Res_Net_Cifar_10.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "tpYImrK5G29t"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.datasets import cifar10\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, add , Input,GlobalAveragePooling2D\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
        "from tensorflow.keras.regularizers import l2\n",
        "from keras.models import load_model\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import pandas as pd\n",
        "from keras.models import Model\n",
        "from keras.optimizers import Adam\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams['axes.unicode_minus'] = False\n",
        "plt.style.use('fivethirtyeight')\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q0uDwlQLXhIe",
        "outputId": "3d63ce80-4b13-4bdd-d538-9f024a1f58af"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "print('x_train shape:', x_train.shape)\n",
        "print(x_train.shape[0], 'train samples')\n",
        "print(x_test.shape[0], 'test samples')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ong3vGfrHr86",
        "outputId": "ec57dcb5-0635-4271-c90e-7fb5742732b6"
      },
      "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",
            "170498071/170498071 [==============================] - 2s 0us/step\n",
            "x_train shape: (50000, 32, 32, 3)\n",
            "50000 train samples\n",
            "10000 test samples\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting some random 10 images\n",
        "\n",
        "class_names = ['airplane','automobile','bird','cat','deer',\n",
        "               'dog','frog','horse','ship','truck']\n",
        "\n",
        "fig = plt.figure(figsize=(10,5))\n",
        "for i in range(10):\n",
        "    ax = fig.add_subplot(2, 5, 1 + i, xticks=[], yticks=[])\n",
        "    idx = np.where(y_train[:]==i)[0]\n",
        "    features_idx = x_train[idx,::]\n",
        "    img_num = np.random.randint(features_idx.shape[0])\n",
        "    im = (features_idx[img_num,::])\n",
        "    ax.set_title(class_names[i])\n",
        "    plt.imshow(im)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "whpjiwuoHu6B",
        "outputId": "84bf5f1e-7d97-43c6-fd57-5c4c288e5523"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert class vectors to binary class matrices.\n",
        "y_train = tf.keras.utils.to_categorical(y_train, 10)\n",
        "y_test = tf.keras.utils.to_categorical(y_test, 10)"
      ],
      "metadata": {
        "id": "QvfHwn-oJCya"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def ResidualBlock(inputs, filters, stride=1):\n",
        "    x = Conv2D(filters, (3, 3), strides=stride, padding='same')(inputs)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Activation('relu')(x)\n",
        "    x = Conv2D(filters, (3, 3), strides=1, padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    shortcut = inputs\n",
        "    if stride != 1 or inputs.shape[-1] != filters:\n",
        "        shortcut = Conv2D(filters, (1, 1), strides=stride)(inputs)\n",
        "    x = add([x, shortcut])\n",
        "    x = Activation('relu')(x)\n",
        "    return x\n"
      ],
      "metadata": {
        "id": "OpN3jxYOMUPh"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_model(input_shape, num_classes, num_blocks):\n",
        "    inputs = Input(shape=input_shape)\n",
        "    x = Conv2D(64, (3, 3), strides=1, padding='same')(inputs)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Activation('relu')(x)\n",
        "    for i in range(num_blocks):\n",
        "        x = ResidualBlock(x, 64)\n",
        "    x = Conv2D(64, (3, 3), strides=1, padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Activation('relu')(x)\n",
        "    x = Conv2D(num_classes, (1, 1), strides=1)(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Activation('relu')(x)\n",
        "    x = GlobalAveragePooling2D()(x)\n",
        "    outputs = Activation('softmax')(x)\n",
        "    model = Model(inputs, outputs)\n",
        "    return model\n",
        "\n",
        "input_shape = (32, 32, 3)\n",
        "num_classes = 10\n",
        "num_blocks = 6\n",
        "model = create_model(input_shape, num_classes, num_blocks)\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer=Adam(learning_rate=0.001),\n",
        "              metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "u3bUwEqFMcfR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install visualkeras"
