Изменил(а) на 'labworks/LW1/report.md'

labworks/LW1/IS_LR1.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HUUZx52sc1LD"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "os.chdir('/content/drive/MyDrive/Colab Notebooks')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# импорт модулей\n",
+        "from tensorflow import keras\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import sklearn"
+      ],
+      "metadata": {
+        "id": "3Y-Ux1dadqdA"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "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": "w25XE8ADdqP5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# создание своего разбиения датасета\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "# объединяем в один набор\n",
+        "X = np.concatenate((X_train, X_test))\n",
+        "y = np.concatenate((y_train, y_test))\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 = 19) #(5*4-1)"
+      ],
+      "metadata": {
+        "id": "QcXt9zqCdqDH"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# вывод размерностей\n",
+        "print('Shape of X train:', X_train.shape)\n",
+        "print('Shape of y train:', y_train.shape)"
+      ],
+      "metadata": {
+        "id": "9Cd705vod51B",
+        "collapsed": true
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Выводим 4 изображения\n",
+        "plt.figure(figsize=(10, 3))\n",
+        "for i in range(4):\n",
+        "    plt.subplot(1, 4, i + 1)\n",
+        "    plt.imshow(X_train[i], cmap='gray')\n",
+        "    plt.title(f'Label: {y_train[i]}')\n",
+        "    plt.axis('off')\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "vLYfI---d5rm",
+        "collapsed": true
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# развернем каждое изображение 28*28 в вектор 784\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)"
+      ],
+      "metadata": {
+        "id": "d0oyu59gd5fz",
+        "collapsed": true
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# переведем метки в one-hot\n",
+        "from keras.utils import to_categorical\n",
+        "\n",
+        "y_train = to_categorical(y_train)\n",
+        "y_test = to_categorical(y_test)\n",
+        "\n",
+        "print('Shape of transformed y train:', y_train.shape)\n",
+        "num_classes = y_train.shape[1]"
+      ],
+      "metadata": {
+        "id": "Q227fINPeD1A",
+        "collapsed": true
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from keras.models import Sequential\n",
+        "from keras.layers import Dense"
+      ],
+      "metadata": {
+        "id": "TzaA61smeDoO"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 1. создаем модель - объявляем ее объектом класса Sequential\n",
+        "model = Sequential()\n",
+        "# 2. добавляем выходной слой(скрытые слои отсутствуют)\n",
+        "model.add(Dense(units=num_classes, activation='softmax'))\n",
+        "# 3. компилируем модель\n",
+        "model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
+      ],
+      "metadata": {
+        "id": "Liq39zruhz0d"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# вывод информации об архитектуре модели\n",
+        "print(model.summary())"
+      ],
+      "metadata": {
+        "id": "jMGGsq7piZOu",
+        "collapsed": true
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Обучаем модель\n",
+        "H = model.fit(X_train, y_train, validation_split=0.1, epochs=50)"
+      ],
+      "metadata": {
+        "id": "n_pCdxphiedM",
+        "collapsed": true
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# вывод графика ошибки по эпохам\n",
+        "plt.plot(H.history['loss'])\n",
+        "plt.plot(H.history['val_loss'])\n",
+        "plt.grid()\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('loss')\n",
+        "plt.legend(['train_loss', 'val_loss'])\n",
+        "plt.title('Loss by epochs')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "Sz_YOlsVivoR",
+        "collapsed": true
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "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": "hpJALaZGnyWF",
+        "collapsed": true
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# сохранение модели на диск\n",
+        "model.save('/content/drive/MyDrive/Colab Notebooks/models/model_zero_hide.keras')"
+      ],
+      "metadata": {
+        "id": "Z6eSmpwXn1zM"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model100 = Sequential()\n",
+        "model100.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid'))\n",
+        "model100.add(Dense(units=num_classes, activation='softmax'))\n",
+        "\n",
+        "model100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
+      ],
+      "metadata": {
+        "id": "G1qGmPNF9afO"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# вывод информации об архитектуре модели\n",
+        "print(model100.summary())"
+      ],
