From ca98677d2b7cd6969c2845ab7a0e902305571cc4 Mon Sep 17 00:00:00 2001 From: AnikeevAnA Date: Sat, 15 Nov 2025 12:41:05 +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/LW3'?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- labworks/LW3/IS_LR3.ipynb | 447 ++++++++++++++++++++++++++++++++++++ labworks/LW3/IS_LR3_2.ipynb | 346 ++++++++++++++++++++++++++++ 2 files changed, 793 insertions(+) create mode 100644 labworks/LW3/IS_LR3.ipynb create mode 100644 labworks/LW3/IS_LR3_2.ipynb diff --git a/labworks/LW3/IS_LR3.ipynb b/labworks/LW3/IS_LR3.ipynb new file mode 100644 index 0000000..40fe028 --- /dev/null +++ b/labworks/LW3/IS_LR3.ipynb @@ -0,0 +1,447 @@ +{ + "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": [ + "1 пункт" + ], + "metadata": { + "id": "S59WEX1lbXWW" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "lhabo1q_VXgc" + }, + "outputs": [], + "source": [ + "import os\n", + "os.chdir('/content/drive/MyDrive/Colab Notebooks/IS_LR3')" + ] + }, + { + "cell_type": "code", + "source": [ + "# импорт модулей\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": "ZYpnLJOCaSFR" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "2 пункт\n" + ], + "metadata": { + "id": "QTplfsEEbWtr" + } + }, + { + "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": "FmAqO707aR_5" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "3 пункт\n" + ], + "metadata": { + "id": "VR6XttyDbpGS" + } + }, + { + "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 = 19)" + ], + "metadata": { + "id": "idfAHcp9aR32" + }, + "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)\n", + "\n", + "print('Shape of X test:', X_test.shape)\n", + "print('Shape of y test:', y_test.shape)" + ], + "metadata": { + "id": "ZcpI4-Mfb8_M" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "4 пункт" + ], + "metadata": { + "id": "MsUxxLu4dXsF" + } + }, + { + "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", + "\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": "xIB0CdYqdWPv" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "5 пункт" + ], + "metadata": { + "id": "1HQjX5z6dp3h" + } + }, + { + "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()\n", + "\n" + ], + "metadata": { + "id": "owMPTAvseQFB" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "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": "thNo1LXUepwN" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "6 пункт" + ], + "metadata": { + "id": "8Vvr7f3ng2EI" + } + }, + { + "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": "JUg1WDEngza0" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "7 пункт" + ], + "metadata": { + "id": "EYoMHxN_hPlv" + } + }, + { + "cell_type": "code", + "source": [ + "# вывод первого тестового изображения и результата распознавания\n", + "n = 222\n", + "result = model.predict(X_test[n:n+1])\n", + "print('NN output:', result)\n", + "plt.show()\n", + "plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))\n", + "print('Real mark: ', np.argmax(y_test[n]))\n", + "print('NN answer: ', np.argmax(result))" + ], + "metadata": { + "id": "ozvxCjFFhF0i" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# вывод второго тестового изображения и результата распознавания\n", + "n = 111\n", + "result = model.predict(X_test[n:n+1])\n", + "print('NN output:', result)\n", + "plt.show()\n", + "plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))\n", + "print('Real mark: ', np.argmax(y_test[n]))\n", + "print('NN answer: ', np.argmax(result))" + ], + "metadata": { + "id": "XrQQWslhjhxA" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "8 пункт" + ], + "metadata": { + "id": "njvWDE6whDUz" + } + }, + { + "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": "HuPTHd_YkZ-V" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "9 пункт\n" + ], + "metadata": { + "id": "uNi4E7gPl8rd" + } + }, + { + "cell_type": "code", + "source": [ + "# загрузка собственного изображения 1\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)\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": "cQUHadWyl_d4" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# загрузка собственного изображения 2\n", + "from PIL import Image\n", + "file_data = Image.open('test_2.png')\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": "D-LsJFTpmsCL" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "10 пункт" + ], + "metadata": { + "id": "B7dQZmiTnFjk" + } + }, + { + "cell_type": "code", + "source": [ + "# путь к сохранённой модели из ЛР1\n", + "model_fc = keras.models.load_model('/content/drive/MyDrive/Colab Notebooks/best_model/model100.keras')\n", + "\n", + "# архитектура модели\n", + "model_fc.summary()\n", + "\n" + ], + "metadata": { + "id": "JAFvXfzHnEyf" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# подготовка тестовых данных для полносвязной модели\n", + "X_test_fc = X_test.reshape(X_test.shape[0], 28*28) # (10000, 784)\n", + "y_test_fc = y_test # если в ЛР3 ты уже перевёл метки