      ],
      "metadata": {
        "id": "sRhx0bInUPBn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing our model (Hidden Input)\n",
        "import visualkeras\n",
        "visualkeras.layered_view(model, scale_xy=10, legend=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "3JP8kWJaUQAo",
        "outputId": "e9204ef4-7478-41a2-cd21-2a00995f43ab"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGBA size=1687x463 at 0x7FB352D2BAC0>"
            ],
            "image/png": 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1olxaM8VSpZ4H2w20jjmP4AVfld7vV47qyFFRc/uVozpyVNTcfuWojhwVNbdfOaojR0XN7VeO6shRUXP7laM6PZ/z2C3OJzNzyWAwtUsql9ZAsVSp58F2A61jziN4wVel9/uVozpyVNTcfuWojhwVNbdfOaojR0XN7VeO6shRUXP7laM6clTU3H7lqE7P5zwRC/PJ7NxUL6lcOk+KpUo9D7YbaB1zHsELviq9368c1ZGjoub2K0d15Kiouf3KUR05Kmpuv3JUR46KmtuvHNWRo6Lm9itHdXo+54lZOJbMTO/zlhLl0nlRLFXqebDdQOuY8whe8FXp/X7lqI4cFTW3XzmqI0dFze1XjurIUVFz+5WjOnJU1Nx+5aiOHBU1t185qtPzOU/McJgsHktmlUu9pFiq1PNgu4HWMecRvOCr0vv9ylEdOSpqbr9yVEeOiprbrxzVkaOi5vYrR3XkqKi5/cpRHTkqam6/clSn53OeqJMnkkGSrdunelnl0ioolir1PNhuoHXMeQQv+Kr0fr9yVEeOiprbrxzVkaOi5vYrR3XkqKi5/cpRHTkqam6/clRHjoqa268c1en5nCducT6ZmUsGg6leVrl0DoqlSj0PthtoHXMewQu+Kr3frxzVkaOi5vYrR3XkqKi5/cpRHTkqam6/clRHjoqa268c1ZGjoub2K0d1ej7nqViYT2bnpn5Z5dIKFEuVeh5sN9A65jyCF3xVer9fOaojR0XN7VeO6shRUXP7laM6clTU3H7lqI4cFTW3XzmqI0dFze1Xjur0fM5Ts3AsmZnu5y0lyqWRFEuVeh5sN9A65jyCF3xVer9fOaojR0XN7VeO6shRUXP7laM6clTU3H7lqI4cFTW3XzmqI0dFze1Xjur0fM5TMxwmi8eSWeVSLyiWKvU82G6gdcx5BC/4qvR+v3JUR46KmtuvHNWRo6Lm9itHdeSoqLn9ylEdOSpqbr9yVEeOiprbrxzV6fmcp+rkiWSQZOv2qV9aufQGiqVKPQ+2G2gdcx7BC74qvd+vHNWRo6Lm9itHdeSoqLn9ylEdOSpqbr9yVEeOiprbrxzVkaOi5vYrR3V6PuepW5xPZuaSwWDql1YuvYZiqVLPg+0GWsecR/CCr0rv9ytHdeSoqLn9ylEdOSpqbr9yVEeOiprbrxzVkaOi5vYrR3XkqKi5/cpRnZ7PuRML88nsXCeXVi79BcVSpZ4H2w20jjmP4AVfld7vV47qyFFRc/uVozpyVNTcfuWojhwVNbdfOaojR0XN7VeO6shRUXP7laM6PZ9zZxaOJTPT/7ylRLmURLFUrefBdgOtY84jeMFXpff7laM6clTU3H7lqI4cFTW3XzmqI0dFze1XjurIUVFz+5WjOnJU1Nx+5ahOz+fcmeEwWTyWzCqXOqFYqtTzYLuB1jHnEbzgq9L7/cpRHTkqam6/clRHjoqa268c1ZGjoub2K0d15Kiouf3KUR05Kmpuv3JUp+dz7tTJE8kgydbtnVy+6XJJsVSp58F2A61jziN4wVel9/uVozpyVNTcfuWojhwVNbdfOaojR0XN7VeO6shRUXP7laM6clTU3H7lqE7P59y5xflkZi4ZDDq5fLPlkmKpUs+D7QZax5xH8IKvSu/3K0d15Kiouf3KUR05Kmpuv3JUR46KmtuvHNWRo6Lm9itHdeSoqLn9ylGdns+5Fxbmk9m5zi7fZLmkWKrU82C7gdYx5xG84KvS+/3KUR05Kmpuv3JUR46KmtuvHNWRo6Lm9itHdeSoqLn9ylEdOSpqbr9yVKfnc+6NhWPJTDeft5Q0WC4plir1PNhuoHXMeQQv+Kr0fr9yVEeOiprbrxzVkaOi5vYrR3XkqKi5/cpRHTkqam6/clRHjoqa268c1en5nHtjOEwWjyWzyqWpUCxV6nmw3UDrmPMIXvBV6f1+5aiOHBU1t185qiNHRc3tV47qyFFRc/uVozpyVNTcfuWojhwVNbdfOarT8zn3yskTySDJ1u2dHaGZckmxVKnnwXYDrWPOI3jBV6X3+5WjOnJU1Nx+5aiOHBU1t185qiNHRc3tV47qyFFRc/uVozpyVNTcfuWoTs/n3DuL88nMXDIYdHaEJsolxVKlngfbDbSOOY/gBV+V3u9XjurIUVFz+5WjOnJU1Nx+5aiOHBU1t185qiNHRc3tV47qyFFRc/uVozo9n3MvLcwns3OdHmHDl0uKpUo9D7YbaB1zHsELviq9368c1ZGjoub2K0d15Kiouf3KUR05Kmpuv3JUR46KmtuvHNWRo6Lm9itHdXo+595aOJbMdPd5S8kGL5cUS5V6Hmw30DrmPIIXfFV6v185qiNHRc3tV47qyFFRc/uVozpyVNTcfuWojhwVNbdfOaojR0XN7VeO6vR8zr01HCaLx5JZ5dJEKJYq9TzYbqB1zHkEL/iq9H6/clRHjoqa268c1ZGjoub2K0d15Kiouf3KUR05Kmpuv3JUR46KmtuvHNXp+Zx77eSJZJBk6/ZOj7EhyyXFUqWeB9sNtI45j+AFX5Xe71eO6shRUXP7laM6clTU3H7lqI4cFTW3XzmqI0dFze1XjurIUVFz+5WjOj2fc+8tziczc8lg0OkxNly5pFiq1PNgu4HWMecRvOCr0vv9ylEdOSpqbr9yVEeOiprbrxzVkaOi5vYrR3XkqKi5/cpRHTkqam6/clSn53NeFxbmk9m5rk+xscolxVKlngfbDbSOOY/gBV+V3u9XjurIUVFz+5WjOnJU1Nx+5aiOHBU1t185qiNHRc3tV47qyFFRc/uVozo9n/O6sXAsmen285aSDVQuKZYq9TzYbqB1zHkEL/iq9H6/clRHjoqa268c1ZGjoub2K0d15Kiouf3KUR05Kmpuv3JUR46KmtuvHNXp+ZzXjeEwWTyWzCqXxkKxVKnnwXYDrWPOI3jBV6X3+5WjOnJU1Nx+5aiOHBU1t185qiNHRc3tV47qyFFRc/uVozpyVNTcfuWoTs/nvK6cPJEMkmzd3vVJ1n+5pFiq1PNgu4HWMecRvOCr0vv9ylEdOSpqbr9yVEeOiprbrxzVkaOi5vYrR3XkqKi5/cpRHTkqam6/clSn53Nedxbnk5m5ZDDo+iTru1xSLFXqebDdQOuY8whe8FXp/X7lqI4cFTW3XzmqI0dFze1XjurIUVFz+5WjOnJU1Nx+5aiOHBU1t185qtPzOa9LC/PJ7FzXp0iyjsslxVKlngfbDbSOOY/gBV+V3u9XjurIUVFz+5WjOnJU1Nx+5aiOHBU1t185qiNHRc3tV47qyFFRc/uVozo9n/O6tXAsmen+85aSdVouKZYq9TzYbqB1zHkEL/iq9H6/clRHjoqa268c1ZGjoub2K0d15Kiouf3KUR05Kmpuv3JUR46KmtuvHNXp+ZzXreEwWTyWzCqX1kSxVKnnwXYDrWPOI3jBV6X3+5WjOnJU1Nx+5aiOHBU1t185qiNHRc3tV47qyFFRc/uVozpyVNTcfuWoTs/nvK6dPJEMkmzd3vVJkqyzckmxVKnnwXYDrWPOI3jBV6X3+5WjOnJU1Nx+5aiOHBU1t185qiNHRc3tV47qyFFRc/uVozpyVNTcfuWoTs/nvO4tziczc8lg0PVJkqyjckmxVKnnwXYDrWPOI3jBV6X3+5WjOnJU1Nx+5aiOHBU1t185qiNHRc3tV47qyFFRc/uVozpyVNTcfuWoTs/nvCEszCezc12f4ovWRbmkWKrU82C7gdYx5xG84KvS+/3KUR05Kmpuv3JUR46KmtuvHNWRo6Lm9itHdeSoqLn9ylEdOSpqbr9yVKfnc94wFo4lM/34vKVkHZRLiqVKPQ+2G2gdcx7BC74qvd+vHNWRo6Lm9itHdeSoqLn9ylEdOSpqbr9yVEeOiprbrxzVkaOi5vYrR3V6PucNYzhMFo8ls8qlVVEsVep5sN1A65jzCF7wVen9fuWojhwVNbdfOaojR0XN7VeO6shRUXP7laM6clTU3H7lqI4cFTW3Xzmq0/M5bygnTySDJFu3d32SL+ptuaRYqtTzYLuB1jHnEbzgq9L7/cpRHTkqam6/clRHjoqa268c1ZGjoub2K0d15Kiouf3KUR05Kmpuv3JUp+dz3nAW55OZuWQw6PokX9TLckmxVKnnwXYDrWPOI3jBV6X3+5WjOnJU1Nx+5aiOHBU1t185qiNHRc3tV47qyFFRc/uVozpyVNTcfuWoTs/nvCEtzCezc12f4nV6Vy4plir1PNhuoHXMeQQv+Kr0fr9yVEeOiprbrxzVkaOi5vYrR3XkqKi5/cpRHTkqam6/clRHjoqa268c1en5nDeshWPJTH8+bynpWbmkWKrU82C7gdYx5xG84KvS+/3KUR05Kmpuv3JUR46KmtuvHNWRo6Lm9itHdeSoqLn9ylEdOSpqbr9yVKfnc96whsNk8Vgyq1wqUixV6nmw3UDrmPMIXvBV6f1+5aiOHBU1t185qiNHRc3tV47qyFFRc/uVozpyVNTcfuWojhwVNbdfOarT8zlvaCdPJIMkW7d3fZLX6UW5pFiq1PNgu4HWMecRvOCr0vv9ylEdOSpqbr9yVEeOiprbrxzVkaOi5vYrR3XkqKi5/cpRHTkqam6/clSn53Pe8Bbnk5m5ZDDo+iSv03m5pFiq1PNgu4HWMecRvOCr0vv9ylEdOSpqbr9yVEeOiprbrxzVkaOi5vYrR3XkqKi5/cpRHTkqam6/clSn53NuwsJ8MjvX9Sm+RKflkmKpUs+D7QZax5xH8IKvSu/3K0d15Kiouf3KUR05Kmpuv3JUR46KmtuvHNWRo6Lm9itHdeSoqLn9ylGdns+5GQvHkpl+fd5S0mG5pFiq1PNgu4HWMecRvOCr0vv9ylEdOSpqbr9yVEeOiprbrxzVkaOi5vYrR3XkqKi5/cpRHTkqam6/clSn53NuxnCYLB5LZpVLSRRL1XoebDfQOuY8ghd8VXq/XzmqI0dFze1XjurIUVFz+5WjOnJU1Nx+5aiOHBU1t185qiNHRc3tV47q9HzOTTl5Ihkk2bq965N8iamXS4qlSj0PthtoHXMewQu+Kr3frxzVkaOi5vYrR3XkqKi5/cpRHTkqam6/clRHjoqa268c1ZGjoub2K0d1ej7n5izOJzNzyWDQ9Um+xFTLJcVSpZ4H2w20jjmP4AVfld7vV47qyFFRc/uVozpyVNTcfuWojhwVNbdfOaojR0XN7VeO6shRUXP7laM6PZ9zkxbmk9m5rk9RNLVySbFUqefBdgOtY84jeMFXpff7laM6clTU3H7lqI4cFTW3XzmqI0dFze1XjurIUVFz+5WjOnJU1Nx+5ahOz+fcrIVjyUz/Pm8pmVK5pFiq1PNgu4HWMecRvOCr0vv9ylEdOSpqbr9yVEeOiprbrxzVkaOi5vYrR3XkqKi5/cpRHTkqam6/clSn53Nu1nCYLB5LZhstlxRLlXoebDfQOuY8ghd8VXq/XzmqI0dFze1XjurIUVFz+5WjOnJU1Nx+5aiOHBU1t185qiNHRc3tV47q9HzOTTt5Ihkk2bq965MUTbRcUixV6nmw3UDrmPMIXvBV6f1+5aiOHBU1t185qiNHRc3tV47qyFFRc/uVozpyVNTcfuWojhwVNbdfOarT8zk3b3E+mZlLBoOuT1I0sXJJsVSp58F2A61jziN4wVel9/uVozpyVNTcfuWojhwVNbdfOaojR0XN7VeO6shRUXP7laM6clTU3H7lqE7P50yShflkdq7rU4w0kXJJsVSp58F2A61jziN4wVel9/uVozpyVNTcfuWojhwVNbdfOaojR0XN7VeO6shRUXP7laM6clTU3H7lqE7P58xfWDiWzPTz85aSCZRLiqVKPQ+2G2gdcx7BC74qvd+vHNWRo6Lm9itHdeSoqLn9ylEdOSpqbr9yVEeOiprbrxzVkaOi5vYrR3V6Pmf+wnCYLB5LZhsplxRLlXoebDfQOuY8ghd8VXq/XzmqI0dFze1XjurIUVFz+5WjOnJU1Nx+5aiOHBU1t185qiNHRc3tV47q9HzOvMbJE8kgydbtXZ9kpLGVS4qlSj0PthtoHXMewQu+Kr3frxzVkaOi5vYrR3XkqKi5/cpRHTkqam6/clRHjoqa268c1ZGjoub2K0d1ej5n3mBxPpmZSwaDrk8y0ljKJcVSpZ4H2w20jjmP4AVfld7vV47qyFFRc/uVozpyVNTcfuWojhwVNbdfOaojR0XN7VeO6shRUXP7laM6PZ8zBQvzyexc16dYUXW5pFiq1PNgu4HWMecRvOCr0vv9ylEdOSpqbr9yVEeOiprbrxzVkaOi5vYrR3XkqKi5/cpRHTkqam6/clSn53NmhIVjyUx/P28pqSyXFEuVeh5sN9A65jyCF3xVer9fOaojR0XN7VeO6shRUXP7laM6clTU3H7lqI4cFTW3XzmqI0dFze1Xjur0fM6MMBwmi8eS2Q1aLimWKvU82G6gdcx5BC/4qvR+v3JUR46KmtuvHNWRo6Lm9itHdeSoqLn9ylEdOSpqbr9yVEeOiprbrxzV6fmcWcHJE8kgydbtXZ9kRWsqlxRLlXoebDfQOuY8ghd8VXq/XzmqI0dFze1XjurIUVFz+5WjOnJU1Nx+5aiOHBU1t185qiNHRc3tV47q9HzOnMPifDIzlwwGXZ9kReddLimWKvU82G6gdcx5BC/4qvR+v3JUR46KmtuvHNWRo6Lm9itHdeSoqLn9ylEdOSpqbr9yVEeOiprbrxzV6fmcWYWF+WR2rutTnNN5lUuKpUo9D7YbaB1zHsELviq9368c1ZGjoub2K0d15Kiouf3KUR05Kmpuv3JUR46KmtuvHNWRo6Lm9itHdXo+Z1Zp4Vgy0+/PW0rOo1xSLFXqebDdQOuY8whe8FXp/X7lqI4cFTW3XzmqI0dFze1XjurIUVFz+5WjOnJU1Nx+5aiOHBU1t185qtPzObNKw2GyeCyZ7X+5NBgOh8NzPegf/oO/nf/0kQ/nzW+6IFsuXNPHNL3O6TPDPPbEc/lr1+/IJW/ePPJxn3385Xz+86/m4gs258JB/XXPDIc58uKz+bqtX55LLrhw5OMe3Homf55Xk4u2JBdeUH3dnFlKji0me3clF79p9OP+7PlsevbF5MI3JReMnsuqLZ3J0jNPJ2+9Jtky+ro7n3k6W1583pzXypzL/uTp5MQLyaYLxvP+oMNhcuqF5M1vWX7OUba8KZsytN81X1iOSuSokv2WyVHlheWopLn9ylHlheWopLn9ylHlheWopLn9ylHlheWopLn9ylHlhbuZ8/DM6Qxf+ELyff8fxdK4fOHzyf/2Pyb/t58Y62cuXfCLP5RDv/OxvOtd7xrbc57zT9Arr7ySLzz3bPa9Y0e+5z1vH8tF3/+xo7lgU/ID3za6fTt9ZikfWHg6bz29Pd81e/VYrvvBhceyKcl379g9+rrDpTx6wZ/nmd1XZtO3/p/Gct2l3/7PWdo0SL59hec7fSabfutwLrjkimzZc/NYrvvKoU9mmE0ZXr/C8y2dyYXPH8iezW825zUy59KFzyRPfSGbXtmcTTvq/wuIJFl65uksDYfJthX+S47hMJuGp3PBV+y03zWSoy8lR/Xst3RhOaolR6XDtbdfOaojR6XDtbdfOaojR6XDtbdfOaojR6XDtbdfOarT1Zxf/r3fzunjn00uHv3OZJynxflkZm6sxdKknLNc2rJlS+bmdiXbFsZWLj1y5ERefOmV/M1veOuKj3v88EvZdPiSsYX7M89/LqdefTnftn3lv1L2yVdOZ/6qK7Ppr3/dWK47fPyzyUsvJ9+0d+UHHnk2Fxzfli17bhrLdc8sPpEzL7+U4TtW/n2cWfyzvOPznzfnNTLnEf7wyQweXMgFO64cz3VffD559ZXkLZev/MCXTuaCma+03zWSozI5qmO/I8hRFTka9bjG9itHVeRo1OMa268cVZGjUY9rbL9yVEWORj2usf3KUZUu53z6+FPJfXcn3/K3x/KczVuYT2bnuj7FqtT/XT8AAAAAAKA5gx1XJo8+uPw5QdRbOJbM9P/zlhLlEgAAAAAAsAaDCzYnt9ye3HNnMlzq+jjr23C4XNLNKpcAAAAAAICN7Pq/eJu9ww90e4717uSJZJBk6/auT7IqyiUAAAAAAGBtBpuSW+9IDtyVvPRC16dZvxbnk5m5ZDDo+iSrolwCAAAAAADWbnYuuWZfct/dXZ9k/VqYX57jOqFcAgAAAAAA6tx8W/Log8ufG8T5WziWzKyPz1tKlEsAAAAAAECti7cmt9ye3HNnMlzq+jTry3C4XMrNKpcAAAAAAICWXH/T8o+HH+j2HOvNyRPJIMnW7V2fZNWUSwAAAAAAQL3BpuTWO5IDdyUvvdD1adaPxflkZi4ZDLo+yaoplwAAAAAAgPGYnUuu2Zfcd3fXJ1k/FuaX57aOKJcAAAAAAIDxufm25NEHlz9HiHNbOJbMrJ/PW0qUSwAAAAAAwDhdvDW55fbknjuT4VLXp+m34XC5hJtVLgEAAAAAAC27/qblHw8/0O05+u7kiWSQZOv2rk9yXpRLAAAAAADAeA02JbfekRy4K3npha5P01+L88nMXDIYdH2S86JcAgAAAAAAxm92LrlmX3Lf3V2fpL8W5pfntM4olwAAAAAAgMm4+bbk0QeXP1eIL7VwLJlZX5+3lCiXAAAAAACASbl4a3LL7ck9dybDpa5P0y/D4XLpNqtcAgAAAAAA+EvX37T84+EHuj1H35w8kQySbN3e9UnOm3IJAAAAAACYnMGm5NY7kgN3JS+90PVp+mNxPpmZSwaDrk9y3pRLAAAAAADAZM3OJdfsS+67u+uT9MfC/PJc1iHlEgAAAAAAMHk335Y8+uDy5wyRLBxLZtbf5y0lyiUAAAAAAGAaLt6a3HJ7cs+dyXCp69N0azhcLtlmlUsAAAAAAACjXX/T8o+HH+j2HF07eSIZJNm6veuTrIlyCQAAAAAAmI7BpuTWO5IDdyUvvdD1abqzOJ/MzCWDQdcnWRPlEgAAAAAAMD2zc8k1+5L77u76JN1ZmF+ewzqlXAIAAAAAAKbr5tuSRx9c/tyhFi0cS2bW5+ctJcolAAAAAABg2i7emtxye3LPnclwqevTTNdwuFyqzSqXAAAAAAAAVu/6m5Z/PPxAt+eYtpMnkkGSrdu7PsmaKZcAAAAAAIDpG2xKbr0jOXBX8tILXZ9mehbnk5m5ZDDo+iRrplwCAAAAAAC6MTuXXLMvue/urk8yPQvzy7/vdUy5BAAAAAAAdOfm25JHH1z+HKIWLBxLZtbv5y0lyiUAAAAAAKBLF29Nbrk9uefOZLjU9WkmazhcLtFmlUsAAAAAAABrd/1Nyz8efqDbc0zayRPJIMnW7V2fpIpyCQAAAAAA6NZgU3LrHcmBu5KXXuj6NJOzOJ/MzCWDQdcnqaJcAgAAAAAAujc7l1yzL7nv7q5PMjkL88u/z3VOuQQAAAAAAPTDzbcljz64/LlEG9HCsWRmfX/eUqJcAgAAAAAA+uLirckttyf33JkMl7o+zXgNh8ul2axyCQAAAAAAYHyuv2n5x8MPdHuOcTt5Ihkk2bq965NUUy4BAAAAAAD9MdiU3HpHcuCu5KUXuj7N+CzOJzNzyWDQ9UmqKZcAAAAAAIB+mZ1LrtmX3Hd31ycZn4X55d/XBqBcAgAAAAAA+ufm25JHH1z+nKKNYOFYMrP+P28pUS4BAAAAAAB9dPHW5Jbbk3vuTIZLXZ+mznC4XJLNKpcAAAAAAAAm5/qbln88/EC356h18kQySLJ1e9cnGQvlEgAAAAAA0E+DTcmtdyQH7kpeeqHr06zd4nwyM5cMBl2fZCyUSwAAAAAAQH/NziXX7Evuu7vrk6zdwvzy72ODUC4BAAAAAAD9dvNtyaMPLn9u0Xq0cCyZ2Rift5QolwAAAAAAgL67eGtyy+3JPXcmw6WuT3N+hsPlUmxWuQQAAAAAADA919+0/OPhB7o9x/k6eSIZJNm6veuTjI1yCQAAAAAA6L/BpuTWO5IDdyUvvdD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          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# early_stopping = tf.keras.callbacks.EarlyStopping(\n",
        "#     monitor='val_loss', \n",
        "#     verbose=1,\n",
        "#     patience=20, # number of epochs that didnt bring an improvement \n",
        "#     mode='min',\n",
        "#     restore_best_weights=True)\n",
        "\n",
        "checkpoint = ModelCheckpoint(filepath='/content/drive/My Drive/best_model_cifar10.h5',\n",
        "                                   monitor='val_accuracy', \n",
        "                                   mode = 'max',\n",
        "                                   save_best_only=True,\n",
        "                                   verbose=1)\n",
        "\n",
        "\n",
        "history = model.fit(x_train, y_train,\n",
        "                    validation_data=[x_test, y_test], \n",
        "                    epochs=50,\n",
        "                    batch_size=32,\n",
        "                    callbacks=[checkpoint], \n",
        "                    verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EbTw8JYOMt_u",
        "outputId": "2d5a4077-22c6-40db-83d4-6a069bc7f3d7"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 1.5946 - accuracy: 0.4684\n",
            "Epoch 1: val_accuracy improved from -inf to 0.49730, saving model to /content/drive/My Drive/best_model_cifar10.h5\n",
            "1563/1563 [==============================] - 67s 37ms/step - loss: 1.5946 - accuracy: 0.4684 - val_loss: 1.4926 - val_accuracy: 0.4973\n",
            "Epoch 2/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 1.1415 - accuracy: 0.6247\n",
            "Epoch 2: val_accuracy improved from 0.49730 to 0.60430, saving model to /content/drive/My Drive/best_model_cifar10.h5\n",
            "1563/1563 [==============================] - 62s 40ms/step - loss: 1.1415 - accuracy: 0.6247 - val_loss: 1.1676 - val_accuracy: 0.6043\n",
            "Epoch 3/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.9443 - accuracy: 0.6912\n",
            "Epoch 3: val_accuracy did not improve from 0.60430\n",
            "1563/1563 [==============================] - 64s 41ms/step - loss: 0.9443 - accuracy: 0.6912 - val_loss: 1.1954 - val_accuracy: 0.5977\n",
            "Epoch 4/50\n",
            "1562/1563 [============================>.] - ETA: 0s - loss: 0.8097 - accuracy: 0.7349\n",
            "Epoch 4: val_accuracy did not improve from 0.60430\n",
            "1563/1563 [==============================] - 60s 39ms/step - loss: 0.8098 - accuracy: 0.7348 - val_loss: 1.2481 - val_accuracy: 0.5978\n",
            "Epoch 5/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.7165 - accuracy: 0.7675\n",
