXfnN+b+IggE+rhsTD0ljYoSCm8PQn2r4NrZ0GPc812lwu++yt89zfzoTL5XYhKNkG19N/mD7K2AgZfC6MfNEHsqAZH1dFbl8PMXGkLqv/YB3bB6+5l8W5dYC5GganBfnATbJkLE/4Ow2+v//lVJeaPrssZYDlFvrTu/ck0ZW370tT4wuPNCN2+l5oVpLZ/Zb59VZeYdWdH3HXqlN3bDuwy/zarZ5hFWPpfYUJ83QemsnDx82Btweqah7LgkzshPM5882zuOI2v/mx6t9y1HOL7HPvYp3fDmvfg7p+OXWOgBSTQ24Gismr+9fU2pi/PIthm4Zfn9OC2Ud0IC2rDZWMrDsCbF5s2w+s/gYS+8PGvTKAOvg4ufg7sIcc+p7zQjHRd8aoJroaEdoSBV5vgTxp4tBZWuh9evxCqiuHmL8wx63LUwAc3mqXzLv4HDLvNfdwis23TZ7BzkTn2OQ/BuY827Vy3zDcfMD3GNm3/ptAadn9vmlN2LTbnPGiKCfEuw0+cMK2swDS/bJkLXc+Gy18y1zV8wekw37giErzz+i6naVL76RXY8BFYbOY9NfJeE5BawzdPmnUCeo83I6CDwpv++tsWwke3m04FtRWQ2B+mvA/RnZv2/IoD8MJAs17AVW+c+HjpfvjXENM8eXXrOhFIoLcjOwvKeGb+FuZv2E9iVDB3junJ5ad1JiqkDRaVBijNM/PElBdBZKLp0njh/8AZvzz5V+FDe2HDx6aJyBYMthD3bbCpoW/6HDbPBWc1JA4wwd7zAhPWB/fAjZ9BSgPNSo4ac5F06xcwfJoZ3br7B9BOE4B9J0JxtuledvU7pjvmyayfDR/eCmjofZH5Gt2aWpfWJlC+/zvsXQ4RSSaoTr+p8VDS2szl88VDgIILnzRB1xaLiPPv03QAABRFSURBVB9WVQwzp5guele9ZaaS8ITaStj5rfl/3zofygsgKAKG3my+kUR1OvE5K16Deb814zKunWVq2yfjdMC3/2MqFYkZJmwP7IQPbjb/9lNmNm3cxqL/Md9C7/jRfBjU59u/wrdPm8Xhuwxv/DUb0OpAV0qNB/4JWIFXtdZ/bWC/K4EPgWFa65OmtQS6d63YfYCn521iVdYhQu1WJg5K5roRqQxMifH+wQ9lwesXmZrOVW9C93M887qVB2H9R+ardo77vWMNgus+MJOMnYyj2h3q8yGuj6n19r0UOg0yHzS1VfDmBMjfbK45NNQuvfM7Mztl56HQe5xp1nLWmgA++34zJqA5dv8ACx6F3NUQ3QXO/jUMnnriN5nGHNwNH98BWT9CdCqcdY+Z07655Wmu0jx490rzIdmxmwnDK1+F/pef/HlVJbD8ZfN/arGZDyCL3TSVKKsZILfjG/MeCo4yawCkX2xqwCHRJ3/tzXPNILuoZLMYe0MftqV5MPtW861oyPUw4Vmwh5rH8jaaMR3lBXDF/5lrPQ2eSzE8nwHdR5smxYbUlMO/TjOViFu/bHFbf6sCXSllBbYCFwDZwApgitZ643H7RQJzgSDgbgn0U8Pa7ENMX5bFnDX7qKx1MqBzFNcO78qkwcmEB3uxOabyEGiXmcjLGwq2mDbT1DNNT4KmcDmhJKfhZomSffDKGLNu6u3fnFj23LXwxgSI6QI3zzMXYEtyYeEfYd0siEoxc+T0m9T4H2tJLiz8gzmH6FQY85DpAtqamrXLZdrclzxnavphsXDGr0wzkzf+Hw7sNF1EywrMxfCUoTD9asj+CS57GQZNrv95WctMD6xDWaYW7Kw1beG6ThfcyGRT0+8zAdJGNXz9pCF7fzKBDJA6wjQFhSeY24hE861v/sPmg+WS58w3vuOVFcB715rzOe+P5gO7vv/X756FRU/CL7+HTgNPXq5Vb8Oce8w3mf6XNe+c3Fob6GcCj2utL3TffwRAa/30cfu9ACwEHgR+K4F+aimpquXTn3OYvjyLzftLiQy2cdXQLtx4Vle6xjajrTHQZS031wG6jYJrPzh6ce3gbnhtnKlF3vrliW2re36EeQ+akbOdTzc9HXqNM1+/64aAs9bUTL/9q7tmfx+c/RvP16T3LDUX6LYtML1iht5seoG0ZkK2unLXmpq5y2G+IaW486W6DGZeYwZ3XfpPOP3Go89x1ppmie//Yb6NXPGKCdvDXC7zeq5a86Ha2t4qhdvMh+2hLNM/vLwAqJN3sT3h6rcbbiIB881tzt3mgzdtlOlbHtrRfECGdoTQGPj0LugyAq59r/EyuZzmfTRwcot7XrU20H8BjNda3+a+fz1whtb67jr7nAb8Xmt9pVLqWxoIdKXUNGAaQGpq6ul79uxp0QmJltNasyrrEG8v3c3ctbk4tea89ARuOqsbI3vGonzV5etUsvIt+Oxe02wx7klz4fa1cVBRZML8+B4MhzkdZmriVW/D/rVmW1Rn00zQaxxYg03zSuEW6HUhXPTXVvd4aFTeBtMjZN0HJsCueqv13Rx3LzFt5sGRcP3HJ/571FbC+1NNT5zDvYsKt8NHt5lFUQZPhfFP179mrTc5Heb/sDzfXMTsfHrTPuC0Nh+Oa94zF34rD5oPnrpu/6bpXYNdrlb1SPJqoCulLMA3wE1a690nC/S6pIbue3klVUxftofpy7MoKq+hV0IEN41M44ohKYQGNbAEXXvx+f2Q+Rpc8oIJ6PxNcMOnTVsABEyTyvavTBPIjkWmnzRATFe46G/Q5yLvlb0+O7+D2beZ7qUX/91cOG3uh7fW5oLwJ3dCh64mzKNT6t/XUW0uLG6Za2qjmz4zF7gv/efJ26P9gdamu2jFARPwFnvjTS0e5NUmF6VUNLADODxxdxJwAJh4slCXQD91VNU6+XxtLm/8sIsN+0roEGbn2jNSueHMNBKjmnlxLlA4aky/+qwfzUW6a6a3PIQdNbB3melJ0//yoxfe2lppnumat+s7GHiN6cbZ1CaYAzth3u9g+0KzqMq1sxpvl3fWmuNt+Bi6nwuXvVR/zxTRLK0NdBvmouh5QA7moui1WusNDez/LVJD90taa37adYDXluxi4aY8bBbFpQOTueXsbgzo3EjPgkBUVmBGmw6ZaubmCAQup+mZ8+3TENer8SaY2irTZPP9P8wF23N/b7p+NnXgjstpevB0GtJ+Bj55mSe6LU4AXsB0W3xda/2UUuoJIFNrPee4fb9FAt3v7Skq540fdjMrcy8VNU7O6NaRa89IZVy/JGmOCQRHmmBKIO1s07c/cQAkDTBz4Vhtpslo3oOmdt7/CtODJyrZ1yVv92RgkWix4spa3l+RxdtL95B9sJKIYBsTMpK48rQUhnfrKBdR/VlpHix6ykxsVbDZ9C4Bc/E2potZYjC2p7mweXgqB+FzEuii1VwuzfJdB5i9Kpt563KpqHHSpWMoVwxJYfyAJPokRmKxSLj7LUcNFG0zvWL2rzP9/LsMNz19PDmHvmg1CXThURU1Duav38/sVdn8uKMIrSE2PIgRPWI5q0csI3vE0TU2TGrvQniBBLrwmv3FVXy/rYClO4r4YUcheSVmZZbOMaGM7h3Hhf2TOKtHHEE2uSAmhCdIoIs2obVmZ2E5P+4o4sfthSzeWkB5jZOoEBvn901k/IAkRveOJ8QuF1WFaCkJdOETVbVOlmwr5Iv1+/lqUx7FlbWEB1k5p088I3vGcVaPONKkaUaIZjlZoLfhZNmivQmxWzm/XyLn90uk1uli6Y4ivli/n0Wb85m3bj8AydEhnNkjjrN6xDKiRyzJ0SES8EK0kAS6aBN2q4XRveMZ3TserTW73E0zS3cUsWhLPrNXZQPQIcxOelIUfZIi6dspkvSkKHonRkrfdyGaQAJdtDmlFN3jI+geH8HUEV1xuTRb8kr5adcBNu8vYVNu6ZEBTQAWBUNSOzA2PYHz+ibQJzFSavFC1EPa0MUpyeXS7D1YwabcUjbsK+bbLQWsyykGTA+a8/omMDY9gUEpMcSE2SXgRbshF0VFQMgrqWLR5ny+3pzPkm2FVNaaGnxYkJXkmFCSY0LpHBNCcnQoA1KiGd0rHqsMdhIBRi6KioCQGBXCNcNTuWZ4KlW1TpbvOsC2vFL2Hapi36FK9hVXsiGnmKJys9h0cnQIVw3twuRhXUiO8dEMh0K0Iamhi4BTWePku635zPhpL99vK0ABY/okcM2wLoxNT8BmlUFOwn9JDV20K6FBVsYP6MT4AZ3Ye6CCWZl7eX/FXqZtzqdDmJ3BXWLISIlhUEo0GSnRJES20znfRcCRGrpoFxxOF4u2FLBgw37WZRezLb8Ul/utnxQVQr/kKMKCrNitFmwWhc2qsFks2K0WBnSO4pze8cRGyCRVwvekhi7aPZvVwgX9ErmgXyIA5dUONuaWsDa7mLXZh9iyv5Qah4talwunU+NwmZ+qWicVPzhRCgZ3iWFsnwTOTU+gf3KU9KwRpxypoQtxEi6XZv2+Yr7ZnM+izfmsyTZdJxOjgumVEEmwzULQ4R+ruU2OCeX8von0ToyQ0BceJ90WhfCQgtJqvt2Sz7dbC8g9VEmN00V1rYsap4sah/k5UFGD1pAWG8aF/ZMY1z+JIV1iZL544RES6EK0ofySKhZuymPBhjyW7iik1qmJjwxmRPdYahxOSiodlFTVmp9KB1W1Tkb2jGPS4GQu6JdIWJC0hIqGSaAL4SPFlbV8uyWfBRv2s2ZvMRHBNqJCbUSF2IkKtRMVYkMDX23MY19xFaF2K+P6JzJpcDKjesVjly6W4jgS6EKc4lwuzYrdB/hk9T7mrculuLKWDmF2usWFExpkJcRmJSTISqjdSojdQteO4YzsGUd6kiz9195IoAvhR2ocLhZvLWDeulzyS6uprHVSVes0tzXm9mCFWdA5NjyIs3rGcXbPWEb2jCOlQxhaaypqnJRWOSitqqW02oHDqekeH06cdL30e9JtUQg/EmSzHJlHviH7i6v4YXshP2wvZMn2Qj5bsw+AiGAbFTWOI33sjxcXEUSfpMgjUxSnJ0XSLS6cyBC7N05FtDGpoQvh57TWbM8v4/tthWQdqCAyxEZEsI3IEDsRITYiQ2woYHt+GVv2l7Ilr5SteaVU1bqOvEZseBBdY8PoGhvuvg0jJjTo2G6Z7q6ZoUFWwuw2woKt0sbvA1JDFyKAKaXolRhJr8TIk+43pk/Ckd+dLk3WgQq27C9ld1E5e4rK2VNUwU+7DvDJ6hyaWs8LsloIC7YSZrcSHRZEasdQ0mLDSY0No2tH8+GQHBMqs162kSYFulJqPPBPwAq8qrX+63GP3w/cBjiAAuAWrfUeD5dVCOEhVouiW1w43eLCT3isqtZJ9sFKSqtqTd/64/raV9Y6qahxUlHtoKLW3JbXODlYXsOOgnIWbSmgxnG09h9ks9A3KZKBKTEMTIlmYEoMPRMiJOS9oNFAV0pZgReBC4BsYIVSao7WemOd3X4GhmqtK5RSdwDPAJO9UWAhhHeF2K30TIho8fNdLs3+kip2F5WTVVTBjoIy1uUU8/HPObyzzNTzQu1W+iVHYbcqKt0XeitqzMXfihonVosiMthGRIiN8ODDTUg2okPtxEeGkBAZbH6izO/xkcHS/EPTaujDge1a650ASqn3gEnAkUDXWi+qs/8yYKonCymE8B8Wizqy4MhZPY5ud7k0OwvLWZt9iLXZxWzMLcGlISYsiGR3l8xQ963DpSmrdlBe7aCs2kFplYP9xVUcrKihqLzmhCYhpSA+IphOMaEkR4fQKTqU5JgQOseEMqBzNCkdQtvFNAxNCfTOwN4697OBM06y/63AF/U9oJSaBkwDSE1NbWIRhRCBwGJR9EyIoGdCBFecltLi13E4XRSW1ZBfWkV+STX5pdXklVSRW1xJbnEVW/NK+W5rwZE1aQHiI4M5LTWG01I7cFrXDmR0jibIaqG8xkFJlYOSylrzU+XAZlXEhQcTGxFEx/AgQuz+s0C5Ry+KKqWmAkOBc+p7XGv9CvAKmF4unjy2EKJ9sFktJEWHkBTd8Dz2WmtKKh3sOVDOmr2HWJV1iFVZB1mwIQ8w1xC01g1276wrMthGbEQQ8ZHBdI4JpXOHUFI6hB35vVN0CCE260kHeGmtqXa4KK92UFHjJCzI6pXpmJsS6DlAlzr3U9zbjqGUOh/4PXCO1rraM8UTQojmU0oRHWZnYFgMA1NiuP5Ms72wrJqfsw6xNvsQgHsKBjMVQ3SoncgQO7UuF0VlNRSVVVNUXkNhWTVFZTXsL6lixe6DfLY2F2c9nwRBNgvBNgvBNjOaN8hqobLWSbn7onHd59w5pge/G5/u8fNuSqCvAHoppbphgvwa4Nq6OyilhgD/B4zXWud7vJRCCOEBcRHBx8yL3xIOp4u80mqyD1SQc6iSvJJqqmqdVDmcVNe6qHa4qK51Uu10EWq3EhFsIyzIeuTibniwjb6dTt7FtKUaDXSttUMpdTewANNt8XWt9Qal1BNAptZ6DvAsEAF84L7wkKW1nuiVEgshhA/ZrBbT3HIKLjzepDZ0rfU8YN5x2/5Y5/fzPVwuIYQQzSQdN4UQIkBIoAshRICQQBdCiAAhgS6EEAFCAl0IIQKEBLoQQgQICXQhhAgQPluxSClVALR0zvQ4oNCDxfEn7fXc5bzbFznvhnXVWsfX94DPAr01lFKZDS3BFOja67nLebcvct4tI00uQggRICTQhRAiQPhroL/i6wL4UHs9dznv9kXOuwX8sg1dCCHEify1hi6EEOI4EuhCCBEg/C7QlVLjlVJblFLblVIP+7o83qKUel0pla+UWl9nW0el1EKl1Db3bQdfltEblFJdlFKLlFIblVIblFL3ubcH9LkrpUKUUj8ppda4z/vP7u3dlFLL3e/395VSQb4uqzcopaxKqZ+VUp+77wf8eSuldiul1imlViulMt3bWvU+96tAV0pZgReBi4B+wBSlVD/flspr3gTGH7ftYeBrrXUv4Gv3/UDjAB7QWvcDRgB3uf+PA/3cq4GxWutBwGBgvFJqBPA34HmtdU/gIHCrD8voTfcBm+rcby/nfa7WenCdvuetep/7VaADw4HtWuudWusa4D1gko/L5BVa68XAgeM2TwLecv/+FnBZmxaqDWitc7XWq9y/l2L+yDsT4OeujTL3Xbv7RwNjgQ/d2wPuvAGUUinAxcCr7vuKdnDeDWjV+9zfAr0zsLfO/Wz3tvYiUWud6/59P9DylW79gFIqDRgCLKcdnLu72WE1kA8sBHYAh7TWDvcugfp+fwH4HeBy34+lfZy3Br5USq1USk1zb2vV+7xJa4qKU4/WWiulArbPqVIqApgN/FprXeJefBwI3HPXWjuBwUqpGOBjIN3HRfI6pdQlQL7WeqVSaoyvy9PGztZa5yilEoCFSqnNdR9syfvc32roOUCXOvdT3NvaizylVCcA922+j8vjFUopOybMp2utP3JvbhfnDqC1PgQsAs4EYpRShytegfh+HwlMVErtxjShjgX+SeCfN1rrHPdtPuYDfDitfJ/7W6CvAHq5r4AHAdcAc3xcprY0B7jR/fuNwKc+LItXuNtPXwM2aa2fq/NQQJ+7UireXTNHKRUKXIC5frAI+IV7t4A7b631I1rrFK11Gubv+Rut9XUE+HkrpcKVUpGHfwfGAetp5fvc70aKKqUmYNrcrMDrWuunfFwkr1BKzQTGYKbTzAP+BHwCzAJSMVMPX621Pv7CqV9TSp0NfA+s42ib6qOYdvSAPXel1EDMRTArpqI1S2v9hFKqO6bm2hH4GZiqta72XUm9x93k8lut9SWBft7u8/vYfdcGzNBaP6WUiqUV73O/C3QhhBD187cmFyGEEA2QQBdCiAAhgS6EEAFCAl0IIQKEBLoQQgQICXQhhAgQEuhCCBEg/h8gq3aYSFpEXwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_dP9EBN9Z2xO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "212e8c6a-e214-4451-b3f9-4412c9d8f873"
      },
      "source": [
        "plt.plot(r.history['accuracy'], label='acc')\n",
        "plt.plot(r.history['val_accuracy'], label='val_acc')\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fed065abe48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y2KQTQAqZ44E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "outputId": "6eaac825-608a-4292-e5ba-fd975894336f"
      },
      "source": [
        "# Plot confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import itertools\n",
        "\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "  \"\"\"\n",
        "  This function prints and plots the confusion matrix.\n",
        "  Normalization can be applied by setting `normalize=True`.\n",
        "  \"\"\"\n",
        "  if normalize:\n",
        "      cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "      print(\"Normalized confusion matrix\")\n",
        "  else:\n",
        "      print('Confusion matrix, without normalization')\n",
        "\n",
        "  print(cm)\n",
        "\n",
        "  plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "  plt.title(title)\n",
        "  plt.colorbar()\n",
        "  tick_marks = np.arange(len(classes))\n",
        "  plt.xticks(tick_marks, classes, rotation=45)\n",
        "  plt.yticks(tick_marks, classes)\n",
        "\n",
        "  fmt = '.2f' if normalize else 'd'\n",
        "  thresh = cm.max() / 2.\n",
        "  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "      plt.text(j, i, format(cm[i, j], fmt),\n",
        "               horizontalalignment=\"center\",\n",
        "               color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "  plt.tight_layout()\n",
        "  plt.ylabel('True label')\n",
        "  plt.xlabel('Predicted label')\n",
        "  plt.show()\n",
        "\n",
        "\n",
