From 3289b87a5d5c9edd348d4fc686785960f76cf954 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, 30 Nov 2025 12:00:37 +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_2.ipynb | 346 ++++++++++++++++++++++++++++++++++++ 1 file changed, 346 insertions(+) create mode 100644 labworks/LW3/IS_LR3_2.ipynb diff --git a/labworks/LW3/IS_LR3_2.ipynb b/labworks/LW3/IS_LR3_2.ipynb new file mode 100644 index 0000000..c765475 --- /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": [ + "**Пункт 1**" + ], + "metadata": { + "id": "KR8uP1u_tFii" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "v8fjN3CMpmzp" + }, + "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": "VMuk53SHqFE6" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Пункт 2**" + ], + "metadata": { + "id": "bie8IdvhtMwI" + } + }, + { + "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": "zU_qTq3QpSaj" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Пункт 3**" + ], + "metadata": { + "id": "EKz2pMH5tPgM" + } + }, + { + "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 = 15)" + ], + "metadata": { + "id": "Tj2SdIX6qjyS" + }, + "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": "rxfIoGknpVr2" + }, + "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": "ELkzGpxQpYss" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Пункт 4**" + ], + "metadata": { + "id": "R8UnsPwFtcT6" + } + }, + { + "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": "tLtI_dWgpb5Q" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Пункт 5**" + ], + "metadata": { + "id": "OQTGDyuytpyz" + } + }, + { + "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()" + ], + "metadata": { + "id": "fchBhH0mpffb" + }, + "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": "pt4hPpfLpiAR" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Пункт 6**" + ], + "metadata": { + "id": "CyI5uGgetwim" + } + }, + { + "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": "niQVFBRnpklL" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Пункт 7**" + ], + "metadata": { + "id": "-Os4bCnAtzCP" + } + }, + { + "cell_type": "code", + "source": [ + "# ПРАВИЛЬНО распознанное изображение\n", + "n = 10\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": "oLC2nN-MpnVD" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# НЕВЕРНО 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": "qMkBgHiqppyZ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Пункт 8**" + ], + "metadata": { + "id": "RVk_bSDct3Km" + } + }, + { + "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()" + ], + "metadata": { + "id": "isaoRHSXpLSA" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file