            "Epoch 5: val_accuracy improved from 0.60430 to 0.70110, saving model to /content/drive/My Drive/best_model_cifar10.h5\n",
            "1563/1563 [==============================] - 59s 38ms/step - loss: 0.7165 - accuracy: 0.7675 - val_loss: 0.8685 - val_accuracy: 0.7011\n",
            "Epoch 6/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.6338 - accuracy: 0.7964\n",
            "Epoch 6: val_accuracy did not improve from 0.70110\n",
            "1563/1563 [==============================] - 59s 38ms/step - loss: 0.6338 - accuracy: 0.7964 - val_loss: 0.9228 - val_accuracy: 0.6811\n",
            "Epoch 7/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.5731 - accuracy: 0.8153\n",
            "Epoch 7: val_accuracy did not improve from 0.70110\n",
            "1563/1563 [==============================] - 57s 36ms/step - loss: 0.5731 - accuracy: 0.8153 - val_loss: 1.0423 - val_accuracy: 0.6469\n",
            "Epoch 8/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.5298 - accuracy: 0.8285\n",
            "Epoch 8: val_accuracy improved from 0.70110 to 0.70980, saving model to /content/drive/My Drive/best_model_cifar10.h5\n",
            "1563/1563 [==============================] - 57s 37ms/step - loss: 0.5298 - accuracy: 0.8285 - val_loss: 0.9091 - val_accuracy: 0.7098\n",
            "Epoch 9/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.4920 - accuracy: 0.8410\n",
            "Epoch 9: val_accuracy improved from 0.70980 to 0.76980, saving model to /content/drive/My Drive/best_model_cifar10.h5\n",
            "1563/1563 [==============================] - 59s 38ms/step - loss: 0.4920 - accuracy: 0.8410 - val_loss: 0.6859 - val_accuracy: 0.7698\n",
            "Epoch 10/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.4532 - accuracy: 0.8532\n",
            "Epoch 10: val_accuracy improved from 0.76980 to 0.78010, saving model to /content/drive/My Drive/best_model_cifar10.h5\n",
            "1563/1563 [==============================] - 59s 38ms/step - loss: 0.4532 - accuracy: 0.8532 - val_loss: 0.6450 - val_accuracy: 0.7801\n",
            "Epoch 11/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.4216 - accuracy: 0.8637\n",
            "Epoch 11: val_accuracy did not improve from 0.78010\n",
            "1563/1563 [==============================] - 57s 36ms/step - loss: 0.4216 - accuracy: 0.8637 - val_loss: 0.7105 - val_accuracy: 0.7566\n",
            "Epoch 12/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.3908 - accuracy: 0.8738\n",
            "Epoch 12: val_accuracy did not improve from 0.78010\n",
            "1563/1563 [==============================] - 57s 36ms/step - loss: 0.3908 - accuracy: 0.8738 - val_loss: 0.7168 - val_accuracy: 0.7671\n",
            "Epoch 13/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.3689 - accuracy: 0.8826\n",
            "Epoch 13: val_accuracy improved from 0.78010 to 0.79380, saving model to /content/drive/My Drive/best_model_cifar10.h5\n",
            "1563/1563 [==============================] - 59s 38ms/step - loss: 0.3689 - accuracy: 0.8826 - val_loss: 0.6287 - val_accuracy: 0.7938\n",
            "Epoch 14/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.3432 - accuracy: 0.8906\n",
            "Epoch 14: val_accuracy improved from 0.79380 to 0.81290, saving model to /content/drive/My Drive/best_model_cifar10.h5\n",
            "1563/1563 [==============================] - 59s 38ms/step - loss: 0.3432 - accuracy: 0.8906 - val_loss: 0.5701 - val_accuracy: 0.8129\n",
            "Epoch 15/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.3226 - accuracy: 0.8941\n",
            "Epoch 15: val_accuracy did not improve from 0.81290\n",
            "1563/1563 [==============================] - 58s 37ms/step - loss: 0.3226 - accuracy: 0.8941 - val_loss: 0.5636 - val_accuracy: 0.8112\n",
            "Epoch 16/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.3000 - accuracy: 0.9037\n",
            "Epoch 16: val_accuracy did not improve from 0.81290\n",
            "1563/1563 [==============================] - 59s 38ms/step - loss: 0.3000 - accuracy: 0.9037 - val_loss: 0.5937 - val_accuracy: 0.8024\n",
            "Epoch 17/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.2802 - accuracy: 0.9107\n",
            "Epoch 17: val_accuracy did not improve from 0.81290\n",
            "1563/1563 [==============================] - 57s 37ms/step - loss: 0.2802 - accuracy: 0.9107 - val_loss: 0.6192 - val_accuracy: 0.8031\n",
