форкнуто от main/is_dnn
Родитель
44b4ca77b8
Сommit
bfb899d78e
@ -0,0 +1,846 @@
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# Отчёт по лабораторной работе №3
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## по теме: "Распознавание изображений"
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---
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Выполнили: Бригада 2, Мачулина Д.В., Бирюкова А.С., А-02-22
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---
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## Задание 1
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### 1. Создание блокнота и настройка среды
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```python
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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 import layers
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from tensorflow.keras.models import Sequential
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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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```
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---
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### 2. Загрузка набора данных MNIST
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```python
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from keras.datasets import mnist
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(X_train, y_train), (X_test, y_test) = mnist.load_data()
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```
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---
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### 3. Разбиение набора данных на общучающие и тестовые (номер бригады - 2)
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```python
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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 = 60000,
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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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```
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Shape of X train: (60000, 28, 28)
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Shape of y train: (60000,)
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Shape of X test: (10000, 28, 28)
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Shape of y test: (10000,)
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---
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### 4. Предобработка данных
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```python
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# Зададим параметры данных и модели
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num_classes = 10
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input_shape = (28, 28, 1)
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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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# Расширяем размерность входных данных, чтобы каждое изображение имело
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# размерность (высота, ширина, количество каналов)
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X_train = np.expand_dims(X_train, -1)
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X_test = np.expand_dims(X_test, -1)
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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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```
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Shape of transformed X train: (60000, 28, 28, 1)
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Shape of transformed X test: (10000, 28, 28, 1)
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Shape of transformed y train: (60000, 10)
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Shape of transformed y test: (10000, 10)
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---
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### 5. Реализация и обучение модели свёрточной нейронной сети
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```python
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# создаем модель
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model = Sequential()
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model.add(layers.Conv2D(32, kernel_size=(3, 3), activation="relu", input_shape=input_shape))
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model.add(layers.MaxPooling2D(pool_size=(2, 2)))
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model.add(layers.Conv2D(64, kernel_size=(3, 3), activation="relu"))
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model.add(layers.MaxPooling2D(pool_size=(2, 2)))
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model.add(layers.Dropout(0.5))
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model.add(layers.Flatten())
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model.add(layers.Dense(num_classes, activation="softmax"))
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model.summary()
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```
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<table>
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<thead>
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<tr>
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<th>Layer (type)</th>
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<th>Output Shape</th>
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<th>Param #</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>conv2d (Conv2D)</td>
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<td>(None, 26, 26, 32)</td>
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<td> 320 </td>
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</tr>
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<tr>
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<td>max_pooling2d (MaxPooling2D)</td>
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<td>(None, 13, 13, 32) </td>
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<td>0</td>
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</tr>
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<tr>
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<td>conv2d_1 (Conv2D) </td>
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<td>(None, 11, 11, 64) </td>
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<td>18,496</td>
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</tr>
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<tr>
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<td>max_pooling2d_1 (MaxPooling2D)</td>
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<td>(None, 5, 5, 64)</td>
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<td>0</td>
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</tr>
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<tr>
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<td>dropout (Dropout)</td>
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<td>(None, 5, 5, 64)</td>
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<td>0</td>
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</tr>
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<tr>
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<td>flatten (Flatten) </td>
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<td>(None, 1600)</td>