        "p_test = model.predict(X_test).argmax(axis=1)\n",
        "cm = confusion_matrix(y_test, p_test)\n",
        "plot_confusion_matrix(cm, list(range(10)))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[910  10   5  11   5   4   3   3  32  17]\n",
            " [  4 950   1   2   1   1   1   0   8  32]\n",
            " [ 44   1 776  44  34  37  51   5   5   3]\n",
            " [ 10   6  10 815  16  74  44  10   3  12]\n",
            " [  7   2  16  40 858  29  25  21   1   1]\n",
            " [  6   3  11 107  19 814  20  13   3   4]\n",
            " [  3   0   8  20   3   5 957   0   2   2]\n",
            " [  7   1   7  28  22  26   8 895   3   3]\n",
            " [ 26  11   1   8   2   0   6   2 925  19]\n",
            " [  7  41   2   2   2   0   3   1   9 933]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PEocUBYYZ8M8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# label mapping\n",
        "labels = '''airplane\n",
        "automobile\n",
        "bird\n",
        "cat\n",
        "deer\n",
        "dog\n",
        "frog\n",
        "horse\n",
        "ship\n",
        "truck'''.split()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CzrC6pScZ_Rf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "6c6c96cf-e528-454b-e9f2-df56d1bebd3e"
      },
      "source": [
        "# Show some misclassified examples\n",
        "misclassified_idx = np.where(p_test != y_test)[0]\n",
        "i = np.random.choice(misclassified_idx)\n",
        "plt.imshow(X_test[i], cmap='gray')\n",
        "plt.title(\"True label: %s Predicted: %s\" % (labels[y_test[i]], labels[p_test[i]]));"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bk6EB3__aBxM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        },
        "outputId": "8ad46be9-d4fe-4f44-f3d6-75eae91509e4"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 32, 32, 3)]       0         \n",
            "_________________________________________________________________\n",
            "conv2d (Conv2D)              (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1024)              2098176   \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                10250     \n",
            "=================================================================\n",
            "Total params: 2,397,226\n",
            "Trainable params: 2,396,330\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ajPSeiLHaf7U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}