tensorflow2.0 DenseNet121 训练 cifar100

from tensorflow.keras import layers, regularizers, Sequential, optimizers
import tensorflow as tf
import numpy as np

def regularized_padded_conv2d(*args, **kwargs):
    ''' 带标准化的卷积 '''

    return layers.Conv2D(
        *args, **kwargs,
        padding='same',
        kernel_regularizer=regularizers.l2(5e-5),
        bias_regularizer=regularizers.l2(5e-5),
        kernel_initializer='glorot_normal'
    )

def load_cifar100_with_DataAugmentation():
    (train_x, train_y), (test_x, test_y) = tf.keras.datasets.cifar100.load_data()
    train_x = np.array(tf.reshape(train_x, shape=(50000, 32, 32, 3)), dtype=np.float) / 255.0
    train_y = tf.keras.utils.to_categorical(train_y)

    test_x = np.array(tf.reshape(test_x, shape=(10000, 32, 32, 3)), dtype=np.float) / 255.0
    test_y = tf.keras.utils.to_categorical(test_y)

    train_noise = tf.random.normal(shape=train_x.shape, mean=0.0, stddev=0.05)
    train_x = tf.add(train_x, train_noise)

    flip_1 = tf.image.flip_up_down(train_x)
    flip_2 = tf.image.flip_left_right(train_x)
    flip_3 = tf.image.random_flip_up_down(train_x)
    flip_4 = tf.image.random_flip_left_right