定义生成对抗网络训练部分的代码：判别器输入为真实图像数据集和淹没图像数据集，

生成器输入为随机噪声。
生成器通过生成伪造的图像来欺骗判别器，判别器则会尽可能地判别真实图像和伪造图像的区别。
训练过程反复进行，直到生成器生成的图像能够以假乱真，并被判别器识别为真实图像为止。以下是定义生成对抗网络训练部分的代码：

# 定义生成器和判别器模型
generator = ...
discriminator = ...

# 定义损失函数
cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)

# 定义生成器损失函数
def generator_loss(fake_output):
    return 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 = cross_entropy(tf.ones_like(real_output), real_output)
    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
    total_loss = real_loss + fake_loss
    return total_loss

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

# 定义训练步骤
@tf.function
def train_step(images):
    noise = tf.random.normal([BATCH_SIZE, noise_dim])

    # 计算生成器的损失和梯度
    with tf.GradientTape() as gen_tape:
        generated_images = generator(noise, training=True)
        fake_output = discriminator(generated_images, training=True)
        gen_loss = generator_loss(fake_output)
    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))

    # 计算判别器的损失和梯度
    with tf.GradientTape() as disc_tape:
        real_output = discriminator(images, training=True)
        fake_output = discriminator(generated_images, training=True)
        disc_loss = discriminator_loss(real_output, fake_output)
    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)
    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))

# 开始训练
for epoch in range(EPOCHS):
    for image_batch in dataset:
        train_step(image_batch)