{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "3cEMJ4CNPwG7",
        "outputId": "3080f99c-2b70-4a4c-aeb7-6db6b5f003af"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "import os\n",
        "os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab3')\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras import layers\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.metrics import ConfusionMatrixDisplay"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# загрузка датасета\n",
        "from keras.datasets import cifar10\n",
        "(X_train, y_train), (X_test, y_test) = cifar10.load_data()"
      ],
      "metadata": {
        "id": "JoDKLOdXRCsT"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# создание своего разбиения датасета\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "# объединяем в один набор\n",
        "X = np.concatenate((X_train, X_test))\n",
        "y = np.concatenate((y_train, y_test))\n",
        "\n",
        "# разбиваем по вариантам\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
        "                                                    test_size = 10000,\n",
        "                                                    train_size = 50000,\n",
        "                                                    random_state = 7)\n",
        "# вывод размерностей\n",
        "print('Shape of X train:', X_train.shape)\n",
        "print('Shape of y train:', y_train.shape)\n",
        "print('Shape of X test:', X_test.shape)\n",
        "print('Shape of y test:', y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "hAgd3b8BRLhO",
        "outputId": "1ae5326d-e09b-4204-9acf-b6cf82aa57d6"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X train: (50000, 32, 32, 3)\n",
            "Shape of y train: (50000, 1)\n",
            "Shape of X test: (10000, 32, 32, 3)\n",
            "Shape of y test: (10000, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
        " 'dog', 'frog', 'horse', 'ship', 'truck']\n",
        "plt.figure(figsize=(10,10))\n",
        "for i in range(25):\n",
        " plt.subplot(5,5,i+1)\n",
        " plt.xticks([])\n",
        " plt.yticks([])\n",
        " plt.grid(False)\n",
        " plt.imshow(X_train[i])\n",
        " plt.xlabel(class_names[y_train[i][0]])\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        },
        "collapsed": true,
        "id": "_8ijo_GZSWKs",
        "outputId": "f74c2604-2bf9-4ece-c6ae-4751049a882e"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 25 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Зададим параметры данных и модели\n",
        "num_classes = 10\n",
        "input_shape = (32, 32, 3)\n",
        "\n",
        "# Приведение входных данных к диапазону [0, 1]\n",
        "X_train = X_train / 255\n",
        "X_test = X_test / 255\n",
        "\n",
        "print('Shape of transformed X train:', X_train.shape)\n",
        "print('Shape of transformed X test:', X_test.shape)\n",
        "\n",
        "# переведем метки в one-hot\n",
        "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
        "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
        "print('Shape of transformed y train:', y_train.shape)\n",
        "print('Shape of transformed y test:', y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "iz2dCYrqS0Wb",
        "outputId": "e5678c4c-3776-42c5-ea44-325f79f2f659"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of transformed X train: (50000, 32, 32, 3)\n",
            "Shape of transformed X test: (10000, 32, 32, 3)\n",
            "Shape of transformed y train: (50000, 10)\n",
            "Shape of transformed y test: (10000, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# создаем модель\n",
        "model = Sequential()\n",
        "\n",
        "# Блок 1\n",
        "model.add(layers.Conv2D(32, (3, 3), padding=\"same\",\n",
        "                        activation=\"relu\", input_shape=input_shape))\n",
        "model.add(layers.BatchNormalization())\n",
        "model.add(layers.Conv2D(32, (3, 3), padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.BatchNormalization())\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Dropout(0.25))\n",
        "\n",
        "# Блок 2\n",
        "model.add(layers.Conv2D(64, (3, 3), padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.BatchNormalization())\n",
        "model.add(layers.Conv2D(64, (3, 3), padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.BatchNormalization())\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Dropout(0.25))\n",
        "\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(128, activation='relu'))\n",
        "model.add(layers.Dropout(0.5))\n",
        "model.add(layers.Dense(num_classes, activation=\"softmax\"))\n",
        "\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 780
        },
        "collapsed": true,
        "id": "jVwoiDdCXfWI",
        "outputId": "43237a3c-34b6-44bd-ac2f-47682f690330"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_4\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_12 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m896\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_6           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m128\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_13 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │         \u001b[38;5;34m9,248\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_7           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m128\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_9 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_6 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_14 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_8           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m256\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_15 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m36,928\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_9           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m256\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_10 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_7 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten_3 