From 7177c7a2322ebee22197cafc50c9ce1e28392cb3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9F=D0=B8=D0=B2=D0=BE=D0=B2=D0=B0=D1=80=D0=BE=D0=B2=20?= =?UTF-8?q?=D0=AF=D1=80=D0=BE=D1=81=D0=BB=D0=B0=D0=B2?= Date: Sun, 21 Sep 2025 08:51:18 +0000 Subject: [PATCH] =?UTF-8?q?=D0=97=D0=B0=D0=B3=D1=80=D1=83=D0=B7=D0=B8?= =?UTF-8?q?=D0=BB(=D0=B0)=20=D1=84=D0=B0=D0=B9=D0=BB=D1=8B=20=D0=B2=20'lab?= =?UTF-8?q?works/LW1'?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- labworks/LW1/IS_LR1.ipynb | 868 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 868 insertions(+) create mode 100644 labworks/LW1/IS_LR1.ipynb diff --git a/labworks/LW1/IS_LR1.ipynb b/labworks/LW1/IS_LR1.ipynb new file mode 100644 index 0000000..7fda3fa --- /dev/null +++ b/labworks/LW1/IS_LR1.ipynb @@ -0,0 +1,868 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "CAumUvAGaImn" + }, + "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": "h5MSWSsQamWR" + }, + "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": "95AfnWl1aq9X" + }, + "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 = 15)" + ], + "metadata": { + "id": "F2Fe8Fa6av1X" + }, + "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": "w5R3s-subD5z" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Вывод 4 изображений\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()" + ], + "metadata": { + "id": "YmYWjSeDbKFg" + }, + "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": "NGKvRZ8fbypE" + }, + "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": "dKZDth4wdMoi" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from keras.models import Sequential\n", + "from keras.layers import Dense" + ], + "metadata": { + "id": "HdlasD8UdSFr" + }, + "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": "f7EFobe4dTjU" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# вывод информации об архитектуре модели\n", + "print(model.summary())" + ], + "metadata": { + "id": "Fr_Lnir_eTUS" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# обучение модели\n", + "H = model.fit(X_train, y_train, validation_split=0.1, epochs=50)" + ], + "metadata": { + "id": "P4jek-2sedhi" + }, + "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": "JUeBjeS0ffg2" + }, + "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": "9h5aG6MtfnjN" + }, + "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": "31ngORxnfsJb" + }, + "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": "GuUp0o_nf_Oq" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# вывод информации об архитектуре модели\n", + "print(model100.summary())" + ], + "metadata": { + "id": "1RJG5PfSgSdz" + }, + "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": "Ofd6o3nzgc8D" + }, + "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": "On3RA9ZghcLj" + }, + "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": "d-2h4TVuhemj" + }, + "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": "1mvHa_c8hjJx" + }, + "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": "WO3ZHI6xhlVt" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# вывод информации об архитектуре модели\n", + "print(model300.summary())" + ], + "metadata": { + "id": "BqRtNfophpf3" + }, + "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": "YrP4IANqhwjf" + }, + "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": "M7D5NYCSiqzI" + }, + "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": "5dBUsxjVivJU" + }, + "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": "0GB5tz5eizCo" + }, + "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": "9FlJqDcci26k" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# вывод информации об архитектуре модели\n", + "print(model500.summary())" + ], + "metadata": { + "id": "TbPS-5fKi9mZ" + }, + "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": "rODU_cugjBOX" + }, + "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": "7uCJOOJGkTCc" + }, + "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": "H5BhhLZrkWFq" + }, + "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": "Uyv2pf5FkYjc" + }, + "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": "0X6rM1m6klas" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# вывод информации об архитектуре модели\n", + "print(model10050.summary())" + ], + "metadata": { + "id": "CJRW6vaKkm9o" + }, + "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": "wWbPA8j4k18a" + }, + "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": "BnxtXX1kl33n" + }, + "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": "c97Qx3pul98e" + }, + "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": "Dn5qMhDAmBlZ" + }, + "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": "YIfzGZVzmCqT" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# вывод информации об архитектуре модели\n", + "print(model100100.summary())" + ], + "metadata": { + "id": "aK8ffWILmIDg" + }, + "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": "Dz7X9T55mLCh" + }, + "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": "eF7B4wucnIPS" + }, + "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": "yxdjaq6bnNXt" + }, + "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": "Sr9bCq_KnP85" + }, + "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": "BV7wEu2SoMaB" + }, + "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": "hg2PYRgwoTiU" + }, + "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": "A8O5K-_4oeK9" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# вывод тестового изображения и результата распознавания\n", + "n = 123\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": "pk03l3jdpUp5" + }, + "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": "PkjvyImOpii6" + }, + "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": "wcbVyWwusUx6" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# загрузка собственного изображения\n", + "from PIL import Image\n", + "file2_data = Image.open('test2.png')\n", + "file2_data = file2_data.convert('L') # перевод в градации серого\n", + "test2_img = np.array(file2_data)" + ], + "metadata": { + "id": "JY7tkymctESN" + }, + "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": "saUm4dytutDS" + }, + "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": "3DV_1KeKvo3S" + }, + "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": "uBXsSP-iweMO" + }, + "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": "s9FSbb99wh_9" + }, + "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": "ppK14r4-w0Av" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "ZaKbfAx8xaud" + } + } + ] +} \ No newline at end of file