+      "metadata": {
+        "id": "2WtfjJKY9abn",
+        "collapsed": true
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Обучаем модель\n",
+        "H = model100.fit(X_train, y_train, validation_split=0.1, epochs=50)"
+      ],
+      "metadata": {
+        "id": "rPuWd80o9aYD",
+        "collapsed": true
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# вывод графика ошибки по эпохам\n",
+        "plt.plot(H.history['loss'])\n",
+        "plt.plot(H.history['val_loss'])\n",
+        "plt.grid()\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('loss')\n",
+        "plt.legend(['train_loss', 'val_loss'])\n",
+        "plt.title('Loss by epochs')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "JLrW7S1g9aUe",
+        "collapsed": true
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Оценка качества работы модели на тестовых данных\n",
+        "scores = model100.evaluate(X_test, y_test)\n",
+        "print('Loss on test data:', scores[0])\n",
+        "print('Accuracy on test data:', scores[1])"
+      ],
+      "metadata": {
+        "id": "8jdS02JZ9aRc",
+        "collapsed": true
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# сохранение модели на диск\n",
+        "model100.save('/content/drive/MyDrive/Colab Notebooks/models/model100in_1hide.keras')"
+      ],
+      "metadata": {
+        "id": "_bR3qoBy9aNy"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model300 = Sequential()\n",
+        "model300.add(Dense(units=300,input_dim=num_pixels, activation='sigmoid'))\n",
+        "model300.add(Dense(units=num_classes, activation='softmax'))\n",
+        "\n",
+        "model300.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
+      ],
+      "metadata": {
+        "id": "V4m3nGORGnPC"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# вывод информации об архитектуре модели\n",
+        "print(model300.summary())"
+      ],
+      "metadata": {
+        "id": "yETaYKzdA9fp",
+        "collapsed": true
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Обучаем модель\n",
+        "H = model300.fit(X_train, y_train, validation_split=0.1, epochs=50)"
+      ],
+      "metadata": {
+        "id": "SFPh0Lw-A9Zq",
+        "collapsed": true
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# вывод графика ошибки по эпохам\n",
+        "plt.plot(H.history['loss'])\n",
+        "plt.plot(H.history['val_loss'])\n",
+        "plt.grid()\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('loss')\n",
+        "plt.legend(['train_loss', 'val_loss'])\n",
+        "plt.title('Loss by epochs')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "6mvOMGiLA9QE",
+        "collapsed": true
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Оценка качества работы модели на тестовых данных\n",
+        "scores = model300.evaluate(X_test, y_test)\n",
+        "print('Loss on test data:', scores[0])\n",
+        "print('Accuracy on test data:', scores[1])"
+      ],
+      "metadata": {
+        "id": "WOJyUHP79Z86"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# сохранение модели на диск\n",
+        "model300.save('/content/drive/MyDrive/Colab Notebooks/models/model300in_1hide.keras')"
+      ],
+      "metadata": {
+        "id": "XsWc7S4aCyiE"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model500 = Sequential()\n",
+        "model500.add(Dense(units=500,input_dim=num_pixels, activation='sigmoid'))\n",
+        "model500.add(Dense(units=num_classes, activation='softmax'))\n",
+        "\n",
+        "model500.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
+      ],
+      "metadata": {
+        "id": "QSL-6YbkJxm0"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# вывод информации об архитектуре модели\n",
+        "print(model500.summary())"
+      ],
+      "metadata": {
+        "id": "Vs1x3ooKCybg"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Обучаем модель\n",
+        "H = model500.fit(X_train, y_train, validation_split=0.1, epochs=50)"
+      ],
+      "metadata": {
+        "id": "x3kzDT5qCyYY"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# вывод графика ошибки по эпохам\n",
+        "plt.plot(H.history['loss'])\n",
+        "plt.plot(H.history['val_loss'])\n",
+        "plt.grid()\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('loss')\n",
+        "plt.legend(['train_loss', 'val_loss'])\n",
+        "plt.title('Loss by epochs')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "FwSLP5I8CyU0"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Оценка качества работы модели на тестовых данных\n",
+        "scores = model500.evaluate(X_test, y_test)\n",
+        "print('Loss on test data:', scores[0])\n",
+        "print('Accuracy on test data:', scores[1])"
+      ],
+      "metadata": {
+        "id": "5mDveUNPCyRH"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# сохранение модели на диск\n",
