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+{
+  "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": "markdown",
+      "source": [
+        "## Задание 1"
+      ],
+      "metadata": {
+        "id": "oZs0KGcz01BY"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 1) В среде Google Colab создали новый блокнот (notebook). Импортировали необходимые для работы библиотеки и модули."
+      ],
+      "metadata": {
+        "id": "gz18QPRz03Ec"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# импорт модулей\n",
+        "import os\n",
+        "os.mkdir('/content/drive/MyDrive/Colab Notebooks/is_lab3')\n",
+        "os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab3')\n",
+        "\n",
+        "from tensorflow import keras\n",
+        "from tensorflow.keras import layers\n",
+        "from tensorflow.keras.models import Sequential\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"
+      ],
+      "metadata": {
+        "id": "mr9IszuQ1ANG"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4H9UW0x9aaEQ",
+        "outputId": "264ebd43-1773-4874-fbac-98ae491f292c"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2) Загрузили набор данных MNIST, содержащий размеченные изображения рукописных цифр.  "
+      ],
+      "metadata": {
+        "id": "FFRtE0TN1AiA"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# загрузка датасета\n",
+        "from keras.datasets import mnist\n",
+        "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
+      ],
+      "metadata": {
+        "id": "Ixw5Sp0_1A-w"
+      },
+      "execution_count": 43,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3) Разбили набор данных на обучающие и тестовые данные в соотношении 60 000:10 000 элементов. Параметр random_state выбрали равным (4k – 1)=15, где k=4 –номер бригады. Вывели размерности полученных обучающих и тестовых массивов данных."
+      ],
+      "metadata": {
+        "id": "aCo_lUXl1BPV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# создание своего разбиения датасета\n",
+        "from sklearn.model_selection import train_test_split\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 = 60000,\n",
+        "                                                    random_state = 15)\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": {
+        "id": "BrSjcpEe1BeV",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0299c15c-a632-4c99-8f60-f5a6f331a7dd"
+      },
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Shape of X train: (60000, 28, 28)\n",
+            "Shape of y train: (60000,)\n",
+            "Shape of X test: (10000, 28, 28)\n",
+            "Shape of y test: (10000,)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 4) Провели предобработку данных: привели обучающие и тестовые данные к формату, пригодному для обучения сверточной нейронной сети. Входные данные принимают значения от 0 до 1, метки цифр закодированы по принципу «one-hot encoding». Вывели размерности предобработанных обучающих и тестовых массивов данных."
+      ],
+      "metadata": {
+        "id": "4hclnNaD1BuB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Зададим параметры данных и модели\n",
+        "num_classes = 10\n",
+        "input_shape = (28, 28, 1)\n",
+        "\n",
+        "# Приведение входных данных к диапазону [0, 1]\n",
+        "X_train = X_train / 255\n",
+        "X_test = X_test / 255\n",
+        "\n",
+        "# Расширяем размерность входных данных, чтобы каждое изображение имело\n",
+        "# размерность (высота, ширина, количество каналов)\n",
+        "\n",
+        "X_train = np.expand_dims(X_train, -1)\n",
+        "X_test = np.expand_dims(X_test, -1)\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": {
+        "id": "xJH87ISq1B9h",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4aa0f492-359c-4ac8-dd85-e66fa8757751"
+      },
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Shape of transformed X train: (60000, 28, 28, 1)\n",
+            "Shape of transformed X test: (10000, 28, 28, 1)\n",
+            "Shape of transformed y train: (60000, 10)\n",
+            "Shape of transformed y test: (10000, 10)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 5) Реализовали модель сверточной нейронной сети и обучили ее на обучающих данных с выделением части обучающих данных в качестве валидационных. Вывели информацию об архитектуре нейронной сети."
+      ],
+      "metadata": {
+        "id": "7x99O8ig1CLh"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# создаем модель\n",
+        "model = Sequential()\n",
+        "model.add(layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\", input_shape=input_shape))\n",
+        "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
+        "model.add(layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"))\n",
+        "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
+        "model.add(layers.Dropout(0.5))\n",
+        "model.add(layers.Flatten())\n",
+        "model.add(layers.Dense(num_classes, activation=\"softmax\"))\n",
+        "\n",
+        "model.summary()"
+      ],
+      "metadata": {
+        "id": "Un561zSH1Cmv",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 353
+        },
+        "outputId": "02469669-3b81-49ba-b5ea-6bbf019a13d9"
+      },
+      "execution_count": 46,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"sequential_3\"\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_3\"</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_10 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m320\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_7 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_11 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_8 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m64\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;34m5\u001b[0m, \u001b[38;5;34m5\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;34m1600\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │        \u001b[38;5;34m16,010\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_10 (<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\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_7 (<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\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_11 (<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\">11</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_8 (<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\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</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\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</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\">1600</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_4 (<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\">16,010</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m34,826\u001b[0m (136.04 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\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">34,826</span> (136.04 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m34,826\u001b[0m (136.04 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\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">34,826</span> (136.04 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\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\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# компилируем и обучаем модель\n",
+        "batch_size = 512\n",
+        "epochs = 15\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": {
+        "id": "q_h8PxkN9m0v",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "62f45088-0758-4ef0-a718-e0ab53195750"
+      },
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/15\n",
+            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 35ms/step - accuracy: 0.6005 - loss: 1.3162 - val_accuracy: 0.9470 - val_loss: 0.1911\n",
+            "Epoch 2/15\n",
+            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9392 - loss: 0.2061 - val_accuracy: 0.9640 - val_loss: 0.1177\n",
+            "Epoch 3/15\n",
+            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9598 - loss: 0.1344 - val_accuracy: 0.9728 - val_loss: 0.0931\n",
+            "Epoch 4/15\n",
+            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9671 - loss: 0.1093 - val_accuracy: 0.9783 - val_loss: 0.0776\n",
+            "Epoch 5/15\n",
+            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9727 - loss: 0.0872 - val_accuracy: 0.9798 - val_loss: 0.0676\n",
+            "Epoch 6/15\n",
+            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9738 - loss: 0.0834 - val_accuracy: 0.9803 - val_loss: 0.0614\n",
+            "Epoch 7/15\n",
+            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9801 - loss: 0.0671 - val_accuracy: 0.9830 - val_loss: 0.0555\n",
+            "Epoch 8/15\n",
+            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9794 - loss: 0.0665 - val_accuracy: 0.9847 - val_loss: 0.0515\n",
+            "Epoch 9/15\n",
+            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9810 - loss: 0.0598 - val_accuracy: 0.9847 - val_loss: 0.0485\n",
+            "Epoch 10/15\n",
+            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9813 - loss: 0.0578 - val_accuracy: 0.9853 - val_loss: 0.0463\n",
+            "Epoch 11/15\n",
+            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9834 - loss: 0.0535 - val_accuracy: 0.9853 - val_loss: 0.0441\n",
+            "Epoch 12/15\n",
+            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9845 - loss: 0.0492 - val_accuracy: 0.9867 - val_loss: 0.0419\n",
+            "Epoch 13/15\n",
+            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9842 - loss: 0.0491 - val_accuracy: 0.9867 - val_loss: 0.0417\n",
+            "Epoch 14/15\n",
+            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9859 - loss: 0.0441 - val_accuracy: 0.9877 - val_loss: 0.0401\n",
+            "Epoch 15/15\n",
+            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9853 - loss: 0.0446 - val_accuracy: 0.9877 - val_loss: 0.0382\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.history.History at 0x7ef4d83bbfb0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 47
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 6) Оценили качество обучения на тестовых данных. Вывели значение функции ошибки и значение метрики качества классификации на тестовых данных."