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<td>0</td>
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</tr>
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<tr>
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<td>dense (Dense)</td>
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<td>(None, 10)</td>
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<td>16,010</td>
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</tr>
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</tbody>
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</table>
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Total params: 34,826 (136.04 KB)
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Trainable params: 34,826 (136.04 KB)
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Non-trainable params: 0 (0.00 B)
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```python
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# компилируем и обучаем модель
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batch_size = 512
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epochs = 15
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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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```
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---
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### 6. Оценка качества обучения на тестовых данных
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```python
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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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```
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Loss on test data: 0.04353996366262436
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Accuracy on test data: 0.9876000285148621
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---
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### 7. Подача на вход обученной модели тестовых изображений
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```python
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# вывод тестового изображения и результата распознавания
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n = 333
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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(28,28), 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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```
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Real mark: 3
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NN answer: 3
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```python
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# вывод тестового изображения и результата распознавания
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n = 222
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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(28,28), 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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```
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Real mark: 2
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NN answer: 2
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---
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### 8. Вывод отчёта о качестве классификации тестовой выборки и матрицы ошибок для тестовой выборки
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```python
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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))
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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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display = ConfusionMatrixDisplay(confusion_matrix=conf_matrix)
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display.plot()
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plt.show()
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```
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<table>
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<thead>
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<tr>
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<th>№(type)</th>
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<th>precision</th>
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<th>recall</th>
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<th>f1-score</th>
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<th>support</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>0</td>
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<td>0.99</td>
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<td>0.99</td>
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<td>0.99</td>
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<td>968</td>
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</tr>
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<tr>
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<td>1</td>
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<td>1.00</td>
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<td>0.99</td>
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<td>0.99</td>
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<td>1087</td>
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</tr>
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<tr>
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<td>2</td>
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<td>0.99</td>
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<td>0.99</td>
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<td>0.99</td>
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<td>1000</td>
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</tr>
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<tr>
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<td>3</td>
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<td>0.99</td>
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<td>0.98</td>
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<td>0.99</td>
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<td>1039</td>
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</tr>
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<tr>
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<td>4</td>
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<td>0.99</td>
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<td>0.99</td>
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<td>0.99</td>
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<td>966</td>
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</tr>
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<tr>
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<td>5</td>
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<td>0.98</td>
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<td>0.99</td>
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<td>0.99</td>
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<td>908</td>
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</tr>
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<tr>
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<td>6</td>
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<td>0.99</td>
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<td>0.99</td>