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │       \u001b[38;5;34m524,416\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_8 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,290\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_6           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_7           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_8           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_9           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">524,416</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m592,042\u001b[0m (2.26 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">592,042</span> (2.26 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m591,658\u001b[0m (2.26 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">591,658</span> (2.26 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m384\u001b[0m (1.50 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">384</span> (1.50 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 64\n",
        "epochs = 50\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
        "model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "5OAdCMZ2Xjac",
        "outputId": "6babf73d-ec5c-41fb-9141-02f5b66bec19"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 18ms/step - accuracy: 0.2869 - loss: 2.0611 - val_accuracy: 0.4912 - val_loss: 1.3440\n",
            "Epoch 2/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.4823 - loss: 1.4245 - val_accuracy: 0.5306 - val_loss: 1.3164\n",
            "Epoch 3/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 8ms/step - accuracy: 0.5579 - loss: 1.2440 - val_accuracy: 0.6416 - val_loss: 0.9811\n",
            "Epoch 4/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.6049 - loss: 1.1170 - val_accuracy: 0.6588 - val_loss: 0.9544\n",
            "Epoch 5/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.6420 - loss: 1.0236 - val_accuracy: 0.7096 - val_loss: 0.8580\n",
            "Epoch 6/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.6799 - loss: 0.9205 - val_accuracy: 0.6866 - val_loss: 0.9137\n",
            "Epoch 7/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.6960 - loss: 0.8744 - val_accuracy: 0.7164 - val_loss: 0.7933\n",
            "Epoch 8/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7182 - loss: 0.8150 - val_accuracy: 0.7492 - val_loss: 0.7311\n",
            "Epoch 9/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 8ms/step - accuracy: 0.7266 - loss: 0.7832 - val_accuracy: 0.7552 - val_loss: 0.7084\n",
            "Epoch 10/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.7434 - loss: 0.7428 - val_accuracy: 0.7456 - val_loss: 0.7759\n",
            "Epoch 11/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7521 - loss: 0.7132 - val_accuracy: 0.7662 - val_loss: 0.6787\n",
            "Epoch 12/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 8ms/step - accuracy: 0.7676 - loss: 0.6744 - val_accuracy: 0.7694 - val_loss: 0.6662\n",
            "Epoch 13/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7746 - loss: 0.6513 - val_accuracy: 0.7818 - val_loss: 0.6265\n",
            "Epoch 14/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.7857 - loss: 0.6170 - val_accuracy: 0.7862 - val_loss: 0.6263\n",
            "Epoch 15/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 8ms/step - accuracy: 0.7898 - loss: 0.6089 - val_accuracy: 0.7670 - val_loss: 0.6986\n",
            "Epoch 16/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 8ms/step - accuracy: 0.7923 - loss: 0.5927 - val_accuracy: 0.7836 - val_loss: 0.6244\n",
            "Epoch 17/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.8020 - loss: 0.5676 - val_accuracy: 0.7718 - val_loss: 0.6886\n",
            "Epoch 18/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.8127 - loss: 0.5410 - val_accuracy: 0.7874 - val_loss: 0.6365\n",
            "Epoch 19/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.8199 - loss: 0.5235 - val_accuracy: 0.7916 - val_loss: 0.6148\n",
            "Epoch 20/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.8194 - loss: 0.5110 - val_accuracy: 0.8006 - val_loss: 0.6143\n",
            "Epoch 21/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 8ms/step - accuracy: 0.8252 - loss: 0.5031 - val_accuracy: 0.7932 - val_loss: 0.6536\n",
            "Epoch 22/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.8332 - loss: 0.4793 - val_accuracy: 0.8000 - val_loss: 0.6312\n",
            "Epoch 23/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.8351 - loss: 0.4699 - val_accuracy: 0.7950 - val_loss: 0.6454\n",
            "Epoch 24/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.8417 - loss: 0.4573 - val_accuracy: 0.7992 - val_loss: 0.6198\n",
            "Epoch 25/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.8458 - loss: 0.4401 - val_accuracy: 0.7964 - val_loss: 0.6369\n",
            "Epoch 26/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.8467 - loss: 0.4380 - val_accuracy: 0.7906 - val_loss: 0.6787\n",
            "Epoch 27/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 8ms/step - accuracy: 0.8537 - loss: 0.4269 - val_accuracy: 0.7964 - val_loss: 0.6376\n",
            "Epoch 28/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.8533 - loss: 0.4183 - val_accuracy: 0.8022 - val_loss: 0.6392\n",
            "Epoch 29/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.8561 - loss: 0.4099 - val_accuracy: 0.8194 - val_loss: 0.5746\n",
            "Epoch 30/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.8590 - loss: 0.4008 - val_accuracy: 0.8114 - val_loss: 0.5972\n",
            "Epoch 31/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 8ms/step - accuracy: 0.8674 - loss: 0.3831 - val_accuracy: 0.8142 - val_loss: 0.6090\n",
            "Epoch 32/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 8ms/step - accuracy: 0.8669 - loss: 0.3852 - val_accuracy: 0.8098 - val_loss: 0.6277\n",
            "Epoch 33/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.8669 - loss: 0.3798 - val_accuracy: 0.8112 - val_loss: 0.5966\n",
            "Epoch 34/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.8685 - loss: 0.3794 - val_accuracy: 0.8194 - val_loss: 0.5824\n",
