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135 строки
4.6 KiB
Python
135 строки
4.6 KiB
Python
# -*- coding: utf-8 -*-
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"""IS_LR3_2
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Automatically generated by Colab.
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Original file is located at
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https://colab.research.google.com/drive/1ATu8wYdHLgC6dGpFJboJXvIoTohx65eT
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"""
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from google.colab import drive
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drive.mount('/content/drive')
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import os
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os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab3')
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from tensorflow import keras
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from tensorflow.keras.models import Sequential
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from tensorflow.keras import layers
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.metrics import classification_report, confusion_matrix
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from sklearn.metrics import ConfusionMatrixDisplay
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# загрузка датасета
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from keras.datasets import cifar10
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(X_train, y_train), (X_test, y_test) = cifar10.load_data()
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# создание своего разбиения датасета
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from sklearn.model_selection import train_test_split
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# объединяем в один набор
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X = np.concatenate((X_train, X_test))
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y = np.concatenate((y_train, y_test))
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# разбиваем по вариантам
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X_train, X_test, y_train, y_test = train_test_split(X, y,
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test_size = 10000,
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train_size = 50000,
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random_state = 7)
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# вывод размерностей
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print('Shape of X train:', X_train.shape)
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print('Shape of y train:', y_train.shape)
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print('Shape of X test:', X_test.shape)
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print('Shape of y test:', y_test.shape)
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class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
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'dog', 'frog', 'horse', 'ship', 'truck']
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plt.figure(figsize=(10,10))
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for i in range(25):
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plt.subplot(5,5,i+1)
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plt.xticks([])
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plt.yticks([])
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plt.grid(False)
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plt.imshow(X_train[i])
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plt.xlabel(class_names[y_train[i][0]])
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plt.show()
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# Зададим параметры данных и модели
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num_classes = 10
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input_shape = (32, 32, 3)
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# Приведение входных данных к диапазону [0, 1]
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X_train = X_train / 255
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X_test = X_test / 255
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print('Shape of transformed X train:', X_train.shape)
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print('Shape of transformed X test:', X_test.shape)
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# переведем метки в one-hot
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y_train = keras.utils.to_categorical(y_train, num_classes)
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y_test = keras.utils.to_categorical(y_test, num_classes)
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print('Shape of transformed y train:', y_train.shape)
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print('Shape of transformed y test:', y_test.shape)
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# создаем модель
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model = Sequential()
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# Блок 1
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model.add(layers.Conv2D(32, (3, 3), padding="same",
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activation="relu", input_shape=input_shape))
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model.add(layers.BatchNormalization())
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model.add(layers.Conv2D(32, (3, 3), padding="same", activation="relu"))
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model.add(layers.BatchNormalization())
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model.add(layers.MaxPooling2D((2, 2)))
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model.add(layers.Dropout(0.25))
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# Блок 2
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model.add(layers.Conv2D(64, (3, 3), padding="same", activation="relu"))
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model.add(layers.BatchNormalization())
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model.add(layers.Conv2D(64, (3, 3), padding="same", activation="relu"))
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model.add(layers.BatchNormalization())
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model.add(layers.MaxPooling2D((2, 2)))
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model.add(layers.Dropout(0.25))
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model.add(layers.Flatten())
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model.add(layers.Dense(128, activation='relu'))
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model.add(layers.Dropout(0.5))
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model.add(layers.Dense(num_classes, activation="softmax"))
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model.summary()
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batch_size = 64
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epochs = 50
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model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
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model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)
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scores = model.evaluate(X_test, y_test)
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print('Loss on test data:', scores[0])
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print('Accuracy on test data:', scores[1])
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for n in [5,17]:
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result = model.predict(X_test[n:n+1])
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print('NN output:', result)
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plt.imshow(X_test[n].reshape(32,32,3), cmap=plt.get_cmap('gray'))
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plt.show()
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print('Real mark: ', np.argmax(y_test[n]))
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print('NN answer: ', np.argmax(result))
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# истинные метки классов
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true_labels = np.argmax(y_test, axis=1)
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# предсказанные метки классов
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predicted_labels = np.argmax(model.predict(X_test), axis=1)
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# отчет о качестве классификации
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print(classification_report(true_labels, predicted_labels, target_names=class_names))
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# вычисление матрицы ошибок
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conf_matrix = confusion_matrix(true_labels, predicted_labels)
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# отрисовка матрицы ошибок в виде "тепловой карты"
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fig, ax = plt.subplots(figsize=(6, 6))
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disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix,display_labels=class_names)
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disp.plot(ax=ax, xticks_rotation=45) # поворот подписей по X и приятная палитра
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plt.tight_layout() # чтобы всё влезло
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plt.show() |