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GAN.py
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GAN.py
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import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
from skimage.io import imsave
import os
import shutil
img_width = 28
img_height = 28
img_size = img_height * img_width
to_train = True
to_restore = False
output_path = 'output'
#总迭代次数500次
max_epoch = 500
h1_size = 150
h2_size = 300
z_size = 100
batch_size = 256
def build_generator(z_prior):
w1 = tf.Variable(tf.truncated_normal([z_size,h1_size],stddev=0.1),name='g_w1',dtype=tf.float32)
b1 = tf.Variable(tf.zeros([h1_size]),name='g_b1',dtype=tf.float32)
h1 = tf.nn.relu(tf.matmul(z_prior,w1) + b1)
w2 = tf.Variable(tf.truncated_normal([h1_size,h2_size],stddev=0.1),name='g_w2',dtype=tf.float32)
b2 = tf.Variable(tf.zeros([h2_size]),name='g_b2',dtype=tf.float32)
h2 = tf.nn.relu(tf.matmul(h1,w2)+b2)
w3 = tf.Variable(tf.truncated_normal([h2_size,img_size],stddev=0.1),name='g_w3',dtype=tf.float32)
b3 = tf.Variable(tf.zeros([img_size]),name='g_b3',dtype=tf.float32)
h3 = tf.matmul(h2,w3)+b3
x_generate = tf.nn.tanh(h3)
g_params = [w1,b1,w2,b2,w3,b3]
return x_generate,g_params
def build_discriminator(x_data,x_generated,keep_prob):
#将real img 和 generated img拼在一起
x_in = tf.concat([x_data,x_generated],0)
w1 = tf.Variable(tf.truncated_normal([img_size,h2_size],stddev=0.1),name='d_w1',dtype=tf.float32)
b1 = tf.Variable(tf.zeros([h2_size]),name='d_b1',dtype=tf.float32)
h1 = tf.nn.dropout(tf.nn.relu(tf.matmul(x_in,w1)+b1),keep_prob)
w2 = tf.Variable(tf.truncated_normal([h2_size,h1_size],stddev=0.1),name='d_w2',dtype=tf.float32)
b2 = tf.Variable(tf.zeros([h1_size]),name='d_b2',dtype=tf.float32)
h2 = tf.nn.dropout(tf.nn.relu(tf.matmul(h1,w2)+b2),keep_prob)
w3 = tf.Variable(tf.truncated_normal([h1_size,1]),name='d_w3',dtype=tf.float32)
b3 = tf.Variable(tf.zeros([1]),name='d_b3',dtype=tf.float32)
h3 = tf.matmul(h2,w3)+b3
"""
1，函数原型 tf.slice(inputs,begin,size,name='')
2，用途：从inputs中抽取部分内容
inputs：可以是list,array,tensor
begin：n维列表，begin[i] 表示从inputs中第i维抽取数据时，相对0的起始偏移量，也就是从第i维的begin[i]开始抽取数据
size：n维列表，size[i]表示要抽取的第i维元素的数目
有几个关系式如下:
（1） i in [0,n]
（2）tf.shape(inputs)[0]=len(begin)=len(size)
（3）begin[i]>=0 抽取第i维元素的起始位置要大于等于0
（4）begin[i]+size[i]<=tf.shape(inputs)[i]
"""
"""
h3的size:[batch_size + batch_size,1]
所以 y_data 是对 real img的判别结果
y_generated 是对 generated img 的判别结果
"""
y_data = tf.nn.sigmoid(tf.slice(h3,[0,0],[batch_size,-1],name=None))
y_generated = tf.nn.sigmoid(tf.slice(h3,[batch_size,0],[-1,-1],name=None))
d_params = [w1,b1,w2,b2,w3,b3]
return y_data,y_generated,d_params
def show_result(batch_res,fname,grid_size=(0,0),grid_pad=5):
batch_res = 0.5 * batch_res.reshape((batch_res.shape[0], img_height, img_width)) + 0.5
img_h, img_w = batch_res.shape[1], batch_res.shape[2]
grid_h = img_h * grid_size[0] + grid_pad * (grid_size[0] - 1)
grid_w = img_w * grid_size[1] + grid_pad * (grid_size[1] - 1)
img_grid = np.zeros((grid_h, grid_w), dtype=np.uint8)
for i, res in enumerate(batch_res):
if i >= grid_size[0] * grid_size[1]:
break
img = (res) * 255
img = img.astype(np.uint8)
row = (i // grid_size[0]) * (img_h + grid_pad)
col = (i % grid_size[1]) * (img_w + grid_pad)
img_grid[row:row + img_h, col:col + img_w] = img
imsave(fname, img_grid)
def train():
# load data
mnist = input_data.read_data_sets('MNIST_data',one_hot=True)
x_data = tf.placeholder(tf.float32,[None,img_size],name='x_data')
z_prior = tf.placeholder(tf.float32,[None,z_size],name='z_prior')
keep_prob = tf.placeholder(tf.float32,name='keep_prob')
global_step = tf.Variable(0,name="global_step",trainable=False)
x_generated,g_params = build_generator(z_prior)
y_data,y_generated,d_params = build_discriminator(x_data,x_generated,keep_prob)
d_loss =-( tf.log(y_data) + tf.log(1-y_generated))
g_loss = -(tf.log(y_generated))
optimizer = tf.train.AdamOptimizer(0.0001)
d_trainer= optimizer.minimize(d_loss,var_list=d_params)
g_trainer = optimizer.minimize(g_loss,var_list=g_params)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init)
if to_restore:
chkpt_fname = tf.train.latest_checkpoint(output_path)
saver.restore(sess, chkpt_fname)
else:
if os.path.exists(output_path):
shutil.rmtree(output_path)
os.mkdir(output_path)
z_sample_val = np.random.normal(0,1,size=(batch_size,z_size)).astype(np.float32)
steps = 60000 / batch_size
for i in range(sess.run(global_step),max_epoch):
for j in np.arange(steps):
print("epoch:%s, iter:%s" % (i, j))
# 每一步迭代，我们都会加载256个训练样本，然后执行一次train_step
x_value, _ = mnist.train.next_batch(batch_size)
x_value = 2 * x_value.astype(np.float32) - 1
z_value = np.random.normal(0, 1, size=(batch_size, z_size)).astype(np.float32)
# 执行生成
sess.run(d_trainer,
feed_dict={x_data: x_value, z_prior: z_value, keep_prob: 0.7})
# 执行判别
if j % 1 == 0:
sess.run(g_trainer,
feed_dict={x_data: x_value, z_prior: z_value, keep_prob: 0.7})
x_gen_val = sess.run(x_generated, feed_dict={z_prior: z_sample_val})
show_result(x_gen_val, "output/sample{0}.jpg".format(i))
z_random_sample_val = np.random.normal(0, 1, size=(batch_size, z_size)).astype(np.float32)
x_gen_val = sess.run(x_generated, feed_dict={z_prior: z_random_sample_val})
show_result(x_gen_val, "output/random_sample{0}.jpg".format(i))
sess.run(tf.assign(global_step, i + 1))
saver.save(sess, os.path.join(output_path, "model"), global_step=global_step)
train()