帮我用python写一个生成对抗网络

好的，以下是一个简单的生成对抗网络的Python代码：

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

# 定义生成器模型
def make_generator_model():
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Dense(256, input_shape=(100,), use_bias=False))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.LeakyReLU())

    model.add(tf.keras.layers.Dense(512, use_bias=False))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.LeakyReLU())

    model.add(tf.keras.layers.Dense(28 * 28 * 1, activation='tanh', use_bias=False))
    model.add(tf.keras.layers.Reshape((28, 28, 1)))

    return model

# 定义判别器模型
def make_discriminator_model():
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Flatten(input_shape=(28, 28, 1)))
    model.add(tf.keras.layers.Dense(512, use_bias=False))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.LeakyReLU())
    model.add(tf.keras.layers.Dense(256, use_bias=False))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.LeakyReLU())
    model.add(tf.keras.layers.Dense(1))

    return model

# 定义生成器损失函数
def generator_loss(fake_output):
    return tf.losses.sigmoid_cross_entropy(tf.ones_like(fake_output), fake_output)

# 定义判别器损失函数
def discriminator_loss(real_output, fake_output):
    real_loss = tf.losses.sigmoid_cross_entropy(tf.ones_like(real_output), real_output)
    fake_loss = tf.losses.sigmoid_cross_entropy(tf.zeros_like(fake_output), fake_output)
    return real_loss + fake_loss

# 定义优化器
generator_optimizer = tf.optimizers.Adam(1e-4)
discriminator_optimizer = tf.optimizers.Adam(1e-4)

# 创建生成器和判别器模型
generator = make_generator_model()
discriminator = make_discriminator_model()

# 定义训练函数
@tf.function
def train_step(images):
    noise = tf.random.normal([batch_size, 100])

    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
        generated_images = generator(noise, training=True)

        real_output = discriminator(images, training=True)
        fake_output = discriminator(generated_images, training=True)

        gen_loss = generator_loss(fake_output)
        disc_loss = discriminator_loss(real_output, fake_output)

    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)

    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))

# 加载MNIST数据集
(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()
train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')
train_images = (train_images - 127.5) / 127.5  # 将像素值转化到[-1, 1]之间
batch_size = 256
train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(60000).batch(batch_size)

# 训练模型
epochs = 100
for epoch