            "Epoch 35/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.8719 - loss: 0.3688 - val_accuracy: 0.8162 - val_loss: 0.6381\n",
            "Epoch 36/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.8718 - loss: 0.3660 - val_accuracy: 0.8168 - val_loss: 0.5981\n",
            "Epoch 37/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.8785 - loss: 0.3505 - val_accuracy: 0.8054 - val_loss: 0.6218\n",
            "Epoch 38/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 8ms/step - accuracy: 0.8812 - loss: 0.3460 - val_accuracy: 0.8158 - val_loss: 0.6058\n",
            "Epoch 39/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.8805 - loss: 0.3441 - val_accuracy: 0.8184 - val_loss: 0.5941\n",
            "Epoch 40/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.8817 - loss: 0.3422 - val_accuracy: 0.8146 - val_loss: 0.6009\n",
            "Epoch 41/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.8766 - loss: 0.3513 - val_accuracy: 0.8166 - val_loss: 0.5945\n",
            "Epoch 42/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.8840 - loss: 0.3354 - val_accuracy: 0.8178 - val_loss: 0.6113\n",
            "Epoch 43/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.8872 - loss: 0.3287 - val_accuracy: 0.8278 - val_loss: 0.5791\n",
            "Epoch 44/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 8ms/step - accuracy: 0.8890 - loss: 0.3196 - val_accuracy: 0.8126 - val_loss: 0.6564\n",
            "Epoch 45/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.8871 - loss: 0.3257 - val_accuracy: 0.8252 - val_loss: 0.5983\n",
            "Epoch 46/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.8906 - loss: 0.3150 - val_accuracy: 0.8122 - val_loss: 0.6215\n",
            "Epoch 47/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.8908 - loss: 0.3083 - val_accuracy: 0.8226 - val_loss: 0.5767\n",
            "Epoch 48/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.8921 - loss: 0.3090 - val_accuracy: 0.8194 - val_loss: 0.6160\n",
            "Epoch 49/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.8921 - loss: 0.3063 - val_accuracy: 0.8238 - val_loss: 0.6257\n",
            "Epoch 50/50\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.8962 - loss: 0.3017 - val_accuracy: 0.8230 - val_loss: 0.6093\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x786494d78620>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores = model.evaluate(X_test, y_test)\n",
        "print('Loss on test data:', scores[0])\n",
        "print('Accuracy on test data:', scores[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "4sLNTO4MZDJ8",
        "outputId": "b7fdd048-71ad-4247-d941-3d73e377745c"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.8182 - loss: 0.6295\n",
            "Loss on test data: 0.635892927646637\n",
            "Accuracy on test data: 0.8194000124931335\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for n in [5,17]:\n",
        "  result = model.predict(X_test[n:n+1])\n",
        "  print('NN output:', result)\n",
        "\n",
        "  plt.imshow(X_test[n].reshape(32,32,3), cmap=plt.get_cmap('gray'))\n",
        "  plt.show()\n",
        "  print('Real mark: ', np.argmax(y_test[n]))\n",
        "  print('NN answer: ', np.argmax(result))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "collapsed": true,
        "id": "EI4gFAF2ZGcb",
        "outputId": "da26844a-7e4a-4e34-8b87-40ca6ee0a2c5"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step\n",
            "NN output: [[0.19707698 0.12492625 0.35796925 0.08262431 0.0519276  0.01125633\n",
            "  0.04046627 0.03933177 0.03143692 0.0629843 ]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Real mark:  0\n",
            "NN answer:  2\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
            "NN output: [[6.0066624e-05 5.0396560e-05 4.3396626e-02 7.0300132e-02 1.8314796e-03\n",
            "  8.7599868e-01 8.3906803e-04 7.5012995e-03 1.6654752e-05 5.6444887e-06]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Real mark:  5\n",
            "NN answer:  5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# истинные метки классов\n",
        "true_labels = np.argmax(y_test, axis=1)\n",
        "# предсказанные метки классов\n",
        "predicted_labels = np.argmax(model.predict(X_test), axis=1)\n",
        "\n",
        "# отчет о качестве классификации\n",
        "print(classification_report(true_labels, predicted_labels, target_names=class_names))\n",
        "# вычисление матрицы ошибок\n",
        "conf_matrix = confusion_matrix(true_labels, predicted_labels)\n",
        "# отрисовка матрицы ошибок в виде \"тепловой карты\"\n",
        "fig, ax = plt.subplots(figsize=(6, 6))\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix,display_labels=class_names)\n",
        "disp.plot(ax=ax, xticks_rotation=45)  # поворот подписей по X и приятная палитра\n",
        "plt.tight_layout()  # чтобы всё влезло\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 901
        },
        "collapsed": true,
        "id": "owZ0FasEZzmK",
        "outputId": "535f65f0-5073-4145-fd18-02030b361a75"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    airplane       0.85      0.86      0.86      1013\n",
            "  automobile       0.93      0.91      0.92       989\n",
            "        bird       0.75      0.75      0.75      1018\n",
            "         cat       0.69      0.66      0.67      1049\n",
            "        deer       0.79      0.78      0.78      1009\n",
            "         dog       0.73      0.68      0.71       978\n",
            "        frog       0.79      0.90      0.84       981\n",
            "       horse       0.88      0.84      0.86       986\n",
            "        ship       0.89      0.92      0.91      1029\n",
            "       truck       0.88      0.91      0.89       948\n",
            "\n",
            "    accuracy                           0.82     10000\n",
            "   macro avg       0.82      0.82      0.82     10000\n",
            "weighted avg       0.82      0.82      0.82     10000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}