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<td>0.99</td>
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<td>972</td>
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</tr>
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<tr>
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<td>7</td>
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<td>0.98</td>
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<td>0.99</td>
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<td>0.98</td>
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<td>1060</td>
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</tr>
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<tr>
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<td>8</td>
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<td>0.98</td>
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<td>0.98</td>
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<td>0.98</td>
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<td>1015</td>
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</tr>
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<tr>
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<td>9</td>
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<td>0.98</td>
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<td>0.98</td>
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<td>0.98</td>
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<td>985</td>
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</tr>
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<tr>
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<td colspan = 5></td>
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</tr>
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<tr>
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<td>accuracy</td>
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<td></td>
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<td></td>
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<td>0.99</td>
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<td>10000</td>
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</tr>
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<tr>
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<td>macro avg</td>
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<td>0.99</td>
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<td>0.99</td>
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<td>0.99</td>
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<td>10000</td>
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</tr>
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<tr>
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<td>weighted avg</td>
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<td>0.99</td>
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<td>0.99</td>
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<td>0.99</td>
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<td>10000</td>
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</tr>
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</tbody>
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</table>
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![]()
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---
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### 9. Загрузка, предобработка и подача собственных изображения
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```python
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# загрузка собственного изображения
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from PIL import Image
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file_data = Image.open('7.png')
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file_data = file_data.convert('L') # перевод в градации серого
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test_img = np.array(file_data)
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# вывод собственного изображения
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plt.imshow(test_img, cmap=plt.get_cmap('gray'))
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plt.show()
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# предобработка
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test_img = test_img / 255
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test_img = np.reshape(test_img, (1,28,28,1))
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# распознавание
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result = model.predict(test_img)
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print('I think it\'s ', np.argmax(result))
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```
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![]()
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```python
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# загрузка собственного изображения
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from PIL import Image
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file_data = Image.open('5.png')
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file_data = file_data.convert('L') # перевод в градации серого
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test_img = np.array(file_data)
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# вывод собственного изображения
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plt.imshow(test_img, cmap=plt.get_cmap('gray'))
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plt.show()
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# предобработка
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test_img = test_img / 255
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test_img = np.reshape(test_img, (1,28,28,1))
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# распознавание
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result = model.predict(test_img)
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print('I think it\'s ', np.argmax(result))
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```
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![]()
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### 10. Загрузка модели из ЛР1. Оценка качества
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```python
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model = keras.models.load_model("best_model.keras")
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model.summary()
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```
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<table>
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<thead>
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<tr>
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<th>Layer (type)</th>
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<th>Output Shape</th>
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<th>Param #</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>dense_4 (Dense)</td>
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<td>(None, 300)</td>
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<td>235,500</td>
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</tr>
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<tr>
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<td>dense_5 (Dense)</td>