+        "model500.save('/content/drive/MyDrive/Colab Notebooks/models/model500in_1hide.keras')"
+      ],
+      "metadata": {
+        "id": "4IEeNu1rCyNj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model10050 = Sequential()\n",
+        "model10050.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid'))\n",
+        "model10050.add(Dense(units=50,activation='sigmoid'))\n",
+        "model10050.add(Dense(units=num_classes, activation='softmax'))\n",
+        "\n",
+        "model10050.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
+      ],
+      "metadata": {
+        "id": "Ld4hMck_CyKT"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# вывод информации об архитектуре модели\n",
+        "print(model10050.summary())"
+      ],
+      "metadata": {
+        "id": "GVZLuKvqNZEK"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Обучаем модель\n",
+        "H = model10050.fit(X_train, y_train, validation_split=0.1, epochs=50)"
+      ],
+      "metadata": {
+        "id": "UP0suqUbNY9R"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# вывод графика ошибки по эпохам\n",
+        "plt.plot(H.history['loss'])\n",
+        "plt.plot(H.history['val_loss'])\n",
+        "plt.grid()\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('loss')\n",
+        "plt.legend(['train_loss', 'val_loss'])\n",
+        "plt.title('Loss by epochs')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "k-DhnF0SNY3K"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Оценка качества работы модели на тестовых данных\n",
+        "scores = model10050.evaluate(X_test, y_test)\n",
+        "print('Loss on test data:', scores[0])\n",
+        "print('Accuracy on test data:', scores[1])"
+      ],
+      "metadata": {
+        "id": "-7E0BUrMNYx9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# сохранение модели на диск\n",
+        "model10050.save('/content/drive/MyDrive/Colab Notebooks/models/model100in_1hide_50in_2hide.keras')"
+      ],
+      "metadata": {
+        "id": "yu11cXisCyCh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model100100 = Sequential()\n",
+        "model100100.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid'))\n",
+        "model100100.add(Dense(units=100,activation='sigmoid'))\n",
+        "model100100.add(Dense(units=num_classes, activation='softmax'))\n",
+        "\n",
+        "model100100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
+      ],
+      "metadata": {
+        "id": "pTTia0gmRFaV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# вывод информации об архитектуре модели\n",
+        "print(model100100.summary())"
+      ],
+      "metadata": {
+        "id": "XQHhKm8YRFW6"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Обучаем модель\n",
+        "H = model100100.fit(X_train, y_train, validation_split=0.1, epochs=50)"
+      ],
+      "metadata": {
+        "id": "oCgqwCmPRFTT"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ke"
+      ],
+      "metadata": {
+        "id": "YDdSpQO5RFPn"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Оценка качества работы модели на тестовых данных\n",
+        "scores = model100100.evaluate(X_test, y_test)\n",
+        "print('Loss on test data:', scores[0])\n",
+        "print('Accuracy on test data:', scores[1])"
+      ],
+      "metadata": {
+        "id": "D_WHUHCwRFMS"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# сохранение модели на диск\n",
+        "model100100.save('/content/drive/MyDrive/Colab Notebooks/models/model100in_1hide_100in_2hide.keras')"
+      ],
+      "metadata": {
+        "id": "fkBAnNf2RFDL"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# сохранение лучшей модели в папку best_model\n",
+        "model100.save('/content/drive/MyDrive/Colab Notebooks/best_model/model100.keras')"
+      ],
+      "metadata": {
+        "id": "mXGyPCNdS91i"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Загрузка модели с диска\n",
+        "from keras.models import load_model\n",
+        "model = load_model('/content/drive/MyDrive/Colab Notebooks/best_model/model100.keras')"
+      ],
+      "metadata": {
+        "id": "ILyFn-CJp1k8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# вывод тестового изображения и результата распознавания\n",
+        "n = 111\n",
+        "result = model.predict(X_test[n:n+1])\n",
+        "print('NN output:', result)\n",
+        "plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))\n",
+        "plt.show()\n",
+        "print('Real mark: ', str(np.argmax(y_test[n])))\n",
+        "print('NN answer: ', str(np.argmax(result)))"
+      ],
+      "metadata": {
+        "id": "cCk7Do1mp-xb"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# вывод тестового изображения и результата распознавания\n",
+        "n = 222\n",
+        "result = model.predict(X_test[n:n+1])\n",
+        "print('NN output:', result)\n",