            "Epoch 18/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.2637 - accuracy: 0.9166\n",
            "Epoch 18: val_accuracy did not improve from 0.81290\n",
            "1563/1563 [==============================] - 59s 38ms/step - loss: 0.2637 - accuracy: 0.9166 - val_loss: 0.5724 - val_accuracy: 0.8103\n",
            "Epoch 19/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.2423 - accuracy: 0.9232\n",
            "Epoch 19: val_accuracy improved from 0.81290 to 0.82190, saving model to /content/drive/My Drive/best_model_cifar10.h5\n",
            "1563/1563 [==============================] - 57s 37ms/step - loss: 0.2423 - accuracy: 0.9232 - val_loss: 0.5587 - val_accuracy: 0.8219\n",
            "Epoch 20/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.2291 - accuracy: 0.9285\n",
            "Epoch 20: val_accuracy improved from 0.82190 to 0.82870, saving model to /content/drive/My Drive/best_model_cifar10.h5\n",
            "1563/1563 [==============================] - 57s 37ms/step - loss: 0.2291 - accuracy: 0.9285 - val_loss: 0.5326 - val_accuracy: 0.8287\n",
            "Epoch 21/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.2137 - accuracy: 0.9322\n",
            "Epoch 21: val_accuracy did not improve from 0.82870\n",
            "1563/1563 [==============================] - 57s 36ms/step - loss: 0.2137 - accuracy: 0.9322 - val_loss: 0.5511 - val_accuracy: 0.8203\n",
            "Epoch 22/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.1975 - accuracy: 0.9388\n",
            "Epoch 22: val_accuracy did not improve from 0.82870\n",
            "1563/1563 [==============================] - 57s 36ms/step - loss: 0.1975 - accuracy: 0.9388 - val_loss: 0.6149 - val_accuracy: 0.8016\n",
            "Epoch 23/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.1880 - accuracy: 0.9422\n",
            "Epoch 23: val_accuracy did not improve from 0.82870\n",
            "1563/1563 [==============================] - 58s 37ms/step - loss: 0.1880 - accuracy: 0.9422 - val_loss: 0.5943 - val_accuracy: 0.8113\n",
            "Epoch 24/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.1784 - accuracy: 0.9437\n",
            "Epoch 24: val_accuracy did not improve from 0.82870\n",
            "1563/1563 [==============================] - 57s 36ms/step - loss: 0.1784 - accuracy: 0.9437 - val_loss: 0.5375 - val_accuracy: 0.8287\n",
            "Epoch 25/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.1628 - accuracy: 0.9490\n",
            "Epoch 25: val_accuracy did not improve from 0.82870\n",
            "1563/1563 [==============================] - 59s 37ms/step - loss: 0.1628 - accuracy: 0.9490 - val_loss: 1.0206 - val_accuracy: 0.7114\n",
            "Epoch 26/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.1550 - accuracy: 0.9523\n",
            "Epoch 26: val_accuracy did not improve from 0.82870\n",
            "1563/1563 [==============================] - 57s 36ms/step - loss: 0.1550 - accuracy: 0.9523 - val_loss: 0.5636 - val_accuracy: 0.8263\n",
            "Epoch 27/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.1470 - accuracy: 0.9548\n",
            "Epoch 27: val_accuracy did not improve from 0.82870\n",
            "1563/1563 [==============================] - 58s 37ms/step - loss: 0.1470 - accuracy: 0.9548 - val_loss: 0.6990 - val_accuracy: 0.7870\n",
            "Epoch 28/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.1400 - accuracy: 0.9585\n",
            "Epoch 28: val_accuracy did not improve from 0.82870\n",
            "1563/1563 [==============================] - 58s 37ms/step - loss: 0.1400 - accuracy: 0.9585 - val_loss: 0.5825 - val_accuracy: 0.8187\n",
            "Epoch 29/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.1297 - accuracy: 0.9606\n",
            "Epoch 29: val_accuracy did not improve from 0.82870\n",
            "1563/1563 [==============================] - 57s 36ms/step - loss: 0.1297 - accuracy: 0.9606 - val_loss: 0.6786 - val_accuracy: 0.7990\n",
            "Epoch 30/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.1231 - accuracy: 0.9622\n",
            "Epoch 30: val_accuracy did not improve from 0.82870\n",
            "1563/1563 [==============================] - 58s 37ms/step - loss: 0.1231 - accuracy: 0.9622 - val_loss: 0.7188 - val_accuracy: 0.7875\n",