в one-hot\n", + "\n", + "# оценка качества, как в п. 6\n", + "scores = model_fc.evaluate(X_test_fc, y_test_fc, verbose=0)\n", + "print('Loss on test data (FC model):', scores[0])\n", + "print('Accuracy on test data (FC model):', scores[1])" + ], + "metadata": { + "id": "iSMKJsCznIKM" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "11 пункт" + ], + "metadata": { + "id": "YE0Ne5Y5pUaZ" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "c22hf9CjpT6Z" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "S4SaPgPbnIAp" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/labworks/LW3/IS_LR3_2.ipynb b/labworks/LW3/IS_LR3_2.ipynb new file mode 100644 index 0000000..e177965 --- /dev/null +++ b/labworks/LW3/IS_LR3_2.ipynb @@ -0,0 +1,346 @@ +{ + "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": [ + "1 пункт" + ], + "metadata": { + "id": "n2S-d-dy1com" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "EfImqmoI0eWN" + }, + "outputs": [], + "source": [ + "import os\n", + "os.chdir('/content/drive/MyDrive/Colab Notebooks/IS_LR3')" + ] + }, + { + "cell_type": "code", + "source": [ + "# импорт модулей\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": "VBMRl6Iw1eqz" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "2 пункт" + ], + "metadata": { + "id": "LnvGg6FZ1mDT" + } + }, + { + "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": "90-GJpwO1k0u" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "3 пункт" + ], + "metadata": { + "id": "ArL8T5q32POX" + } + }, + { + "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 = 50000,\n", + " random_state = 19)" + ], + "metadata": { + "id": "I9pe41Ni1kvJ" + }, + "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)\n", + "\n", + "print('Shape of X test:', X_test.shape)\n", + "print('Shape of y test:', y_test.shape)" + ], + "metadata": { + "id": "m-KhW4L93B9o" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# вывод 25 изображений из обучающей выборки с подписями классов\n", + "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": "p6RhtysM3Fol" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "4 пункт" + ], + "metadata": { + "id": "ZtUEifrW31w4" + } + }, + { + "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", + "# Расширяем размерность входных данных, чтобы каждое изображение имело\n", + "# размерность (высота, ширина, количество каналов)\n", + "\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": "_1EvYA5C31oT" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "5 пункт" + ], + "metadata": { + "id": "HiZ1igLn4ZvE" + } + }, + { + "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.Conv2D(128, kernel_size=(3, 3), activation=\"relu\"))\n", + "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\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", + "model.summary()\n" + ], + "metadata": { + "id": "szeMGpaW31it" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "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": "CMUVJn2v3bx_" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "6 пункт" + ], + "metadata": { + "id": "CTohcQAy7JGG" + } + }, + { + "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": "ag1rP16A4oJC" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "7 пункт" + ], + "metadata": { + "id": "lWZIle5r96tj" + } + }, + { + "cell_type": "code", + "source": [ + "# ПРАВИЛЬНО распознанное изображение\n", + "n = 0\n", + "result = model.predict(X_test[n:n+1])\n", + "print('NN output:', result)\n", + "\n", + "plt.imshow(X_test[n])\n", + "plt.show()\n", + "\n", + "print('Real class: ', np.argmax(y_test[n]), '->', class_names[np.argmax(y_test[n])])\n", + "print('NN answer:', np.argmax(result), '->', class_names[np.argmax(result)])" + ], + "metadata": { + "id": "b8gAO65m9yZG" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# НЕВЕРНО распознанное изображение\n", + "n = 9\n", + "result = model.predict(X_test[n:n+1])\n", + "print('NN output:', result)\n", + "\n", + "plt.imshow(X_test[n])\n", + "plt.show()\n", + "\n", + "print('Real class: ', np.argmax(y_test[n]), '->', class_names[np.argmax(y_test[n])])\n", + "print('NN answer:', np.argmax(result), '->', class_names[np.argmax(result)])" + ], + "metadata": { + "id": "gZiOC9Ke-BCz" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "8 пункт" + ], + "metadata": { + "id": "MaamG6bP_xsp" + } + }, + { + "cell_type": "code", + "source": [ + "# истинные метки классов\n", + "true_labels = np.argmax(y_test, axis=1)\n", + "\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", + "\n", + "# вычисление матрицы ошибок\n", + "conf_matrix = confusion_matrix(true_labels, predicted_labels)\n", + "\n", + "# отрисовка матрицы ошибок в виде \"тепловой карты\"\n", + "display = ConfusionMatrixDisplay(confusion_matrix=conf_matrix,\n", + " display_labels=class_names)\n", + "display.plot(xticks_rotation=45)\n", + "plt.show()\n" + ], + "metadata": { + "id": "Ix27-pKH_yG-" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file