Nathan님 자료

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data/")
images = mnist.train.images

def xavier_initializer(shape):
    return tf.random_normal(shape=shape, stddev=1/shape[0])

# Generator
z_size = 100
g_w1_size = 400 
g_out_size = 28 * 28

# Discriminator
x_size = 28 * 28
d_w1_size = 400
d_out_size = 1

z = tf.placeholder('float', shape=(None, z_size))
X = tf.placeholder('float', shape=(None, x_size))

g_weights = {
    'w1': tf.Variable(xavier_initializer(shape=(z_size, g_w1_size))),
    'b1': tf.Variable(tf.zeros(shape=[g_w1_size])),
    'out': tf.Variable(xavier_initializer(shape=(g_w1_size, g_out_size))),
    'b2': tf.Variable(tf.zeros(shape=[g_out_size])),
}

d_weights ={
    'w1': tf.Variable(xavier_initializer(shape=(x_size, d_w1_size))),
    'b1': tf.Variable(tf.zeros(shape=[d_w1_size])),
    'out': tf.Variable(xavier_initializer(shape=(d_w1_size, d_out_size))),
    'b2': tf.Variable(tf.zeros(shape=[d_out_size])),
}

def G(z, w=g_weights):
    h1 = tf.nn.relu(tf.matmul(z, w['w1']) + w['b1'])
    return tf.sigmoid(tf.matmul(h1, w['out']) + w['b2'])

def D(x, w=d_weights):
    h1 = tf.nn.relu(tf.matmul(x, w['w1']) + w['b1'])
    return tf.sigmoid(tf.matmul(h1, w['out']) + w['b2'])

def generate_z(n=1):
    return np.random.normal(size=(n, z_size))

sample = G(z)

# Objective functions
G_objective = -tf.reduce_mean(tf.log(D(G(z))))
D_objective = -tf.reduce_mean(tf.log(D(X)) + tf.log(1 - D(G(z))))

G_opt = tf.train.AdamOptimizer().minimize(G_objective, var_list=g_weights.values())
D_opt = tf.train.AdamOptimizer().minimize(D_objective, var_list=d_weights.values())

## Training
batch_size = 128

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    for _ in range(100):
        sess.run(G_opt, feed_dict={
            z: generate_z(batch_size)            
        })
        sess.run(D_opt, feed_dict={
            X: images[np.random.choice(range(len(images)), batch_size)].reshape(batch_size, x_size),
            z: generate_z(batch_size),
        })

    # You may wish to save or plot the image generated
    # to see how it looks like
    image = sess.run(sample, feed_dict={z:generate_z()})