            "Epoch 31/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.1154 - accuracy: 0.9647\n",
            "Epoch 31: val_accuracy did not improve from 0.82870\n",
            "1563/1563 [==============================] - 58s 37ms/step - loss: 0.1154 - accuracy: 0.9647 - val_loss: 0.7417 - val_accuracy: 0.7844\n",
            "Epoch 32/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.1155 - accuracy: 0.9636\n",
            "Epoch 32: val_accuracy did not improve from 0.82870\n",
            "1563/1563 [==============================] - 59s 38ms/step - loss: 0.1155 - accuracy: 0.9636 - val_loss: 0.6031 - val_accuracy: 0.8196\n",
            "Epoch 33/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.1068 - accuracy: 0.9681\n",
            "Epoch 33: val_accuracy improved from 0.82870 to 0.84960, saving model to /content/drive/My Drive/best_model_cifar10.h5\n",
            "1563/1563 [==============================] - 59s 38ms/step - loss: 0.1068 - accuracy: 0.9681 - val_loss: 0.4779 - val_accuracy: 0.8496\n",
            "Epoch 34/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.1008 - accuracy: 0.9698\n",
            "Epoch 34: val_accuracy did not improve from 0.84960\n",
            "1563/1563 [==============================] - 57s 36ms/step - loss: 0.1008 - accuracy: 0.9698 - val_loss: 0.6942 - val_accuracy: 0.7966\n",
            "Epoch 35/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.0982 - accuracy: 0.9701\n",
            "Epoch 35: val_accuracy did not improve from 0.84960\n",
            "1563/1563 [==============================] - 57s 36ms/step - loss: 0.0982 - accuracy: 0.9701 - val_loss: 0.5532 - val_accuracy: 0.8254\n",
            "Epoch 36/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.0931 - accuracy: 0.9729\n",
            "Epoch 36: val_accuracy did not improve from 0.84960\n",
            "1563/1563 [==============================] - 57s 36ms/step - loss: 0.0931 - accuracy: 0.9729 - val_loss: 0.5597 - val_accuracy: 0.8276\n",
            "Epoch 37/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.0943 - accuracy: 0.9726\n",
            "Epoch 37: val_accuracy did not improve from 0.84960\n",
            "1563/1563 [==============================] - 58s 37ms/step - loss: 0.0943 - accuracy: 0.9726 - val_loss: 0.5675 - val_accuracy: 0.8361\n",
            "Epoch 38/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.0923 - accuracy: 0.9724\n",
            "Epoch 38: val_accuracy did not improve from 0.84960\n",
            "1563/1563 [==============================] - 57s 37ms/step - loss: 0.0923 - accuracy: 0.9724 - val_loss: 0.6716 - val_accuracy: 0.8144\n",
            "Epoch 39/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.0868 - accuracy: 0.9733\n",
            "Epoch 39: val_accuracy did not improve from 0.84960\n",
            "1563/1563 [==============================] - 57s 37ms/step - loss: 0.0868 - accuracy: 0.9733 - val_loss: 0.6844 - val_accuracy: 0.8029\n",
            "Epoch 40/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.0797 - accuracy: 0.9774\n",
            "Epoch 40: val_accuracy did not improve from 0.84960\n",
            "1563/1563 [==============================] - 57s 36ms/step - loss: 0.0797 - accuracy: 0.9774 - val_loss: 0.9595 - val_accuracy: 0.7537\n",
            "Epoch 41/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.0762 - accuracy: 0.9779\n",
            "Epoch 41: val_accuracy did not improve from 0.84960\n",
            "1563/1563 [==============================] - 59s 37ms/step - loss: 0.0762 - accuracy: 0.9779 - val_loss: 0.5323 - val_accuracy: 0.8462\n",
            "Epoch 42/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.0768 - accuracy: 0.9769\n",
            "Epoch 42: val_accuracy did not improve from 0.84960\n",
            "1563/1563 [==============================] - 59s 38ms/step - loss: 0.0768 - accuracy: 0.9769 - val_loss: 0.5905 - val_accuracy: 0.8323\n",
            "Epoch 43/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.0761 - accuracy: 0.9773\n",
            "Epoch 43: val_accuracy did not improve from 0.84960\n",
            "1563/1563 [==============================] - 59s 38ms/step - loss: 0.0761 - accuracy: 0.9773 - val_loss: 0.6327 - val_accuracy: 0.8217\n",
            "Epoch 44/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.0652 - accuracy: 0.9808\n",
            "Epoch 44: val_accuracy improved from 0.84960 to 0.85960, saving model to /content/drive/My Drive/best_model_cifar10.h5\n",