+        "plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))\n",
+        "plt.show()\n",
+        "print('Real mark: ', str(np.argmax(y_test[n])))\n",
+        "print('NN answer: ', str(np.argmax(result)))"
+      ],
+      "metadata": {
+        "id": "HrL0sv-1YosF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# загрузка собственного изображения\n",
+        "from PIL import Image\n",
+        "file_data = Image.open('test.png')\n",
+        "file_data = file_data.convert('L') # перевод в градации серого\n",
+        "test_img = np.array(file_data)"
+      ],
+      "metadata": {
+        "id": "tfARmJMip_D8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# вывод собственного изображения\n",
+        "plt.imshow(test_img, cmap=plt.get_cmap('gray'))\n",
+        "plt.show()\n",
+        "# предобработка\n",
+        "test_img = test_img / 255\n",
+        "test_img = test_img.reshape(1, num_pixels)\n",
+        "# распознавание\n",
+        "result = model.predict(test_img)\n",
+        "print('I think it\\'s ', np.argmax(result))"
+      ],
+      "metadata": {
+        "id": "60zdtlMduHhT"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# загрузка собственного изображения\n",
+        "from PIL import Image\n",
+        "file2_data = Image.open('test_2.png')\n",
+        "file2_data = file2_data.convert('L') # перевод в градации серого\n",
+        "test2_img = np.array(file2_data)"
+      ],
+      "metadata": {
+        "id": "JcO7pbCSuvrv"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# вывод собственного изображения\n",
+        "plt.imshow(test2_img, cmap=plt.get_cmap('gray'))\n",
+        "plt.show()\n",
+        "# предобработка\n",
+        "test2_img = test2_img / 255\n",
+        "test2_img = test2_img.reshape(1, num_pixels)\n",
+        "# распознавание\n",
+        "result_2 = model.predict(test2_img)\n",
+        "print('I think it\\'s ', np.argmax(result_2))"
+      ],
+      "metadata": {
+        "id": "2E0evx2su4y1"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# загрузка собственного изображения, повернутого на 90 градусов\n",
+        "from PIL import Image\n",
+        "file90_data = Image.open('test90.png')\n",
+        "file90_data = file90_data.convert('L') # перевод в градации серого\n",
+        "test90_img = np.array(file90_data)"
+      ],
+      "metadata": {
+        "id": "ZsRQAhIIa_vD"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# вывод собственного изображения\n",
+        "plt.imshow(test90_img, cmap=plt.get_cmap('gray'))\n",
+        "plt.show()\n",
+        "# предобработка\n",
+        "test90_img = test90_img / 255\n",
+        "test90_img = test90_img.reshape(1, num_pixels)\n",
+        "# распознавание\n",
+        "result_3 = model.predict(test90_img)\n",
+        "print('I think it\\'s ', np.argmax(result_3))"
+      ],
+      "metadata": {
+        "id": "nQnk_zZMbM01"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# загрузка собственного изображения, повернутого на 90 градусов\n",
+        "from PIL import Image\n",
+        "file902_data = Image.open('test90_2.png')\n",
+        "file902_data = file902_data.convert('L') # перевод в градации серого\n",
+        "test902_img = np.array(file902_data)"
+      ],
+      "metadata": {
+        "id": "IXK_VfJqbhJ3"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# вывод собственного изображения\n",
+        "plt.imshow(test902_img, cmap=plt.get_cmap('gray'))\n",
+        "plt.show()\n",
+        "# предобработка\n",
+        "test902_img = test902_img / 255\n",
+        "test902_img = test902_img.reshape(1, num_pixels)\n",
+        "# распознавание\n",
+        "result_4 = model.predict(test902_img)\n",
+        "print('I think it\\'s ', np.argmax(result_4))"
+      ],
+      "metadata": {
+        "id": "S5WcjVtMb-bp"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "metadata": {
+        "id": "n4-_iFTWXNTJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

labworks/LW1/report.md

@@ -0,0 +1,577 @@
+# Отчет по лабораторной работе №1
+Аникеев Андрей, Чагин Сергей, А-02-22
+
+## 1. В среде Google Colab создание нового блокнота.
+```
+import os
+os.chdir('/content/drive/MyDrive/Colab Notebooks')
+```
+
+1.1 Импорт необходимых модулей.
+```
+from tensorflow import keras
+import matplotlib.pyplot as plt
+import numpy as np
+import sklearn
+```
+
+## 2. Загрузка датасета.
+```
+from keras.datasets import mnist
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+```
+
+## 3. Разбиение набора данных на обучающий и тестовый.
+```
+from sklearn.model_selection import train_test_split
+```
+3.1 Объединение в один набор.
+```
+X = np.concatenate((X_train, X_test))
+y = np.concatenate((y_train, y_test))
+```
+3.2 Разбиение по вариантам. (5 бригада -> k=4*5-1)
+```
+X_train, X_test, y_train, y_test = train_test_split(X, y,test_size = 10000,train_size = 60000, random_state = 19)
+```
+
+3.3 Вывод размерностей.