+      ],
+      "metadata": {
+        "id": "HL2_LVga1C3l"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Оценка качества работы модели на тестовых данных\n",
+        "scores = model.evaluate(X_test, y_test)\n",
+        "print('Loss on test data:', scores[0])\n",
+        "print('Accuracy on test data:', scores[1])"
+      ],
+      "metadata": {
+        "id": "81Cgq8dn9uL6",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4aa1724a-c7e7-4e86-8738-9db4cfdd8282"
+      },
+      "execution_count": 48,
+      "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: 0.9895 - loss: 0.0345\n",
+            "Loss on test data: 0.035905033349990845\n",
+            "Accuracy on test data: 0.988099992275238\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 7) Подали на вход обученной модели два тестовых изображения. Вывели изображения, истинные метки и результаты распознавания."
+      ],
+      "metadata": {
+        "id": "KzrVY1SR1DZh"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# вывод двух тестовых изображений и результатов распознавания\n",
+        "\n",
+        "for n in [15, 16]:\n",
+        "  result = model.predict(X_test[n:n+1])\n",
+        "  print('NN output:', result)\n",
+        "\n",
+        "  plt.imshow(X_test[n].reshape(28,28), 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": {
+        "id": "dbfkWjDI1Dp7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "0cc5e03c-4f4c-4792-fb4c-4eaf54d9d7b6"
+      },
+      "execution_count": 49,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 342ms/step\n",
+            "NN output: [[3.2891677e-07 9.9978304e-01 4.7009278e-05 3.9200216e-07 1.5089162e-04\n",
+            "  1.8456345e-09 4.2153893e-08 6.1042369e-06 1.2237400e-05 8.7371088e-09]]\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:  1\n",
+            "NN answer:  1\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "NN output: [[9.9996102e-01 1.2646287e-15 1.2460175e-08 2.6192890e-08 2.9560595e-16\n",
+            "  3.1950063e-07 7.6320879e-07 5.2810489e-11 2.4268709e-07 3.7699298e-05]]\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:  0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 8) Вывели отчет о качестве классификации тестовой выборки и матрицу ошибок для тестовой выборки."
+      ],
+      "metadata": {
+        "id": "YgiVGr5_1D3u"
+      }
+    },
+    {
+      "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))\n",
+        "# вычисление матрицы ошибок\n",
+        "conf_matrix = confusion_matrix(true_labels, predicted_labels)\n",
+        "# отрисовка матрицы ошибок в виде \"тепловой карты\"\n",
+        "display = ConfusionMatrixDisplay(confusion_matrix=conf_matrix)\n",
+        "display.plot()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "7MqcG_wl1EHI",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 761
+        },
+        "outputId": "0510223e-d46f-4437-f188-2eb12b10ea26"
+      },
+      "execution_count": 50,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.99      0.99      0.99       994\n",
+            "           1       0.99      0.99      0.99      1194\n",
+            "           2       0.98      0.99      0.98       975\n",
+            "           3       0.99      0.99      0.99      1031\n",
+            "           4       0.98      0.99      0.99       967\n",
+            "           5       0.99      0.99      0.99       937\n",
+            "           6       0.99      0.99      0.99       964\n",
+            "           7       0.99      0.99      0.99       998\n",
+            "           8       0.98      0.98      0.98       965\n",
+            "           9       0.99      0.98      0.98       975\n",
+            "\n",
+            "    accuracy                           0.99     10000\n",
+            "   macro avg       0.99      0.99      0.99     10000\n",
+            "weighted avg       0.99      0.99      0.99     10000\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 9) Загрузили, предобработали и подали на вход обученной нейронной сети собственное изображение, созданное при выполнении лабораторной работы №1. Вывели изображение и результат распознавания."
+      ],
+      "metadata": {
+        "id": "amaspXGW1EVy"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# загрузка собственного изображения\n",
+        "from PIL import Image\n",
+        "\n",
+        "for name_image in ['1.png', '2.png']:\n",
+        "  file_data = Image.open(name_image)\n",
+        "  file_data = file_data.convert('L') # перевод в градации серого\n",
+        "  test_img = np.array(file_data)\n",
+        "\n",
+        "  # вывод собственного изображения\n",
+        "  plt.imshow(test_img, cmap=plt.get_cmap('gray'))\n",
+        "  plt.show()\n",
+        "\n",
+        "  # предобработка\n",
+        "  test_img = test_img / 255\n",
+        "  test_img = np.reshape(test_img, (1,28,28,1))\n",
+        "\n",
+        "  # распознавание\n",
+        "  result = model.predict(test_img)\n",
+        "  print('I think it\\'s', np.argmax(result))"
+      ],
+      "metadata": {
+        "id": "ktWEeqWd1EyF",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 912
+        },
+        "outputId": "f4bb8d7e-3fe8-413c-94c9-63ae905fe5d8"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "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": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step\n",
+            "I think it's 1\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": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
+            "I think it's 2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 10) Загрузили с диска модель, сохраненную при выполнении лабораторной работы №1. Вывели информацию об архитектуре модели. Повторили для этой модели п. 6."
