wgan的tensorflow实现

下面是一个简单的 WGAN-TF 实现，仅供参考：

import tensorflow as tf
import numpy as np

# 定义生成器网络
def generator(x, is_training=True, reuse=False):
    with tf.variable_scope('generator', reuse=reuse):
        x = tf.layers.dense(x, 128)
        x = tf.nn.relu(x)
        x = tf.layers.dense(x, 784)
        return tf.nn.tanh(x)

# 定义判别器网络
def discriminator(x, is_training=True, reuse=False):
    with tf.variable_scope('discriminator', reuse=reuse):
        x = tf.layers.dense(x, 128)
        x = tf.nn.relu(x)
        x = tf.layers.dense(x, 1)
        return x

# 定义输入占位符
real_images = tf.placeholder(tf.float32, [None, 784])
z = tf.placeholder(tf.float32, [None, 100])

# 通过生成器网络生成假图像
fake_images = generator(z)

# 计算判别器网络对真图像和假图像的输出
d_real = discriminator(real_images)
d_fake = discriminator(fake_images, reuse=True)

# 定义 WGAN 的损失函数
d_loss = tf.reduce_mean(d_fake - d_real)
g_loss = tf.reduce_mean(-d_fake)

# 获取判别器和生成器的变量
d_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'discriminator')
g_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'generator')

# 定义优化器
d_train_op = tf.train.RMSPropOptimizer(learning_rate=0.00005).minimize(d_loss, var_list=d_vars)
g_train_op = tf.train.RMSPropOptimizer(learning_rate=0.00005).minimize(g_loss, var_list=g_vars)

# 训练 WGAN
sess = tf.Session()
sess.run(tf.global_variables_initializer())

for i in range(10000):
    # 获取一个 batch 的真实图像
    batch_images = mnist.train.next_batch(64)[0]
    batch_images = batch_images.reshape(-1, 784)

    # 生成噪声并通过生成器生成对应的假图像
    batch_z = np.random.uniform(-1, 1, [64, 100])
    sess.run(g_train_op, feed_dict={z: batch_z})

    # 训练判别器
    for j in range(5):
        sess.run(d_train_op, feed_dict={real_images: batch_images, z: batch_z})

    # 每 100 次迭代输出一次损失函数
    if i % 100 == 0:
        d_loss_val, g_loss_val = sess.run([d_loss, g_loss], feed_dict={real_images: batch_images, z: batch_z})
        print('Iteration %d, d_loss = %.4f, g_loss = %.4f' % (i, d_loss_val, g_loss_val))

需要注意的是，WGAN 的训练过程与 GAN 有所不同，具体可以参考原论文和相关的实现代码。此外，上面的代码仅是一个简单的示例，实际使用时可能需要进行更加细致的调参和改进。