+```
+print('Shape of X train:', X_train.shape)
+print('Shape of y train:', y_train.shape)
+```
+
+> Shape of X train: (60000, 28, 28)
+> Shape of y train: (60000,) 
+
+## 4. Вывод обучающих данных.
+4.1 Выведем первые четыре элемента обучающих данных.
+```
+plt.figure(figsize=(10, 3))
+for i in range(4):
+    plt.subplot(1, 4, i + 1)
+    plt.imshow(X_train[i], cmap='gray')
+    plt.title(f'Label: {y_train[i]}')
+    plt.axis('off')
+plt.tight_layout()
+plt.show()
+```
+
+![отображение элементов](1.png)
+
+## 5. Предобработка данных.
+5.1 Развернем каждое изображение в вектор.
+```
+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)
+```
+
+> Shape of transformed X train: (60000, 784) 
+
+5.2 Переведем метки в one-hot.
+```
+from keras.utils import to_categorical
+
+y_train = to_categorical(y_train)
+y_test = to_categorical(y_test)
+
+print('Shape of transformed y train:', y_train.shape)
+num_classes = y_train.shape[1]
+```
+
+> Shape of transformed y train: (60000, 10) 
+
+## 6. Реализация и обучение однослойной нейронной сети.
+```
+from keras.models import Sequential
+from keras.layers import Dense
+```
+
+6.1. Создаем модель - объявляем ее объектом класса Sequential, добавляем выходной слой.
+```
+model = Sequential()
+model.add(Dense(units=num_classes, activation='softmax'))
+```
+6.2. Компилируем модель.
+```
+model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
+print(model.summary())
+```
+
+>Model: "sequential_6"
+>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
+>┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
+>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
+>│ dense_18 (Dense)                │ ?                      │   0 (unbuilt) │
+>└─────────────────────────────────┴────────────────────────┴───────────────┘
+>Total params: 0 (0.00 B)
+>Trainable params: 0 (0.00 B)
+>Non-trainable params: 0 (0.00 B)
+
+6.3 Обучаем модель.
+```
+H = model.fit(X_train, y_train, validation_split=0.1, epochs=50)
+```
+
+6.4 Выводим график функции ошибки
+```
+plt.plot(H.history['loss'])
+plt.plot(H.history['val_loss'])
+plt.grid()
+plt.xlabel('Epochs')
+plt.ylabel('loss')
+plt.legend(['train_loss', 'val_loss'])
+plt.title('Loss by epochs')
+plt.show()
+```
+
+![график функции ошибки](2.png)
+
+## 7. Применение модели к тестовым данным.
+```
+scores = model.evaluate(X_test, y_test)
+print('Loss on test data:', scores[0])
+print('Accuracy on test data:', scores[1])
+```
+
+>accuracy: 0.9213 - loss: 0.2825
+>Loss on test data: 0.28365787863731384
+>Accuracy on test data: 0.9225000143051147 
+
+## 8. Добавление одного скрытого слоя.
+8.1 При 100 нейронах в скрытом слое.
+```
+model100 = Sequential()
+model100.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid'))
+model100.add(Dense(units=num_classes, activation='softmax'))
+
+model100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']
+
+print(model100.summary())
+```
+
+>Model: "sequential_10"
+>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
+>┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
+>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
+>│ dense_19 (Dense)                │ (None, 100)            │        78,500 │
+>├─────────────────────────────────┼────────────────────────┼───────────────┤
+>│ dense_20 (Dense)                │ (None, 10)             │         1,010 │
+>└─────────────────────────────────┴────────────────────────┴───────────────┘
+>Total params: 79,510 (310.59 KB)
+>Trainable params: 79,510 (310.59 KB)
+>Non-trainable params: 0 (0.00 B)
+
+8.2 Обучение модели.
+```
+H = model100.fit(X_train, y_train, validation_split=0.1, epochs=50)
+```
+
+8.3 График функции ошибки.