+      ],
+      "metadata": {
+        "id": "mgrihPd61E8w"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_lr1 = keras.models.load_model(\"best_model.keras\")\n",
+        "\n",
+        "model_lr1.summary()"
+      ],
+      "metadata": {
+        "id": "DblXqn3l1FL2",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 209
+        },
+        "outputId": "26b7ca21-8bda-4673-e87c-62a050edde72"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"sequential_9\"\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_9\"</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",
+              "│ dense_20 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m78,500\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_21 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,010\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",
+              "│ dense_20 (<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\">100</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">78,500</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_21 (<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\">1,010</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m79,512\u001b[0m (310.60 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\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">79,512</span> (310.60 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m79,510\u001b[0m (310.59 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\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">79,510</span> (310.59 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\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\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m2\u001b[0m (12.00 B)\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\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (12.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# развернем каждое изображение 28*28 в вектор 784\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
+        "                                                    test_size = 10000,\n",
+        "                                                    train_size = 60000,\n",
+        "                                                    random_state = 23)\n",
+        "num_pixels = X_train.shape[1] * X_train.shape[2]\n",
+        "X_train = X_train.reshape(X_train.shape[0], num_pixels) / 255\n",
+        "X_test = X_test.reshape(X_test.shape[0], num_pixels) / 255\n",
+        "print('Shape of transformed X train:', X_train.shape)\n",
+        "print('Shape of transformed X train:', 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": {
+        "id": "0ki8fhJrEyEt",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7f68607f-562f-4d80-8aca-5aa6de683947"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Shape of transformed X train: (60000, 784)\n",
+            "Shape of transformed X train: (10000, 784)\n",
+            "Shape of transformed y train: (60000, 10)\n",
+            "Shape of transformed y test: (10000, 10)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Оценка качества работы модели на тестовых данных\n",
+        "scores = model_lr1.evaluate(X_test, y_test)\n",
+        "print('Loss on test data:', scores[0])\n",
+        "print('Accuracy on test data:', scores[1])"
+      ],
+      "metadata": {
+        "id": "0Yj0fzLNE12k",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1e47e205-8f77-4a6f-eec3-dc6b004f76f6"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.9474 - loss: 0.1746\n",
+            "Loss on test data: 0.18537543714046478\n",
+            "Accuracy on test data: 0.9453999996185303\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 11) Сравнили обученную модель сверточной сети и наилучшую модель полносвязной сети из лабораторной работы №1 по следующим показателям:\n",
+        "### - количество настраиваемых параметров в сети\n",
+        "### - количество эпох обучения\n",
+        "### - качество классификации тестовой выборки.\n",
+        "### Сделали выводы по результатам применения сверточной нейронной сети для распознавания изображений.  "
+      ],
+      "metadata": {
+        "id": "MsM3ew3d1FYq"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Таблица1:"
+      ],
+      "metadata": {
+        "id": "xxFO4CXbIG88"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "| Модель   | Количество настраиваемых параметров | Количество эпох обучения | Качество классификации тестовой выборки |\n",
+        "|----------|-------------------------------------|---------------------------|-----------------------------------------|\n",
+        "| Сверточная | 34 826                              | 15                        | accuracy:0.988     ; loss:0.036                                 |\n",
+        "| Полносвязная | 79,512                              | 50                        | accuracy:0.9454     ; loss:0.185                                  |\n"
+      ],
+      "metadata": {
+        "id": "xvoivjuNFlEf"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#####По результатам применения сверточной НС, а также по результатам таблицы 1 делаем выводы, что сверточная НС лучше справляется с задачами распознования изображений, чем полносвязная - имеет меньше настраиваемых параметров, быстрее обучается, имеет лучшие показатели качества."
+      ],
+      "metadata": {
+        "id": "YctF8h_sIB-P"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Задание 2"
+      ],
+      "metadata": {
+        "id": "wCLHZPGB1F1y"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### В новом блокноте выполнили п. 2–8 задания 1, изменив набор данных MNIST на CIFAR-10, содержащий размеченные цветные изображения объектов, разделенные на 10 классов.  \n",
+        "### При этом:\n",
+        "### - в п. 3 разбиение данных на обучающие и тестовые произвели в соотношении 50 000:10 000\n",
+        "### - после разбиения данных (между п. 3 и 4) вывели 25 изображений из обучающей выборки с подписями классов\n",
+        "### - в п. 7 одно из тестовых изображений должно распознаваться корректно, а другое – ошибочно.   "
+      ],
+      "metadata": {
+        "id": "DUOYls124TT8"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 1) Загрузили набор данных CIFAR-10, содержащий цветные изображения размеченные на 10 классов: самолет, автомобиль, птица, кошка, олень, собака, лягушка, лошадь, корабль, грузовик."
+      ],
+      "metadata": {
+        "id": "XDStuSpEJa8o"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# загрузка датасета\n",
+        "from keras.datasets import cifar10\n",
+        "\n",
+        "(X_train, y_train), (X_test, y_test) = cifar10.load_data()"
+      ],
+      "metadata": {
+        "id": "y0qK7eKL4Tjy",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "be86c640-a56d-4856-852b-7e0ebf26aaaa"
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
+            "\u001b[1m170498071/170498071\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 0us/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2) Разбили набор данных на обучающие и тестовые данные в соотношении 50 000:10 000 элементов. Параметр random_state выбрали равным (4k – 1)=15, где k=4 –номер бригады. Вывели размерности полученных обучающих и тестовых массивов данных."
+      ],
+      "metadata": {
+        "id": "wTHiBy-ZJ5oh"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# создание своего разбиения датасета\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 = 15)\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": {
+        "id": "DlnFbQogKD2v",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a87bf7a7-68ed-401e-a39e-a08d63fbd809"
+      },
+      "execution_count": 24,
+      "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": "markdown",
+      "source": [
+        "### Вывели 25 изображений из обучающей выборки с подписью классов."
+      ],
+      "metadata": {
+        "id": "pj3bMaz1KZ3a"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
+        "               'dog', 'frog', 'horse', 'ship', 'truck']\n",
+        "\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()"
+      ],
+      "metadata": {
+        "id": "TW8D67KEKhVE",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 826
+        },
+        "outputId": "670357d5-a937-414e-e95d-ecbad3b416e6"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 25 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3) Провели предобработку данных: привели обучающие и тестовые данные к формату, пригодному для обучения сверточной нейронной сети. Входные данные принимают значения от 0 до 1, метки цифр закодированы по принципу «one-hot encoding». Вывели размерности предобработанных обучающих и тестовых массивов данных."
