ex4_1_cnn_mnist_cl-cpu.py

# Set to use CPU
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'

# 1. 분류 CNN 모델링
import keras
from keras import models, layers
from keras import backend

class CNN(models.Sequential):
    def __init__(self, input_shape, num_classes):
        super().__init__()

        self.add(layers.Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
        self.add(layers.Conv2D(64, (3, 3), activation='relu'))
        self.add(layers.MaxPooling2D(pool_size=(2, 2)))
        self.add(layers.Dropout(0.25))
        self.add(layers.Flatten())
        self.add(layers.Dense(128, activation='relu'))
        self.add(layers.Dropout(0.5))
        self.add(layers.Dense(num_classes, activation='softmax'))

        self.compile(loss=keras.losses.categorical_crossentropy,
                      optimizer='rmsprop',
                      metrics=['accuracy'])

# 2. 분류 CNN을 위한 데이터 준비  
from keras import datasets 

class DATA():
    def __init__(self):
        num_classes = 10

        (x_train, y_train), (x_test, y_test) = datasets.mnist.load_data()
        img_rows, img_cols = x_train.shape[1:]

        if backend.image_data_format() == 'channels_first':
            x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
            x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
            input_shape = (1, img_rows, img_cols)
        else:
            x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
            x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
            input_shape = (img_rows, img_cols, 1)

        x_train = x_train.astype('float32')
        x_test = x_test.astype('float32')
        x_train /= 255
        x_test /= 255

        y_train = keras.utils.to_categorical(y_train, num_classes)
        y_test = keras.utils.to_categorical(y_test, num_classes)
        
        self.input_shape = input_shape
        self.num_classes = num_classes
        self.x_train, self.y_train = x_train, y_train
        self.x_test, self.y_test = x_test, y_test        
        
# 3. 학습 효과 분석
from keraspp.skeras import plot_loss, plot_acc
import matplotlib.pyplot as plt

# 4. 분류 CNN 학습 및 테스트
def main():
    batch_size = 128
    epochs = 10

    data = DATA()
    model = CNN(data.input_shape, data.num_classes)

    history = model.fit(data.x_train, data.y_train,
              batch_size=batch_size,
              epochs=epochs,
              validation_split=0.2)

    score = model.evaluate(data.x_test, data.y_test)
    print()
    print('Test loss:', score[0])
    print('Test accuracy:', score[1])

    plot_loss(history)
    plt.show()
    plot_acc(history)
    plt.show()

if __name__ == '__main__':
    main()