+```
+plt.plot(H.history['loss'])
+plt.plot(H.history['val_loss'])
+plt.grid()
+plt.xlabel('Epochs')
+plt.ylabel('loss')
+plt.legend(['train_loss', 'val_loss'])
+plt.title('Loss by epochs')
+plt.show()
+```
+
+![график функции ошибки](3.png)
+
+```
+scores = model100.evaluate(X_test, y_test)
+print('Loss on test data:', scores[0])
+print('Accuracy on test data:', scores[1])
+```
+
+>accuracy: 0.9465 - loss: 0.1946
+>Loss on test data: 0.19745595753192902
+>Accuracy on test data: 0.9442999958992004
+
+8.4 При 300 нейронах в скрытом слое.
+```
+model300 = Sequential()
+model300.add(Dense(units=300,input_dim=num_pixels, activation='sigmoid'))
+model300.add(Dense(units=num_classes, activation='softmax'))
+
+model300.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
+
+print(model300.summary())
+```
+
+>Model: "sequential_14"
+>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
+>┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
+>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
+>│ dense_27 (Dense)                │ (None, 300)            │       235,500 │
+>├─────────────────────────────────┼────────────────────────┼───────────────┤
+>│ dense_28 (Dense)                │ (None, 10)             │         3,010 │
+>└─────────────────────────────────┴────────────────────────┴───────────────┘
+>Total params: 238,510 (931.68 KB)
+>Trainable params: 238,510 (931.68 KB)
+>Non-trainable params: 0 (0.00 B)
+
+8.5 Обучение модели.
+```
+H = model300.fit(X_train, y_train, validation_split=0.1, epochs=50)
+```
+
+8.6 Вывод графиков функции ошибки.
+```
+plt.plot(H.history['loss'])
+plt.plot(H.history['val_loss'])
+plt.grid()
+plt.xlabel('Epochs')
+plt.ylabel('loss')
+plt.legend(['train_loss', 'val_loss'])
+plt.title('Loss by epochs')
+plt.show()
+```
+
+![график функции ошибки](4.png)
+
+```
+scores = model300.evaluate(X_test, y_test)
+print('Loss on test data:', scores[0])
+print('Accuracy on test data:', scores[1])
+```
+
+>accuracy: 0.9361 - loss: 0.2237
+>Loss on test data: 0.22660093009471893
+>Accuracy on test data: 0.9348000288009644
+
+8.7 При 500 нейронах в скрытом слое.
+```
+model500 = Sequential()
+model500.add(Dense(units=500,input_dim=num_pixels, activation='sigmoid'))
+model500.add(Dense(units=num_classes, activation='softmax'))
+
+model500.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
+
+print(model500.summary())
+```
+
+>Model: "sequential_16"
+>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
+>┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
+>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
+>│ dense_31 (Dense)                │ (None, 500)            │       392,500 │
+>├─────────────────────────────────┼────────────────────────┼───────────────┤
+>│ dense_32 (Dense)                │ (None, 10)             │         5,010 │
+>└─────────────────────────────────┴────────────────────────┴───────────────┘
+>Total params: 397,510 (1.52 MB)
+>Trainable params: 397,510 (1.52 MB)
+>Non-trainable params: 0 (0.00 B)
+
+8.8 Обучение модели.
+```
+H = model500.fit(X_train, y_train, validation_split=0.1, epochs=50)
+```
+
+8.9 Вывод графиков функции ошибки.
+```
+plt.plot(H.history['loss'])
+plt.plot(H.history['val_loss'])
+plt.grid()
+plt.xlabel('Epochs')
+plt.ylabel('loss')
+plt.legend(['train_loss', 'val_loss'])
+plt.title('Loss by epochs')
+plt.show()
+```
+
+![график функции ошибки](5.png)
+
+```
+scores = model500.evaluate(X_test, y_test)
+print('Loss on test data:', scores[0])
+print('Accuracy on test data:', scores[1])
+```
+
+>accuracy: 0.9306 - loss: 0.2398
+>Loss on test data: 0.24357788264751434
+>Accuracy on test data: 0.9304999709129333
+
+Как мы видим, лучшая метрика получилась при архитектуре со 100 нейронами в скрытом слое:
+Ошибка на тестовых данных: 0.19745595753192902
+Точность тестовых данных: 0.9442999958992004
+
+## 9. Добавление второго скрытого слоя.
+9.1 При 50 нейронах во втором скрытом слое.