+      ],
+      "metadata": {
+        "id": "d3TPr2w1KQTK"
+      }
+    },
+    {
+      "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": {
+        "id": "iFDpxEauLZ8j",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6b7703c4-3ce4-41ab-bd25-aae10e34decc"
+      },
+      "execution_count": 26,
+      "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": "markdown",
+      "source": [
+        "### 4) Реализовали модель сверточной нейронной сети и обучили ее на обучающих данных с выделением части обучающих данных в качестве валидационных. Вывели информацию об архитектуре нейронной сети."
+      ],
+      "metadata": {
+        "id": "ydNITXptLeGT"
+      }
+    },
+    {
+      "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",
+        "# Блок 3\n",
+        "model.add(layers.Conv2D(128, (3, 3), padding=\"same\", activation=\"relu\"))\n",
+        "model.add(layers.BatchNormalization())\n",
+        "model.add(layers.Conv2D(128, (3, 3), padding=\"same\", activation=\"relu\"))\n",
+        "model.add(layers.BatchNormalization())\n",
+        "model.add(layers.MaxPooling2D((2, 2)))\n",
+        "model.add(layers.Dropout(0.4))\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": {
+        "id": "YhAD5CllLlv7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 984
+        },
+        "outputId": "99c702da-644c-46cd-c2ce-3f0f0f234531"
+      },
+      "execution_count": 27,
+      "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_1\"\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_1\"</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_2 (\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             │ (\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_3 (\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_1           │ (\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_2 (\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_1 (\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_4 (\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_2           │ (\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_5 (\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_3           │ (\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_3 (\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_2 (\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",
+              "│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │        \u001b[38;5;34m73,856\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ batch_normalization_4           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │           \u001b[38;5;34m512\u001b[0m │\n",
+              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │       \u001b[38;5;34m147,584\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ batch_normalization_5           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │           \u001b[38;5;34m512\u001b[0m │\n",
+              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_4 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │       \u001b[38;5;34m262,272\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_4 (\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_2 (\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_2 (<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             │ (<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_3 (<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_1           │ (<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_2 (<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_1 (<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_4 (<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_2           │ (<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_5 (<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_3           │ (<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_3 (<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_2 (<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",
+              "│ conv2d_6 (<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\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ batch_normalization_4           │ (<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\">128</span>)      │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_7 (<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\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │       <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ batch_normalization_5           │ (<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\">128</span>)      │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_4 (<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\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_3 (<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\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ flatten_1 (<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\">2048</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_1 (<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\">262,272</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_4 (<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_2 (<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;34m552,362\u001b[0m (2.11 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\">552,362</span> (2.11 MB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m551,466\u001b[0m (2.10 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\">551,466</span> (2.10 MB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m896\u001b[0m (3.50 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\">896</span> (3.50 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# компилируем и обучаем модель\n",
+        "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": {
+        "id": "3otvqMjjOdq5",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "dc91afb5-d8a9-4541-9104-9dd6cce4d8fe"
+      },
+      "execution_count": 28,
+      "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[1m30s\u001b[0m 26ms/step - accuracy: 0.2649 - loss: 2.1252 - val_accuracy: 0.4706 - val_loss: 1.4156\n",
+            "Epoch 2/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.4601 - loss: 1.4764 - val_accuracy: 0.5836 - val_loss: 1.1330\n",
+            "Epoch 3/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.5653 - loss: 1.2306 - val_accuracy: 0.6386 - val_loss: 1.0458\n",
+            "Epoch 4/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.6361 - loss: 1.0504 - val_accuracy: 0.6950 - val_loss: 0.8850\n",
+            "Epoch 5/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.6788 - loss: 0.9410 - val_accuracy: 0.7124 - val_loss: 0.8396\n",
+            "Epoch 6/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.6999 - loss: 0.8709 - val_accuracy: 0.7282 - val_loss: 0.7933\n",
+            "Epoch 7/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.7321 - loss: 0.7877 - val_accuracy: 0.7426 - val_loss: 0.7512\n",
+            "Epoch 8/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.7522 - loss: 0.7415 - val_accuracy: 0.7510 - val_loss: 0.7512\n",
+            "Epoch 9/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.7654 - loss: 0.6900 - val_accuracy: 0.7766 - val_loss: 0.6617\n",
+            "Epoch 10/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.7855 - loss: 0.6378 - val_accuracy: 0.7578 - val_loss: 0.7086\n",
+            "Epoch 11/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.7883 - loss: 0.6206 - val_accuracy: 0.7872 - val_loss: 0.6364\n",
+            "Epoch 12/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8030 - loss: 0.5865 - val_accuracy: 0.7576 - val_loss: 0.7369\n",
+            "Epoch 13/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.8072 - loss: 0.5700 - val_accuracy: 0.7958 - val_loss: 0.5959\n",
+            "Epoch 14/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8219 - loss: 0.5243 - val_accuracy: 0.8142 - val_loss: 0.5654\n",
+            "Epoch 15/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.8273 - loss: 0.5082 - val_accuracy: 0.7914 - val_loss: 0.6319\n",
+            "Epoch 16/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8374 - loss: 0.4829 - val_accuracy: 0.8262 - val_loss: 0.5276\n",
+            "Epoch 17/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8353 - loss: 0.4944 - val_accuracy: 0.8072 - val_loss: 0.5965\n",
+            "Epoch 18/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8428 - loss: 0.4644 - val_accuracy: 0.8310 - val_loss: 0.5173\n",