            "1563/1563 [==============================] - 59s 38ms/step - loss: 0.0652 - accuracy: 0.9808 - val_loss: 0.4860 - val_accuracy: 0.8596\n",
            "Epoch 45/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.0654 - accuracy: 0.9817\n",
            "Epoch 45: val_accuracy did not improve from 0.85960\n",
            "1563/1563 [==============================] - 59s 38ms/step - loss: 0.0654 - accuracy: 0.9817 - val_loss: 0.5597 - val_accuracy: 0.8420\n",
            "Epoch 46/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.0694 - accuracy: 0.9791\n",
            "Epoch 46: val_accuracy did not improve from 0.85960\n",
            "1563/1563 [==============================] - 57s 36ms/step - loss: 0.0694 - accuracy: 0.9791 - val_loss: 0.6887 - val_accuracy: 0.8150\n",
            "Epoch 47/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.0625 - accuracy: 0.9825\n",
            "Epoch 47: val_accuracy did not improve from 0.85960\n",
            "1563/1563 [==============================] - 57s 36ms/step - loss: 0.0625 - accuracy: 0.9825 - val_loss: 0.7871 - val_accuracy: 0.7999\n",
            "Epoch 48/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.0621 - accuracy: 0.9817\n",
            "Epoch 48: val_accuracy did not improve from 0.85960\n",
            "1563/1563 [==============================] - 59s 38ms/step - loss: 0.0621 - accuracy: 0.9817 - val_loss: 0.5436 - val_accuracy: 0.8404\n",
            "Epoch 49/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.0610 - accuracy: 0.9825\n",
            "Epoch 49: val_accuracy did not improve from 0.85960\n",
            "1563/1563 [==============================] - 59s 38ms/step - loss: 0.0610 - accuracy: 0.9825 - val_loss: 0.6571 - val_accuracy: 0.8228\n",
            "Epoch 50/50\n",
            "1563/1563 [==============================] - ETA: 0s - loss: 0.0601 - accuracy: 0.9827\n",
            "Epoch 50: val_accuracy did not improve from 0.85960\n",
            "1563/1563 [==============================] - 59s 38ms/step - loss: 0.0601 - accuracy: 0.9827 - val_loss: 0.5441 - val_accuracy: 0.8465\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # #determine best epoch\n",
        "val_acc_per_epoch = history.history['val_accuracy']\n",
        "best_epoch = val_acc_per_epoch.index(max(val_acc_per_epoch)) + 1\n",
        "print('Best epoch accuracy: %d' % (best_epoch,))\n",
        "val_acc_per_epoch = history.history['val_loss']\n",
        "best_epoch = val_acc_per_epoch.index(min(val_acc_per_epoch)) + 1\n",
        "print('Best epoch loss: %d' % (best_epoch,))\n",
        "\n",
        "# #plot loss\n",
        "history_frame = pd.DataFrame(history.history)\n",
        "history_frame.loc[:, ['loss', 'val_loss']].plot()\n",
        "history_frame.loc[:, ['accuracy', 'val_accuracy']].plot();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "83j2fR8JM4W1",
        "outputId": "ffdc33ce-9a2b-46f9-bce3-2f0c431bd8e9"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best epoch accuracy: 44\n",
            "Best epoch loss: 33\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#get best model from Training\n",
        "model=load_model('/content/drive/My Drive/best_model_cifar10.h5')\n",
        "\n",
        "#predict \n",
        "\n",
        "def plot_cm(labels, predictions):\n",
        "  cm = confusion_matrix(labels, predictions)\n",
        "  plt.figure(figsize=(10,10))\n",
        "  sns.heatmap(cm, annot=True, fmt=\"d\")\n",
        "  plt.title('Confusion matrix')\n",
        "  plt.ylabel('Actual label')\n",
        "  plt.xlabel('Predicted label')\n",
        "\n",
        "baseline_results = model.evaluate((x_test), y_test,verbose=0)\n",
        "for name, value in zip(model.metrics_names, baseline_results):\n",
        "  print(name, ': ', value)\n",
        "print()\n",
        "\n",
        "test_predictions_baseline = model.predict((x_test),verbose=0)\n",
        "predicted_classes = test_predictions_baseline.argmax(axis=1)\n",
        "plot_cm(y_test.argmax(axis=1), predicted_classes)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "id": "QUOW438tYUJO",
        "outputId": "7508f4be-d73e-4ca5-ea2b-ba24ea413c5c"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loss :  0.48595181107521057\n",
            "accuracy :  0.8596000075340271\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}