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<td>(None, 10)</td>
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<td>3,010</td>
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</tr>
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</tbody>
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</table>
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Total params: 238,512 (931.69 KB)
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Trainable params: 238,510 (931.68 KB)
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Non-trainable params: 0 (0.00 B)
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Optimizer params: 2 (12.00 B)
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```python
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# развернем каждое изображение 28*28 в вектор 784
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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 = 60000,
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random_state = 7)
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num_pixels = X_train.shape[1] * X_train.shape[2]
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X_train = X_train.reshape(X_train.shape[0], num_pixels) / 255
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X_test = X_test.reshape(X_test.shape[0], num_pixels) / 255
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print('Shape of transformed X train:', X_train.shape)
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print('Shape of transformed X train:', 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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```
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Shape of transformed X train: (60000, 784)
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Shape of transformed X train: (10000, 784)
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Shape of transformed y train: (60000, 10)
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Shape of transformed y test: (10000, 10)
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```python
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# Оценка качества работы модели на тестовых данных
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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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```
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Loss on test data: 0.37091827392578125
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Accuracy on test data: 0.9013000130653381
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---
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### 11. Сравнение обученной модели сверточной сети и наилучшей модели полносвязной сети
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<table>
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<thead>
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<tr>
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<th>Модель</th>
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<th>Количество настраиваемых параметров сети</th>
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<th>Количество эпох обучения</th>
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<th>Качество классификации тестовой выборки</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<th>Сверточная</th>
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<td align="center">34,826</td>
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<td align="center">15</td>
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<td align="center">0.9876</td>
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</tr>
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<tr>
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<th>Полносвязная</th>
|
||||
<td align="center">238,512</td>
|
||||
<td align="center">100</td>
|
||||
<td align="center">0.9013</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
Вывод:
|
||||
|
||||
|
||||
## Задание 2
|
||||
### В новом блокноте выполнили п.1-8 задания 1, изменив набор данных MNIST на CIFAR-10
|
||||
### 1. Создание блокнота и настройка среды
|
||||
```python
|
||||
from google.colab import drive
|
||||
drive.mount('/content/drive')
|
||||
import os
|
||||
os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab3')
|
||||
|
||||
from tensorflow import keras
|
||||
from tensorflow.keras.models import Sequential
|
||||
from tensorflow.keras import layers
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from sklearn.metrics import classification_report, confusion_matrix
|
||||
from sklearn.metrics import ConfusionMatrixDisplay
|
||||
```
|
||||
|
||||
### 2.Загрузка набора данных и его разбиение на ообучащие и тестовые
|
||||
```python
|
||||
# загрузка датасета
|
||||
from keras.datasets import cifar10
|
||||
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
|
||||
```
|
||||
|
||||
```python
|
||||
# создание своего разбиения датасета
|
||||
from sklearn.model_selection import train_test_split
|
||||
|
||||
|
||||
# объединяем в один набор
|
||||
X = np.concatenate((X_train, X_test))
|
||||
y = np.concatenate((y_train, y_test))
|
||||
|
||||
# разбиваем по вариантам
|
||||
X_train, X_test, y_train, y_test = train_test_split(X, y,
|
||||
test_size = 10000,
|
||||
train_size = 50000,
|
||||
random_state = 7)
|
||||
# вывод размерностей
|
||||
print('Shape of X train:', X_train.shape)
|
||||
print('Shape of y train:', y_train.shape)
|
||||
print('Shape of X test:', X_test.shape)
|
||||
print('Shape of y test:', y_test.shape)
|
||||
```
|
||||
Shape of X train: (50000, 32, 32, 3)
|
||||
Shape of y train: (50000, 1)
|
||||
Shape of X test: (10000, 32, 32, 3)
|
||||
Shape of y test: (10000, 1)
|
||||
|
||||
### 3. Вывод изображений с подписями классов
|
||||
```python
|
||||
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
|
||||
'dog', 'frog', 'horse', 'ship', 'truck']
|
||||
plt.figure(figsize=(10,10))
|
||||
for i in range(25):
|
||||
plt.subplot(5,5,i+1)
|
||||
plt.xticks([])
|
||||
plt.yticks([])
|
||||
plt.grid(False)
|
||||
plt.imshow(X_train[i])
|
||||
plt.xlabel(class_names[y_train[i][0]])
|
||||
plt.show()
|
||||
```
|
||||
![]()
|
||||
|
||||
### 4. Предобработка данных
|
||||
```python
|
||||
# Зададим параметры данных и модели
|
||||
num_classes = 10
|
||||
input_shape = (32, 32, 3)
|
||||
|
||||
# Приведение входных данных к диапазону [0, 1]
|
||||
X_train = X_train / 255
|
||||
X_test = X_test / 255
|
||||
|
||||
print('Shape of transformed X train:', X_train.shape)
|
||||
print('Shape of transformed X test:', X_test.shape)
|
||||
|
||||
# переведем метки в one-hot
|
||||
y_train = keras.utils.to_categorical(y_train, num_classes)
|
||||
y_test = keras.utils.to_categorical(y_test, num_classes)
|
||||
print('Shape of transformed y train:', y_train.shape)
|
||||
print('Shape of transformed y test:', y_test.shape)
|
||||
```
|
||||
Shape of transformed X train: (50000, 32, 32, 3)
|
||||
Shape of transformed X test: (10000, 32, 32, 3)
|
||||
Shape of transformed y train: (50000, 10)
|
||||
Shape of transformed y test: (10000, 10)
|
||||
|
||||
---
|
||||
### 5. Реализация и обучение модели свёрточной нейронной сети
|
||||
```python
|
||||
# создаем модель
|
||||
model = Sequential()
|
||||
|
||||
# Блок 1
|
||||
model.add(layers.Conv2D(32, (3, 3), padding="same",
|
||||
activation="relu", input_shape=input_shape))
|
||||