+            "Epoch 19/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8506 - loss: 0.4382 - val_accuracy: 0.8094 - val_loss: 0.6068\n",
+            "Epoch 20/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8534 - loss: 0.4367 - val_accuracy: 0.8204 - val_loss: 0.5567\n",
+            "Epoch 21/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 10ms/step - accuracy: 0.8567 - loss: 0.4203 - val_accuracy: 0.8298 - val_loss: 0.5206\n",
+            "Epoch 22/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.8572 - loss: 0.4125 - val_accuracy: 0.8096 - val_loss: 0.5871\n",
+            "Epoch 23/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8659 - loss: 0.3902 - val_accuracy: 0.8352 - val_loss: 0.5270\n",
+            "Epoch 24/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8687 - loss: 0.3854 - val_accuracy: 0.8294 - val_loss: 0.5306\n",
+            "Epoch 25/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8686 - loss: 0.3820 - val_accuracy: 0.8276 - val_loss: 0.5693\n",
+            "Epoch 26/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8730 - loss: 0.3675 - val_accuracy: 0.8400 - val_loss: 0.5132\n",
+            "Epoch 27/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8741 - loss: 0.3597 - val_accuracy: 0.8352 - val_loss: 0.5377\n",
+            "Epoch 28/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.8762 - loss: 0.3631 - val_accuracy: 0.8426 - val_loss: 0.5101\n",
+            "Epoch 29/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8864 - loss: 0.3382 - val_accuracy: 0.8150 - val_loss: 0.5927\n",
+            "Epoch 30/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.8881 - loss: 0.3253 - val_accuracy: 0.8390 - val_loss: 0.5130\n",
+            "Epoch 31/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8867 - loss: 0.3306 - val_accuracy: 0.8406 - val_loss: 0.5136\n",
+            "Epoch 32/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8869 - loss: 0.3284 - val_accuracy: 0.8464 - val_loss: 0.4983\n",
+            "Epoch 33/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8907 - loss: 0.3224 - val_accuracy: 0.8490 - val_loss: 0.4849\n",
+            "Epoch 34/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 10ms/step - accuracy: 0.8962 - loss: 0.3082 - val_accuracy: 0.8292 - val_loss: 0.5812\n",
+            "Epoch 35/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.8938 - loss: 0.3085 - val_accuracy: 0.8318 - val_loss: 0.5522\n",
+            "Epoch 36/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8937 - loss: 0.3166 - val_accuracy: 0.8430 - val_loss: 0.5358\n",
+            "Epoch 37/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9012 - loss: 0.2844 - val_accuracy: 0.8552 - val_loss: 0.5092\n",
+            "Epoch 38/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.9023 - loss: 0.2928 - val_accuracy: 0.8456 - val_loss: 0.5474\n",
+            "Epoch 39/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9082 - loss: 0.2685 - val_accuracy: 0.8500 - val_loss: 0.4935\n",
+            "Epoch 40/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.9040 - loss: 0.2683 - val_accuracy: 0.8526 - val_loss: 0.4914\n",
+            "Epoch 41/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9061 - loss: 0.2696 - val_accuracy: 0.8374 - val_loss: 0.5551\n",
+            "Epoch 42/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.9074 - loss: 0.2685 - val_accuracy: 0.8504 - val_loss: 0.5127\n",
+            "Epoch 43/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9081 - loss: 0.2675 - val_accuracy: 0.8508 - val_loss: 0.5055\n",
+            "Epoch 44/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.9132 - loss: 0.2537 - val_accuracy: 0.8540 - val_loss: 0.5132\n",
+            "Epoch 45/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.9145 - loss: 0.2426 - val_accuracy: 0.8568 - val_loss: 0.4913\n",
+            "Epoch 46/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.9148 - loss: 0.2461 - val_accuracy: 0.8560 - val_loss: 0.5101\n",
+            "Epoch 47/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9158 - loss: 0.2474 - val_accuracy: 0.8514 - val_loss: 0.5321\n",
+            "Epoch 48/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.9197 - loss: 0.2367 - val_accuracy: 0.8450 - val_loss: 0.5670\n",
+            "Epoch 49/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 10ms/step - accuracy: 0.9153 - loss: 0.2506 - val_accuracy: 0.8492 - val_loss: 0.5532\n",
+            "Epoch 50/50\n",
+            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9223 - loss: 0.2278 - val_accuracy: 0.8478 - val_loss: 0.5545\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.history.History at 0x7ef4bc4d4ef0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 5) Оценили качество обучения на тестовых данных. Вывели значение функции ошибки и значение метрики качества классификации на тестовых данных."
+      ],
+      "metadata": {
+        "id": "Vv1kUHWTLl9B"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Оценка качества работы модели на тестовых данных\n",
+        "scores = model.evaluate(X_test, y_test)\n",
+        "print('Loss on test data:', scores[0])\n",
+        "print('Accuracy on test data:', scores[1])"
+      ],
+      "metadata": {
+        "id": "SaDxydiyLmRX",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ef0cce59-50d5-49e2-a03b-debe3f83cae1"
+      },
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - accuracy: 0.8587 - loss: 0.5093\n",
+            "Loss on test data: 0.5083962678909302\n",
+            "Accuracy on test data: 0.857200026512146\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 6) Подали на вход обученной модели два тестовых изображения. Вывели изображения, истинные метки и результаты распознавания."
+      ],
+      "metadata": {
+        "id": "OdgEiyUGLmhP"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# вывод двух тестовых изображений и результатов распознавания\n",
+        "\n",
+        "for n in [2,3]:\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": {
+        "id": "t3yGj1MlLm9H",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "3d281339-dfb6-4df0-d3f1-27d46f513581"
+      },
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
+            "NN output: [[7.2646151e-05 3.8818748e-06 5.3400207e-01 4.6440233e-03 4.0224893e-03\n",
+            "  3.1369660e-04 7.8649860e-04 1.5578480e-05 4.5613229e-01 6.7981441e-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:  8\n",
+            "NN answer:  2\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step\n",
+            "NN output: [[1.9803477e-11 1.8675040e-14 2.2670738e-06 8.6696136e-06 9.9992847e-01\n",
+            "  5.8956124e-05 1.9674586e-09 1.5675565e-06 2.7082836e-13 2.0201145e-12]]\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:  4\n",
+            "NN answer:  4\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 7) Вывели отчет о качестве классификации тестовой выборки и матрицу ошибок для тестовой выборки."
+      ],
+      "metadata": {
+        "id": "3h6VGDRrLnNC"
+      }
+    },
+    {
+      "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": {
+        "id": "od56oyyzM0nw",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 888
+        },
+        "outputId": "7d2fdd35-f04a-457f-b746-0ad790ff36d8"
+      },
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "    airplane       0.90      0.83      0.86      1015\n",
+            "  automobile       0.94      0.94      0.94       933\n",
+            "        bird       0.85      0.79      0.82      1010\n",
+            "         cat       0.78      0.67      0.72      1025\n",
+            "        deer       0.79      0.89      0.84       998\n",
+            "         dog       0.77      0.82      0.79      1006\n",
+            "        frog       0.83      0.94      0.88      1010\n",
+            "       horse       0.94      0.84      0.89      1005\n",
+            "        ship       0.90      0.93      0.91      1001\n",
+            "       truck       0.89      0.94      0.92       997\n",
+            "\n",
+            "    accuracy                           0.86     10000\n",
+            "   macro avg       0.86      0.86      0.86     10000\n",
+            "weighted avg       0.86      0.86      0.86     10000\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### По результатам классификации датасета CIFAR-10 созданной сверточной моделью можно сделать вывод, что она довольно неплохо справилась с задачей. Полученные метрики оценки качества имеют показатели в районе 0.85."
