WGAN-GP 代码（tensorflow）

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import os
import numpy as np
from scipy import misc,ndimage

# 读入本地的MNIST数据集，该函数为mnist专用
mnist = input_data.read_data_sets('MNIST_data')

batch_size = 100
# 每张图片包含28*28个像素点
width,height = 28,28
# 用一个数字数组表示一张图，那么这个数组展开成向量的长度就是28*28=784
mnist_dim = width*height
# 每张图表示一个数字，从0到9
random_dim = 10
epochs = 1000000

def my_init(size):
    # 从[-0.05,0.05]的均匀分布中采样得到维度是size的输出
    return tf.random_uniform(size, -0.05, 0.05)

# 判别器相关参数设定
D_W1 = tf.Variable(my_init([mnist_dim, 128]))
D_b1 = tf.Variable(tf.zeros([128]))
D_W2 = tf.Variable(my_init([128, 32]))
D_b2 = tf.Variable(tf.zeros([32]))
D_W3 = tf.Variable(my_init([32, 1]))
D_b3 = tf.Variable(tf.zeros([1]))
D_variables = [D_W1, D_b1, D_W2, D_b2, D_W3, D_b3]

# 生成器相关参数设定
G_W1 = tf.Variable(my_init([random_dim, 32]))
G_b1 = tf.Variable(tf.zeros([32]))
G_W2 = tf.Variable(my_init([32, 128]))
G_b2 = tf.Variable(tf.zeros([128]))
G_W3 = tf.Variable(my_init([128, mnist_dim]))
G_b3 = tf.Variable(tf.zeros([mnist_dim]))
G_variables = [G_W1, G_b1, G_W2, G_b2, G_W3, G_b3]

# 判别器网络结构
def D(X):
    X = tf.nn.relu(tf.matmul(X, D_W1) + D_b1)
    X = tf.nn.relu(tf.matmul(X, D_W2) + D_b2)
    X = tf.matmul(X, D_W3) + D_b3
    return X

# 生成器网络结构
def G(X):
    X = tf.nn.relu(tf.matmul(X, G_W1) + G_b1)
    X = tf.nn.relu(tf.matmul(X, G_W2) + G_b2)
    X = tf.nn.sigmoid(tf.matmul(X, G_W3) + G_b3)
    return X

# real_X是真实样本，random_X是噪音数据，random_Y是生成器生成的伪样本
real_X = tf.placeholder(tf.float32, shape=[batch_size, mnist_dim])
random_X = tf.placeholder(tf.float32, shape=[batch_size, random_dim])
random_Y = G(random_X)

# 求惩罚项，这个这个惩罚是“软约束”，最终的结果不一定满足这个约束，但是会在约束上下波动。这里Lipschitz约束的C=1
eps = tf.random_uniform([batch_size, 1], minval=0., maxval=1.) # eps是U[0,1]的随机数
# 在真实样本和生成样本之间随机插值，希望这个约束可以“布满”真实样本和生成样本之间的空间
X_inter = eps*real_X + (1. - eps)*random_Y
# 求梯度
grad = tf.gradients(D(X_inter), [X_inter])[0]
# 求梯度的二范数
grad_norm = tf.sqrt(tf.reduce_sum((grad)**2, axis=1))
# Lipschitz限制是要求判别器的梯度不超过K，这个loss项是希望判别器的梯度离K（此处K设为1）越近越好
grad_pen = 10 * tf.reduce_mean(tf.nn.relu(grad_norm - 1.))

# 判别器和生成器的损失函数
D_loss = - tf.reduce_mean(D(real_X)) + tf.reduce_mean(D(random_Y)) + grad_pen
G_loss = - tf.reduce_mean(D(random_Y))

# 判别器和生成器的优化函数
D_solver = tf.train.AdamOptimizer(1e-4, 0.5).minimize(D_loss, var_list=D_variables)
G_solver = tf.train.AdamOptimizer(1e-4, 0.5).minimize(G_loss, var_list=G_variables)

# 创建对话，初始化所有变量
sess = tf.Session()
sess.run(tf.global_variables_initializer())

# 是否存在“out”文件夹，不存在的话新建一个，存放实验结果
if not os.path.exists('out/'):
    os.makedirs('out/')


for e in range(epochs):
    for i in range(5):
        real_batch_X,_ = mnist.train.next_batch(batch_size)
        random_batch_X = np.random.uniform(-1, 1, (batch_size, random_dim))
        _,D_loss_ = sess.run([D_solver,D_loss], feed_dict={
     real_X:real_batch_X, random_X:random_batch_X})
    random_batch_X = np.random.uniform(-1, 1, (batch_size, random_dim))
    _,G_loss_ = sess.run([G_solver,G_loss], feed_dict={
     random_X:random_batch_X})
    if e % 1000 == 0:
        print('epoch %s, D_loss: %s, G_loss: %s'%(e, D_loss_, G_loss_))
        n_rows = 6
        check_imgs = sess.run(random_Y, feed_dict={
     random_X:random_batch_X}).reshape((batch_size, width, height))[:n_rows*n_rows]
        imgs = np.ones((width*n_rows+5*n_rows+5, height*n_rows+5*n_rows+5))
        for i in range(n_rows*n_rows):
            imgs[5+5*(i%n_rows)+width*(i%n_rows):5+5*(i%n_rows)+width+width*(i%n_rows), 5+5*(i//n_rows)+height*(i//n_rows):5+5*(i//n_rows)+height+height*(i//n_rows)] = check_imgs[i]
        misc.imsave('out/%s.png'%(e/1000), imgs)