model.add(layers.BatchNormalization())
|
||||
model.add(layers.Conv2D(32, (3, 3), padding="same", activation="relu"))
|
||||
model.add(layers.BatchNormalization())
|
||||
model.add(layers.MaxPooling2D((2, 2)))
|
||||
model.add(layers.Dropout(0.25))
|
||||
|
||||
# Блок 2
|
||||
model.add(layers.Conv2D(64, (3, 3), padding="same", activation="relu"))
|
||||
model.add(layers.BatchNormalization())
|
||||
model.add(layers.Conv2D(64, (3, 3), padding="same", activation="relu"))
|
||||
model.add(layers.BatchNormalization())
|
||||
model.add(layers.MaxPooling2D((2, 2)))
|
||||
model.add(layers.Dropout(0.25))
|
||||
|
||||
model.add(layers.Flatten())
|
||||
model.add(layers.Dense(128, activation='relu'))
|
||||
model.add(layers.Dropout(0.5))
|
||||
model.add(layers.Dense(num_classes, activation="softmax"))
|
||||
|
||||
|
||||
model.summary()
|
||||
```
|
||||
<table>
|
||||
<thead>
|
||||
<tr>
|
||||
<th>Layer (type)</th>
|
||||
<th>Output Shape</th>
|
||||
<th>Param #</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr>
|
||||
<td>conv2d (Conv2D)</td>
|
||||
<td>(None, 32, 32, 32)</td>
|
||||
<td>896</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>batch_normalization_6 (BatchNormalization)</td>
|
||||
<td>(None, 32, 32, 32) </td>
|
||||
<td>128</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>conv2d_13 (Conv2D)</td>
|
||||
<td>(None, 32, 32, 32)</td>
|
||||
<td>9,248</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>batch_normalization_7 (BatchNormalization)</td>
|
||||
<td>(None, 32, 32, 32)</td>
|
||||
<td>128</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>max_pooling2d_9 (MaxPooling2D)</td>
|
||||
<td>(None, 16, 16, 32)</td>
|
||||
<td>0</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>dropout_6 (Dropout)</td>
|
||||
<td>(None, 16, 16, 32)</td>
|
||||
<td>0</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>conv2d_14 (Conv2D)</td>
|
||||
<td>(None, 16, 16, 64)</td>
|
||||
<td>18,496</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>batch_normalization_8 (BatchNormalization)</td>
|
||||
<td>(None, 16, 16, 64)</td>
|
||||
<td>256</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>conv2d_15 (Conv2D)</td>
|
||||
<td>(None, 16, 16, 64)</td>
|
||||
<td>32,928</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>batch_normalization_9 (BatchNormalization)</td>
|
||||
<td>(None, 16, 16, 64)</td>
|
||||
<td>256</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>max_pooling2d_10 (MaxPooling2D)</td>
|
||||
<td>(None, 8, 8, 64)</td>
|
||||
<td>0</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>dropout_7 (Dropout)</td>
|
||||
<td>(None, 8, 8, 64)</td>
|
||||
<td>0</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>flatten_3 (Flatten) </td>
|
||||
<td>(None, 4096)</td>
|
||||
<td>0</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>dense_6 (Dense)</td>
|
||||
<td>(None, 128)</td>
|
||||
<td>524,416</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>dropout_8 (Dropout)</td>
|
||||
<td>(None, 128)</td>
|
||||
<td>0</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>dense_7 (Dense)</td>
|
||||
<td>(None, 10)</td>
|
||||
<td>1,290</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
Total params: 592,042 (2.26 MB)
|
||||
Trainable params: 591,658 (2.26 MB)
|
||||
Non-trainable params: 384 (1.50 KB)
|
||||
|
||||
```python
|
||||
batch_size = 64
|
||||
epochs = 50
|
||||
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
|
||||
model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)
|
||||
```
|
||||
|
||||
### 6. Оценка качества обучения на тестовых данных
|
||||
```python
|
||||
scores = model.evaluate(X_test, y_test)
|
||||
print('Loss on test data:', scores[0])
|
||||
print('Accuracy on test data:', scores[1])
|
||||
```
|
||||
|
||||
### 7. Подача на вход обученной модели тестовых изображений
|
||||
```python
|
||||
for n in [5,17]:
|
||||
result = model.predict(X_test[n:n+1])
|
||||
print('NN output:', result)
|
||||
|
||||
plt.imshow(X_test[n].reshape(32,32,3), cmap=plt.get_cmap('gray'))
|
||||
plt.show()
|
||||
print('Real mark: ', np.argmax(y_test[n]))
|
||||
print('NN answer: ', np.argmax(result))
|
||||
```
|
||||
![]()
|
||||
Real mark: 0
|
||||
NN answer: 2
|
||||
|
||||
![]()
|
||||
Real mark: 5
|
||||
NN answer: 5
|
||||
|
||||
### 8. Вывод отчёта о качестве классификации тестовой выборки и матрицы ошибок для тестовой выборки
|
||||
```python
|
||||
# истинные метки классов
|
||||
true_labels = np.argmax(y_test, axis=1)
|
||||
# предсказанные метки классов
|
||||
predicted_labels = np.argmax(model.predict(X_test), axis=1)
|
||||
|
||||
# отчет о качестве классификации
|
||||
print(classification_report(true_labels, predicted_labels, target_names=class_names))
|
||||
# вычисление матрицы ошибок
|
||||
conf_matrix = confusion_matrix(true_labels, predicted_labels)
|
||||
# отрисовка матрицы ошибок в виде "тепловой карты"
|
||||
fig, ax = plt.subplots(figsize=(6, 6))
|
||||
disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix,display_labels=class_names)
|
||||
disp.plot(ax=ax, xticks_rotation=45) # поворот подписей по X и приятная палитра
|
||||
plt.tight_layout() # чтобы всё влезло
|
||||
plt.show()
|
||||
```
|
||||
|
||||
<table>
|
||||
<thead>
|
||||
<tr>
|
||||
<th>class</th>
|
||||
<th>precision</th>
|
||||
<th>recall</th>
|
||||
<th>f1-score</th>
|
||||
<th>support</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr>
|
||||
<td>airplane</td>
|
||||
<td>0.85</td>
|
||||
<td>0.86</td>
|
||||
<td>0.86</td>
|
||||
<td>1013</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>automobile</td>
|
||||
<td>0.93</td>
|
||||
<td>0.91</td>
|
||||
<td>0.92</td>
|
||||
<td>989</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>bird</td>
|
||||
<td>0.75</td>
|
||||
<td>0.75</td>
|
||||
<td>0.75</td>
|
||||
<td>1018</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>cat</td>
|
||||
<td>0.69</td>
|
||||
<td>0.66</td>
|
||||
<td>0.67</td>
|
||||
<td>1049</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>deer</td>
|
||||
<td>0.79</td>
|
||||
<td>0.78</td>
|
||||
<td>0.78</td>
|
||||
<td>1009</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>dog</td>
|
||||
<td>0.73</td>
|
||||
<td>0.68</td>
|
||||
<td>0.71</td>
|
||||
<td>978</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>frog</td>
|
||||
<td>0.79</td>
|
||||
<td>0.90</td>
|
||||
<td>0.84</td>
|
||||
<td>981</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>horse</td>
|
||||
<td>0.88</td>
|
||||
<td>0.84</td>
|
||||
<td>0.86</td>
|
||||
<td>986</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>ship</td>
|
||||
<td>0.89</td>
|
||||
<td>0.92</td>
|
||||
<td>0.91</td>
|
||||
<td>1029</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>truck</td>
|
||||
<td>0.88</td>
|
||||
<td>0.91</td>
|
||||
<td>0.89</td>
|
||||
<td>948</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td colspan = 5></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>accuracy</td>
|
||||
<td></td>
|
||||
<td></td>
|
||||
<td>0.82</td>
|
||||
<td>10000</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>macro avg</td>
|
||||
<td>0.82</td>
|
||||
<td>0.82</td>
|
||||
<td>0.82</td>
|
||||
<td>10000</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>weighted avg</td>
|
||||
<td>0.82</td>
|
||||
<td>0.82</td>
|
||||
<td>0.82</td>
|
||||
<td>10000</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
![]()
|
||||
|
||||
Вывод:
|
||||
Загрузка…
Ссылка в новой задаче