我正在尝试应用数据论证来增加训练数据量。
代码如下所示，扩展层由RandomFlip和RandomRotation组成。

def data_augmenter():
    '''
    Create a Sequential model composed of 2 layers
    Returns:
        tf.keras.Sequential
    '''
    ### START CODE HERE
    data_augmentation = tf.keras.Sequential()
    data_augmentation.add((RandomFlip('horizontal')))
    data_augmentation.add(RandomRotation(0.2))
    ### END CODE HERE
    
    return data_augmentation

data_augmentation = data_augmenter()

for image, _ in train_dataset.take(1):
    plt.figure(figsize=(10, 10))
    first_image = image[0]
    plt.imshow(first_image / 255)
    plt.figure(figsize=(10, 10))
    for i in range(9):
        ax = plt.subplot(3, 3, i + 1)
        augmented_image = data_augmentation(tf.cast(tf.expand_dims(first_image, 0), tf.float32))
        plt.imshow(augmented_image[0] / 255)
        plt.axis('off')

Output Images