+      ],
+      "metadata": {
+        "id": "RF4xK1cxamBc"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "j25iOme0l4El"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
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diff --git a/labworks/LW3/report.md b/labworks/LW3/report.md
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--- /dev/null
+++ b/labworks/LW3/report.md
@@ -0,0 +1,506 @@
+# Отчёт по лабораторной работе №3
+Артюшина Валерия, Хохлов Кирилл, А-01-22
+
+# Задание 1
+
+## 1) В среде Google Colab создали новый блокнот (notebook). Импортировали необходимые для работы библиотеки и модули.
+
+```python
+# импорт модулей
+import os
+os.mkdir('/content/drive/MyDrive/Colab Notebooks/is_lab3')
+os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab3')
+
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras.models import Sequential
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.metrics import classification_report, confusion_matrix
+from sklearn.metrics import ConfusionMatrixDisplay
+```
+
+## 2) Загрузили набор данных MNIST, содержащий размеченные изображения рукописных цифр.  
+
+```python
+# загрузка датасета
+from keras.datasets import mnist
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+```
+
+## 3) Разбили набор данных на обучающие и тестовые данные в соотношении 60 000:10 000 элементов. Параметр random_state выбрали равным (4k – 1) = 15, где k = 4 – номер бригады. Вывели размерности полученных обучающих и тестовых массивов данных.
+
+```python
+# создание своего разбиения датасета
+from sklearn.model_selection import train_test_split
+
+# объединяем в один набор
+X = np.concatenate((X_train, X_test))
+y = np.concatenate((y_train, y_test))
+
+# разбиваем по вариантам
+X_train, X_test, y_train, y_test = train_test_split(X, y,
+                                                    test_size = 10000,
+                                                    train_size = 60000,
+                                                    random_state = 15)
+# вывод размерностей
+print('Shape of X train:', X_train.shape)
+print('Shape of y train:', y_train.shape)
+print('Shape of X test:', X_test.shape)
+print('Shape of y test:', y_test.shape)
+```
+```
+Shape of X train: (60000, 28, 28)
+Shape of y train: (60000,)
+Shape of X test: (10000, 28, 28)
+Shape of y test: (10000,)
+```
+
+## 4) Провели предобработку данных: привели обучающие и тестовые данные к формату, пригодному для обучения сверточной нейронной сети. Входные данные принимают значения от 0 до 1, метки цифр закодированы по принципу «one-hot encoding». Вывели размерности предобработанных обучающих и тестовых массивов данных.
+
+```python
+# Зададим параметры данных и модели
+num_classes = 10
+input_shape = (28, 28, 1)
+
+# Приведение входных данных к диапазону [0, 1]
+X_train = X_train / 255
+X_test = X_test / 255
+
+# Расширяем размерность входных данных, чтобы каждое изображение имело
+# размерность (высота, ширина, количество каналов)
+
+X_train = np.expand_dims(X_train, -1)
+X_test = np.expand_dims(X_test, -1)
+print('Shape of transformed X train:', X_train.shape)
+print('Shape of transformed X test:', X_test.shape)
+
+# переведем метки в one-hot
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
+print('Shape of transformed y train:', y_train.shape)
+print('Shape of transformed y test:', y_test.shape)
+```
+```
+Shape of transformed X train: (60000, 28, 28, 1)
+Shape of transformed X test: (10000, 28, 28, 1)
+Shape of transformed y train: (60000, 10)
+Shape of transformed y test: (10000, 10)
+```
+
+## 5) Реализовали модель сверточной нейронной сети и обучили ее на обучающих данных с выделением части обучающих данных в качестве валидационных. Вывели информацию об архитектуре нейронной сети.
+
+```python
+# создаем модель
+model = Sequential()
+model.add(layers.Conv2D(32, kernel_size=(3, 3), activation="relu", input_shape=input_shape))
+model.add(layers.MaxPooling2D(pool_size=(2, 2)))
+model.add(layers.Conv2D(64, kernel_size=(3, 3), activation="relu"))
+model.add(layers.MaxPooling2D(pool_size=(2, 2)))
+model.add(layers.Dropout(0.5))
+model.add(layers.Flatten())
+model.add(layers.Dense(num_classes, activation="softmax"))
+
+model.summary()
+```
+![alt text](p5.png)
+
+```python
+# компилируем и обучаем модель
+batch_size = 512
+epochs = 15
+model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
+model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)
+```
+
+## 6) Оценили качество обучения на тестовых данных. Вывели значение функции ошибки и значение метрики качества классификации на тестовых данных.
+
+```python
+# Оценка качества работы модели на тестовых данных
+scores = model.evaluate(X_test, y_test)
+print('Loss on test data:', scores[0])
+print('Accuracy on test data:', scores[1])
+```
+```
+accuracy: 0.9895 
+loss: 0.0345
+Loss on test data: 0.035905033349990845
+Accuracy on test data: 0.988099992275238
+```
+
+## 7) Подали на вход обученной модели два тестовых изображения. Вывели изображения, истинные метки и результаты распознавания.
+
+```python
+# вывод двух тестовых изображений и результатов распознавания
+
+for n in [15, 16]:
+  result = model.predict(X_test[n:n+1])
+  print('NN output:', result)
+
+  plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))
+  plt.show()
+  print('Real mark: ', np.argmax(y_test[n]))
+  print('NN answer: ', np.argmax(result))
+```
+![alt text](p7_1.png)
+```
+Real mark:  1
+NN answer:  1
+```
+![alt text](p7_2.png)
+```
+Real mark:  0
+NN answer:  0
+```
+
+## 8) Вывели отчет о качестве классификации тестовой выборки и матрицу ошибок для тестовой выборки.
