train.py

import os
import tensorflow as tf
from tensorflow.keras import datasets, layers, models


class CNN(object):
    def __init__(self):
        model = models.Sequential()
        # 32 kernel, kernel_size: 3 * 3, input_size: 28*28*1
        model.add(layers.Conv2D(
            32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
        model.add(layers.MaxPooling2D(2, 2))
        # input_shape: 13 * 13 * 32
        model.add(layers.Conv2D(64, (3, 3), activation='relu'))
        model.add(layers.MaxPooling2D(2, 2))
        # input_shape: 5 * 5 * 64
        model.add(layers.Conv2D(64, (3, 3), activation='relu'))
        # input_shape: 3 * 3 * 64
        model.add(layers.Flatten())
        model.add(layers.Dense(64, activation='relu'))
        model.add(layers.Dense(10, activation='softmax'))
        model.summary()
        self.model = model


class DataSource(object):
    def __init__(self):
        # mnist datasets storage path
        data_path = os.path.abspath(
            os.path.dirname(__file__)) + '/data/mnist.npz'
        (train_images, train_labels), (test_images,
                                       test_labels) = datasets.mnist.load_data(path=data_path)
        train_images = train_images.reshape((60000, 28, 28, 1))
        test_images = test_images.reshape((10000, 28, 28, 1))

        train_images, test_images = train_images / 255.0, test_images / 255.0

        self.train_images, self.test_images = train_images, test_images
        self.train_labels, self.test_labels = train_labels, test_labels


class Train:
    def __init__(self):
        self.cnn = CNN()
        self.data = DataSource()

    def train(self):
        check_path = 'model/cp-{epoch: 04d}.ckpt'
        save_model_cb = tf.keras.callbacks.ModelCheckpoint(
            check_path, save_weights_only=True, verbose=1, period=1)
        self.cnn.model.compile(
            optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
        self.cnn.model.fit(self.data.train_images, self.data.train_labels,
                           epochs=5, callbacks=[save_model_cb])
        test_loss, test_acc = self.cnn.model.evaluate(
            self.data.test_images, self.data.test_labels)
        print('accuracy: %.4f, the size of tested images is %d' %
              (test_acc, len(self.data.test_images)))


if __name__ == '__main__':
    app = Train()
    app.train()