WGAN-GP的代码

WGAN-GP (Wasserstein GAN with Gradient Penalty)是一种改进的生成对抗网络，主要通过添加梯度惩罚项来优化原始的Wasserstein GAN算法，以实现更稳定的训练和更高质量的图像生成。

具体来说，WGAN-GP的损失函数包括两部分：判别器的损失和生成器的损失。其中，判别器的损失由真实数据的Wasserstein距离和生成数据的Wasserstein距离之差加上梯度惩罚项构成，而生成器的损失则是生成数据的Wasserstein距离的相反数。

以下是WGAN-GP的代码示例（使用TensorFlow框架）：

# 定义判别器网络
def discriminator(x, reuse=False):
    with tf.variable_scope('discriminator', reuse=reuse):
        # 定义网络结构
        # ...
        return logits

# 定义生成器网络
def generator(z):
    with tf.variable_scope('generator'):
        # 定义网络结构
        # ...
        return generated_data

# 定义梯度惩罚函数
def gradient_penalty(x, x_generated, discriminator):
    epsilon = tf.random_uniform([], 0.0, 1.0)
    x_hat = epsilon * x + (1 - epsilon) * x_generated
    d_hat = discriminator(x_hat, reuse=True)
    gradients = tf.gradients(d_hat, x_hat)
    slopes = tf.sqrt(tf.reduce_sum(tf.square(gradients), axis=))
    gradient_penalty = tf.reduce_mean((slopes - 1.0) ** 2)
    return gradient_penalty

# 定义损失函数和优化器
z = tf.placeholder(tf.float32, [batch_size, z_dim])
x = tf.placeholder(tf.float32, [batch_size, image_size, image_size, 3])
x_generated = generator(z)
d_real = discriminator(x)
d_generated = discriminator(x_generated, reuse=True)
gp_loss = gradient_penalty(x, x_generated, discriminator)
d_loss = tf.reduce_mean(d_generated) - tf.reduce_mean(d_real) + lambda_gp * gp_loss
g_loss = -tf.reduce_mean(d_generated)
d_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='discriminator')
g_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='generator')
d_train_op = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=0.5).minimize(
    d_loss, var_list=d_vars)
g_train_op = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=0.5).minimize(
    g_loss, var_list=g_vars)

# 训练模型
for epoch in range(num_epochs):
    for i in range(num_batches):
        # 获取真实数据
        x_batch = ...
        # 获取随机噪声
        z_batch = ...
        # 训练判别器
        _, d_loss_batch = sess.run([d_train_op, d_loss], feed_dict={x: x_batch, z: z_batch})
        # 训练生成器
        _, g_loss_batch = sess.run([g_train_op, g_loss], feed_dict={z: z_batch})