+
+```python
+# истинные метки классов
+true_labels = np.argmax(y_test, axis=1)
+# предсказанные метки классов
+predicted_labels = np.argmax(model.predict(X_test), axis=1)
+
+# отчет о качестве классификации
+print(classification_report(true_labels, predicted_labels))
+# вычисление матрицы ошибок
+conf_matrix = confusion_matrix(true_labels, predicted_labels)
+# отрисовка матрицы ошибок в виде "тепловой карты"
+display = ConfusionMatrixDisplay(confusion_matrix=conf_matrix)
+display.plot()
+plt.show()
+```
+```
+              precision    recall  f1-score   support
+
+           0       0.99      0.99      0.99       994
+           1       0.99      0.99      0.99      1194
+           2       0.98      0.99      0.98       975
+           3       0.99      0.99      0.99      1031
+           4       0.98      0.99      0.99       967
+           5       0.99      0.99      0.99       937
+           6       0.99      0.99      0.99       964
+           7       0.99      0.99      0.99       998
+           8       0.98      0.98      0.98       965
+           9       0.99      0.98      0.98       975
+
+    accuracy                           0.99     10000
+   macro avg       0.99      0.99      0.99     10000
+weighted avg       0.99      0.99      0.99     10000
+```
+![alt text](p8.png)
+
+## 9) Загрузили, предобработали и подали на вход обученной нейронной сети собственное изображение, созданное при выполнении лабораторной работы №1. Вывели изображение и результат распознавания.
+
+```python
+# загрузка собственного изображения
+from PIL import Image
+
+for name_image in ['1.png', '2.png']:
+  file_data = Image.open(name_image)
+  file_data = file_data.convert('L') # перевод в градации серого
+  test_img = np.array(file_data)
+
+  # вывод собственного изображения
+  plt.imshow(test_img, cmap=plt.get_cmap('gray'))
+  plt.show()
+
+  # предобработка
+  test_img = test_img / 255
+  test_img = np.reshape(test_img, (1,28,28,1))
+
+  # распознавание
+  result = model.predict(test_img)
+  print('I think it\'s', np.argmax(result))
+```
+![alt text](p9_1.png)
+```
+I think it's 1
+```
+![alt text](p9_2.png)
+```
+I think it's 2
+```
+
+## 10) Загрузили с диска модель, сохраненную при выполнении лабораторной работы №1. Вывели информацию об архитектуре модели. Повторили для этой модели п. 6.
+
+```python
+model_lr1 = keras.models.load_model("best_model.keras")
+
+model_lr1.summary()
+```
+![alt text](p10.png)
+
+
+```python
+# развернем каждое изображение 28*28 в вектор 784
+X_train, X_test, y_train, y_test = train_test_split(X, y,
+                                                    test_size = 10000,
+                                                    train_size = 60000,
+                                                    random_state = 23)
+num_pixels = X_train.shape[1] * X_train.shape[2]
+X_train = X_train.reshape(X_train.shape[0], num_pixels) / 255
+X_test = X_test.reshape(X_test.shape[0], num_pixels) / 255
+print('Shape of transformed X train:', X_train.shape)
+print('Shape of transformed X train:', X_test.shape)
+
+# переведем метки в one-hot
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
+print('Shape of transformed y train:', y_train.shape)
+print('Shape of transformed y test:', y_test.shape)
+```
+```
+Shape of transformed X train: (60000, 784)
+Shape of transformed X train: (10000, 784)
+Shape of transformed y train: (60000, 10)
+Shape of transformed y test: (10000, 10)
+```
+
+```python
+# Оценка качества работы модели на тестовых данных
+scores = model_lr1.evaluate(X_test, y_test)
+print('Loss on test data:', scores[0])
+print('Accuracy on test data:', scores[1])
+```
+```
+accuracy: 0.9474 
+loss: 0.1746
+Loss on test data: 0.18537543714046478
+Accuracy on test data: 0.9453999996185303
+```
+
+## 11) Сравнили обученную модель сверточной сети и наилучшую модель полносвязной сети из лабораторной работы №1 по следующим показателям:
+## - количество настраиваемых параметров в сети
+## - количество эпох обучения
+## - качество классификации тестовой выборки.
+## Сделали выводы по результатам применения сверточной нейронной сети для распознавания изображений.  
+
+Таблица 1:
+
+| Модель   | Количество настраиваемых параметров | Количество эпох обучения | Качество классификации тестовой выборки |
+|----------|-------------------------------------|---------------------------|-----------------------------------------|
+| Сверточная | 34 826                              | 15                        | accuracy:0.988     ; loss:0.036                                 |
+| Полносвязная | 79,512                              | 50                        | accuracy:0.9454     ; loss:0.185                                  |
+
+## Вывод
+По результатам применения сверточной НС, а также по результатам таблицы 1 делаем выводы, что сверточная НС лучше справляется с задачами распознования изображений, чем полносвязная - имеет меньше настраиваемых параметров, быстрее обучается, имеет лучшие показатели качества.
+
+# Задание 2
+
+## В новом блокноте выполнили п. 2–8 задания 1, изменив набор данных MNIST на CIFAR-10, содержащий размеченные цветные изображения объектов, разделенные на 10 классов.  
+## При этом:
+## - в п. 3 разбиение данных на обучающие и тестовые произвели в соотношении 50 000:10 000
+## - после разбиения данных (между п. 3 и 4) вывели 25 изображений из обучающей выборки с подписями классов
+## - в п. 7 одно из тестовых изображений должно распознаваться корректно, а другое – ошибочно.   
+
+## 1) Загрузили набор данных CIFAR-10, содержащий цветные изображения размеченные на 10 классов: самолет, автомобиль, птица, кошка, олень, собака, лягушка, лошадь, корабль, грузовик.
+
+```python
+# загрузка датасета
+from keras.datasets import cifar10
+
+(X_train, y_train), (X_test, y_test) = cifar10.load_data()
+```
+
+## 2) Разбили набор данных на обучающие и тестовые данные в соотношении 50 000:10 000 элементов. Параметр random_state выбрали равным (4k – 1)=15, где k=4 –номер бригады. Вывели размерности полученных обучающих и тестовых массивов данных.
+
+```python
+# создание своего разбиения датасета
+
+# объединяем в один набор
+X = np.concatenate((X_train, X_test))
+y = np.concatenate((y_train, y_test))
+
+# разбиваем по вариантам
+X_train, X_test, y_train, y_test = train_test_split(X, y,
+                                                    test_size = 10000,
+                                                    train_size = 50000,
+                                                    random_state = 15)
+# вывод размерностей
+print('Shape of X train:', X_train.shape)
+print('Shape of y train:', y_train.shape)
+print('Shape of X test:', X_test.shape)
+print('Shape of y test:', y_test.shape)
+```
+```
+Shape of X train: (50000, 32, 32, 3)
+Shape of y train: (50000, 1)
+Shape of X test: (10000, 32, 32, 3)
+Shape of y test: (10000, 1)
+```
+
+## Вывели 25 изображений из обучающей выборки с подписью классов.