+```
+model10050 = Sequential()
+model10050.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid'))
+model10050.add(Dense(units=50,activation='sigmoid'))
+model10050.add(Dense(units=num_classes, activation='softmax'))
+
+model10050.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
+
+print(model10050.summary())
+```
+
+>Model: "sequential_17"
+>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
+>┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
+>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
+>│ dense_33 (Dense)                │ (None, 100)            │        78,500 │
+>├─────────────────────────────────┼────────────────────────┼───────────────┤
+>│ dense_34 (Dense)                │ (None, 50)             │         5,050 │
+>├─────────────────────────────────┼────────────────────────┼───────────────┤
+>│ dense_35 (Dense)                │ (None, 10)             │           510 │
+>└─────────────────────────────────┴────────────────────────┴───────────────┘
+>Total params: 84,060 (328.36 KB)
+>Trainable params: 84,060 (328.36 KB)
+>Non-trainable params: 0 (0.00 B) 
+
+9.2 Обучаем модель.
+```
+H = model10050.fit(X_train, y_train, validation_split=0.1, epochs=50)
+```
+
+9.3 Выводим график функции ошибки.
+```
+plt.plot(H.history['loss'])
+plt.plot(H.history['val_loss'])
+plt.grid()
+plt.xlabel('Epochs')
+plt.ylabel('loss')
+plt.legend(['train_loss', 'val_loss'])
+plt.title('Loss by epochs')
+plt.show()
+```
+
+![график функции ошибки](6.png)
+
+```
+scores = model10050.evaluate(X_test, y_test)
+print('Loss on test data:', scores[0])
+print('Accuracy on test data:', scores[1])
+```
+
+>accuracy: 0.9439 - loss: 0.1962
+>Loss on test data: 0.1993969976902008
+>Accuracy on test data: 0.9438999891281128
+
+9.4 При 100 нейронах во втором скрытом слое.
+```
+model100100 = Sequential()
+model100100.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid'))
+model100100.add(Dense(units=100,activation='sigmoid'))
+model100100.add(Dense(units=num_classes, activation='softmax'))
+
+model100100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
+
+print(model100100.summary())
+```
+
+>Model: "sequential_18"
+>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
+>┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
+>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
+>│ dense_36 (Dense)                │ (None, 100)            │        78,500 │
+>├─────────────────────────────────┼────────────────────────┼───────────────┤
+>│ dense_37 (Dense)                │ (None, 100)            │        10,100 │
+>├─────────────────────────────────┼────────────────────────┼───────────────┤
+>│ dense_38 (Dense)                │ (None, 10)             │         1,010 │
+>└─────────────────────────────────┴────────────────────────┴───────────────┘
+>Total params: 89,610 (350.04 KB)
+>Trainable params: 89,610 (350.04 KB)
+>Non-trainable params: 0 (0.00 B)
+
+9.5 Обучаем модель.
+```
+H = model100100.fit(X_train, y_train, validation_split=0.1, epochs=50)
+```
+
+9.6 Выводим график функции ошибки.
+```
+plt.plot(H.history['loss'])
+plt.plot(H.history['val_loss'])
+plt.grid()
+plt.xlabel('Epochs')
+plt.ylabel('loss')
+plt.legend(['train_loss', 'val_loss'])
+plt.title('Loss by epochs')
+plt.show()
+```
+
+![график функции ошибки](7.png)
+
+```
+scores = model100100.evaluate(X_test, y_test)
+print('Loss on test data:', scores[0])
+print('Accuracy on test data:', scores[1])
+```
+
+>accuracy: 0.9449 - loss: 0.1931
+>Loss on test data: 0.19571688771247864
+>Accuracy on test data: 0.9435999989509583 
+
+## 10. Результаты исследования архитектур нейронной сети.
+
+| Количество скрытых слоев | Количество нейронов в первом скрытом слое | Количество нейронов во втором скрытом слое | Значение метрики качества классификации |
+|--------------------------|-------------------------------------------|--------------------------------------------|------------------------------------------|
+| 0                        | -                                         | -                                          | 0.9225000143051147                       |
+| 1                        | 100                                       | -                                          | 0.9442999958992004                       |
+| 1                        | 300                                       | -                                          | 0.9348000288009644                       |
+| 1                        | 500                                       | -                                          | 0.9304999709129333                       |
+| 2                        | 100                                       | 50                                         | 0.9438999891281128                       |
+| 2                        | 100                                       | 100                                        | 0.9435999989509583                       |
+
+Анализ результатов показал, что наивысшую точность (около 94.5%) демонстрируют модели со сравнительно простой архитектурой: однослойная сеть со 100 нейронами и двухслойная конфигурация (100 и 50 нейронов). Усложнение модели за счет увеличения количества слоев или нейронов не привело к улучшению качества, а в некоторых случаях даже вызвало его снижение. Это свидетельствует о том, что для относительно простого набора данных MNIST более сложные архитектуры склонны к переобучению, в то время как простые модели лучше обобщают закономерности.
