写一个tensorflow版GAN的代码

Sure! 这是一个简单的基于Tensorflow的GAN代码，涉及两个神经网络模型——生成器和判别器：

import tensorflow as tf
import numpy as np

# 定义GAN的判别器和生成器
def generator(x, reuse=False):
    with tf.variable_scope('Generator', reuse=reuse):
        x = tf.layers.dense(x, units=256, activation=tf.nn.relu)
        x = tf.layers.dense(x, units=784, activation=tf.nn.sigmoid)
        return x

def discriminator(x, reuse=False):
    with tf.variable_scope('Discriminator', reuse=reuse):
        x = tf.layers.dense(x, units=256, activation=tf.nn.relu)
        x = tf.layers.dense(x, units=1, activation=None)
        return x

# 定义损失函数，包括交叉熵和梯度下降优化器
x = tf.placeholder(tf.float32, shape=[None, 784], name='x')
z = tf.placeholder(tf.float32, shape=[None, 100], name='z')

g = generator(z)
d_real = discriminator(x)
d_fake = discriminator(g, reuse=True)

d_loss_real = tf.reduce_mean(
    tf.nn.sigmoid_cross_entropy_with_logits(logits=d_real, labels=tf.ones_like(d_real)))
d_loss_fake = tf.reduce_mean(
    tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake, labels=tf.zeros_like(d_fake)))
d_loss = d_loss_real + d_loss_fake

g_loss = tf.reduce_mean(
    tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake, labels=tf.ones_like(d_fake)))

d_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Discriminator')
g_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Generator')

d_train = tf.train.AdamOptimizer().minimize(d_loss, var_list=d_vars)
g_train = tf.train.AdamOptimizer().minimize(g_loss, var_list=g_vars)

# 开始训练模型
batch_size = 32
z_dim = 100
epochs = 100
samples = []

sess = tf.Session()
sess.run(tf.global_variables_initializer())

for epoch in range(epochs):
    for step in range(mnist.train.num_examples // batch_size):
        batch_x, _ = mnist.train.next_batch(batch_size)
        batch_x = batch_x*2 - 1
        batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

        sess.run(d_train, feed_dict={x: batch_x, z: batch_z})
        sess.run(g_train, feed_dict={z: batch_z})

    if epoch%10 == 0:
        print("Epoch {}/{}".format(epoch+1, epochs))
        batch_z = np.random.uniform(-1, 1, size=(16, z_dim))
        gen_samples = sess.run(generator(z, reuse=True), feed_dict={z: batch_z})
        samples.append(gen_samples)

以上是一个简单的GAN代码，你可以根据需要进行更改和调整。