+
+```python
+class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
+               'dog', 'frog', 'horse', 'ship', 'truck']
+
+plt.figure(figsize=(10,10))
+for i in range(25):
+    plt.subplot(5,5,i+1)
+    plt.xticks([])
+    plt.yticks([])
+    plt.grid(False)
+    plt.imshow(X_train[i])
+    plt.xlabel(class_names[y_train[i][0]])
+plt.show()
+```
+![alt text](2_p2.png)
+
+## 3) Провели предобработку данных: привели обучающие и тестовые данные к формату, пригодному для обучения сверточной нейронной сети. Входные данные принимают значения от 0 до 1, метки цифр закодированы по принципу «one-hot encoding». Вывели размерности предобработанных обучающих и тестовых массивов данных.
+
+```python
+# Зададим параметры данных и модели
+num_classes = 10
+input_shape = (32, 32, 3)
+
+# Приведение входных данных к диапазону [0, 1]
+X_train = X_train / 255
+X_test = X_test / 255
+
+print('Shape of transformed X train:', X_train.shape)
+print('Shape of transformed X test:', X_test.shape)
+
+# переведем метки в one-hot
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
+print('Shape of transformed y train:', y_train.shape)
+print('Shape of transformed y test:', y_test.shape)
+```
+```
+Shape of transformed X train: (50000, 32, 32, 3)
+Shape of transformed X test: (10000, 32, 32, 3)
+Shape of transformed y train: (50000, 10)
+Shape of transformed y test: (10000, 10)
+```
+
+## 4) Реализовали модель сверточной нейронной сети и обучили ее на обучающих данных с выделением части обучающих данных в качестве валидационных. Вывели информацию об архитектуре нейронной сети.
+
+```python
+# создаем модель
+model = Sequential()
+
+# Блок 1
+model.add(layers.Conv2D(32, (3, 3), padding="same",
+                        activation="relu", input_shape=input_shape))
+model.add(layers.BatchNormalization())
+model.add(layers.Conv2D(32, (3, 3), padding="same", activation="relu"))
+model.add(layers.BatchNormalization())
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Dropout(0.25))
+
+# Блок 2
+model.add(layers.Conv2D(64, (3, 3), padding="same", activation="relu"))
+model.add(layers.BatchNormalization())
+model.add(layers.Conv2D(64, (3, 3), padding="same", activation="relu"))
+model.add(layers.BatchNormalization())
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Dropout(0.25))
+
+# Блок 3
+model.add(layers.Conv2D(128, (3, 3), padding="same", activation="relu"))
+model.add(layers.BatchNormalization())
+model.add(layers.Conv2D(128, (3, 3), padding="same", activation="relu"))
+model.add(layers.BatchNormalization())
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Dropout(0.4))
+
+model.add(layers.Flatten())
+model.add(layers.Dense(128, activation='relu'))
+model.add(layers.Dropout(0.5))
+model.add(layers.Dense(num_classes, activation="softmax"))
+
+model.summary()
+```
+![alt text](2_p4.png)
+
+```python
+# компилируем и обучаем модель
+batch_size = 64
+epochs = 50
+model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
+model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)
+```
+
+## 5) Оценили качество обучения на тестовых данных. Вывели значение функции ошибки и значение метрики качества классификации на тестовых данных.
+
+```python
+# Оценка качества работы модели на тестовых данных
+scores = model.evaluate(X_test, y_test)
+print('Loss on test data:', scores[0])
+print('Accuracy on test data:', scores[1])
+```
+```
+accuracy: 0.8587 
+loss: 0.5093
+Loss on test data: 0.5083962678909302
+Accuracy on test data: 0.857200026512146
+```
+
+## 6) Подали на вход обученной модели два тестовых изображения. Вывели изображения, истинные метки и результаты распознавания.
+
+```python
+# вывод двух тестовых изображений и результатов распознавания
+
+for n in [2,3]:
+  result = model.predict(X_test[n:n+1])
+  print('NN output:', result)
+
+  plt.imshow(X_test[n].reshape(32,32,3), cmap=plt.get_cmap('gray'))
+  plt.show()
+  print('Real mark: ', np.argmax(y_test[n]))
+  print('NN answer: ', np.argmax(result))
+```
+![alt text](2_p6_1.png)
+```
+Real mark:  8
+NN answer:  2
+```
+![alt text](2_p6_2.png)
+```
+Real mark:  4
+NN answer:  4
+```
+
+## 7) Вывели отчет о качестве классификации тестовой выборки и матрицу ошибок для тестовой выборки.
+
+```python
+# истинные метки классов
+true_labels = np.argmax(y_test, axis=1)
+# предсказанные метки классов
+predicted_labels = np.argmax(model.predict(X_test), axis=1)
+
+# отчет о качестве классификации
+print(classification_report(true_labels, predicted_labels, target_names=class_names))
+# вычисление матрицы ошибок
+conf_matrix = confusion_matrix(true_labels, predicted_labels)
+# отрисовка матрицы ошибок в виде "тепловой карты"
+fig, ax = plt.subplots(figsize=(6, 6))
+disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix,display_labels=class_names)
+disp.plot(ax=ax, xticks_rotation=45)  # поворот подписей по X и приятная палитра
+plt.tight_layout()  # чтобы всё влезло
+plt.show()
+```
+```
+              precision    recall  f1-score   support
+
+    airplane       0.90      0.83      0.86      1015
+  automobile       0.94      0.94      0.94       933
+        bird       0.85      0.79      0.82      1010
+         cat       0.78      0.67      0.72      1025
+        deer       0.79      0.89      0.84       998
+         dog       0.77      0.82      0.79      1006
+        frog       0.83      0.94      0.88      1010
+       horse       0.94      0.84      0.89      1005
+        ship       0.90      0.93      0.91      1001
+       truck       0.89      0.94      0.92       997
+
+    accuracy                           0.86     10000
+   macro avg       0.86      0.86      0.86     10000
+weighted avg       0.86      0.86      0.86     10000
+```
+![alt text](2_p7.png)
+
+## Вывод
+По результатам классификации датасета CIFAR-10 созданной сверточной моделью можно сделать вывод, что она довольно неплохо справилась с задачей. Полученные метрики оценки качества имеют показатели в районе 0.85.
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