+
+## 11. Сохранение наилучшей модели на диск.
+```
+model100.save('/content/drive/MyDrive/Colab Notebooks/best_model/model100.keras')
+```
+
+11.1 Загрузка лучшей модели с диска.
+```
+from keras.models import load_model
+model = load_model('/content/drive/MyDrive/Colab Notebooks/best_model/model100.keras')
+```
+
+## 12. Вывод тестовых изображений и результатов распознаваний.
+```
+n = 111
+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: ', str(np.argmax(y_test[n])))
+print('NN answer: ', str(np.argmax(result)))
+```
+
+>NN output: [[1.1728607e-03 5.4896927e-06 3.3185919e-05 2.6362878e-04 4.8558863e-06
+>9.9795568e-01 1.9454242e-07 1.6833146e-05 4.9621973e-04 5.1067746e-05]]
+![alt text](8.png)
+>Real mark:  5
+>NN answer:  5
+
+```
+n = 222
+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: ', str(np.argmax(y_test[n])))
+print('NN answer: ', str(np.argmax(result)))
+```
+
+>NN output: [[1.02687673e-05 2.02151591e-06 2.86183599e-03 8.74871985e-05
+>1.51387369e-02 6.32769879e-05 3.97122385e-05 4.11829986e-02 1.06158564e-04 9.40507472e-01]]
+![alt text](9.png)
+>Real mark:  9
+>NN answer:  9
+
+## 13. Тестирование на собственных изображениях.
+
+13.1 Загрузка 1 собственного изображения.
+```
+from PIL import Image
+file_data = Image.open('test.png')
+file_data = file_data.convert('L') # перевод в градации серого
+test_img = np.array(file_data)
+```
+
+13.2 Вывод собственного изображения.
+```
+plt.imshow(test_img, cmap=plt.get_cmap('gray'))
+plt.show()
+```
+
+![1 изображение](10.png)
+
+13.3 Предобработка.
+```
+test_img = test_img / 255
+test_img = test_img.reshape(1, num_pixels)
+```
+
+13.4 Распознавание.
+```
+result = model.predict(test_img)
+print('I think it\'s ', np.argmax(result))
+```
+>I think it's  5
+
+13.5 Тест 2 изображения.
+```
+from PIL import Image
+file2_data = Image.open('test_2.png')
+file2_data = file2_data.convert('L') # перевод в градации серого
+test2_img = np.array(file2_data)
+```
+
+```
+plt.imshow(test2_img, cmap=plt.get_cmap('gray'))
+plt.show()
+```
+
+![2 изображение](11.png)
+
+```
+test2_img = test2_img / 255
+test2_img = test2_img.reshape(1, num_pixels)
+```
+
+```
+result_2 = model.predict(test2_img)
+print('I think it\'s ', np.argmax(result_2))
+```
+
+>I think it's  2
+
+Сеть корректно распознала цифры на изображениях.
+
+## 14. Тестирование на повернутых изображениях.
+```
+from PIL import Image
+file90_data = Image.open('test90.png')
+file90_data = file90_data.convert('L') # перевод в градации серого
+test90_img = np.array(file90_data)
+
+plt.imshow(test90_img, cmap=plt.get_cmap('gray'))
+plt.show()
+```
+
+![alt text](12.png)
+
+```
+test90_img = test90_img / 255
+test90_img = test90_img.reshape(1, num_pixels)
+
+result_3 = model.predict(test90_img)
+print('I think it\'s ', np.argmax(result_3))
+```
+
+>I think it's  7
+
+```
+from PIL import Image
+file902_data = Image.open('test90_2.png')
+file902_data = file902_data.convert('L') # перевод в градации серого
+test902_img = np.array(file902_data)
+
+plt.imshow(test902_img, cmap=plt.get_cmap('gray'))
+plt.show()
+```
+
+![alt text](13.png)
+
+```
+test902_img = test902_img / 255
+test902_img = test902_img.reshape(1, num_pixels)
+
+result_4 = model.predict(test902_img)
+print('I think it\'s ', np.argmax(result_4))
+```
+
+>I think it's  7
+
+Сеть не распознала цифры на изображениях корректно.