这里的生成模型和判别模型均为多层感知机（当然也可以换为CNN或LSTM），多层感知机的层数为四层，中间有两个隐藏层。使用的数据集为mnist数据集，训练GAN之后得到的模型能达到的效果是：在生成模型中输入一个随机高斯噪声，生成模型可以输出一张和mnist数据集类似的图片。
#coding=utf-8
import pickle
import tensorflow as tf
import numpy as np
import matplotlib.gridspec as gridspec
import os
import shutil
from scipy.misc import imsave
# 定义一个mnist数据集的类
class mnistReader():
def __init__(self,mnistPath,onehot=True):
self.mnistPath=mnistPath
self.onehot=onehot
self.batch_index=0
print ('read:',self.mnistPath)
fo = open(self.mnistPath, 'rb')
self.train_set,self.valid_set,self.test_set = pickle.load(fo,encoding='bytes')
fo.close()
self.data_label_train=list(zip(self.train_set[0],self.train_set[1]))
np.random.shuffle(self.data_label_train)
# 获取下一个训练集的batch
def next_train_batch(self,batch_size=100):
if self.batch_index < int(len(self.data_label_train)/batch_size):
# print ("batch_index:",self.batch_index )
datum=self.data_label_train[self.batch_index*batch_size:(self.batch_index+1)*batch_size]
self.batch_index+=1
return self._decode(datum,self.onehot)
else:
self.batch_index=0
np.random.shuffle(self.data_label_train)
datum=self.data_label_train[self.batch_index*batch_size:(self.batch_index+1)*batch_size]
self.batch_index+=1
return self._decode(datum,self.onehot)
# 获取测试集的数据
def test_data(self):
tdata,tlabel=self.test_set
data_label_test=list(zip(tdata,tlabel))
return self._decode(data_label_test,self.onehot)
# 把一个batch的训练数据转换为可以放入模型训练的数据
def _decode(self,datum,onehot):
rdata=list() # batch训练数据
rlabel=list()
if onehot:
for d,l in datum:
rdata.append(np.reshape(d,[784])) # 转变形状为：一维向量
hot=np.zeros(10)
hot[int(l)]=1 # label设为100维的one-hot向量
rlabel.append(hot)
else:
for d,l in datum:
rdata.append(np.reshape(d,[784]))
rlabel.append(int(l))
return rdata,rlabel
img_height = 28 # mnist图片高度
img_width = 28 # mnist图片宽度
img_size = img_height * img_width # mnist图像总的大小
to_train = True
to_restore = False
output_path = "GAN2" # 保存的文件的路径
max_epoch = 500 # 最大迭代次数
h1_size = 150 # 第一个隐层的单元数
h2_size = 300 # 第二个隐层的单元数
z_size = 100 # 噪声向量的维度
batch_size = 256 # batch块大小
# 创建生成模型，输入为噪声张量，大小为 :batch_size * 100
def build_generator(z_prior):
# 第一个隐层层
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)
# 第二个隐层
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) # 偏置是加载batch的每个元素上，也就是和tensor的每行相加
# 输出层，输出一个 batch_size * 784 张量，每个元素值在（-1，1）之间
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) # tanh函数输出(-1,1)之间的某个值
g_params = [w1, b1, w2, b2, w3, b3]
return x_generate, g_params
# 创建生成模型，输入为真实图片和生成的图片
def build_discriminator(x_data, x_generated, keep_prob):
# 两个大小batch_size * 784的张量合并为一个 (batch_size*2) * 784的张量，每个tensor的每个元素是一行
x_in = tf.concat([x_data, x_generated], 0) # 相当于把batch_size扩大为2倍
# 第一个隐层
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)
# 第二个隐层
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)
# 输出层，输出一个 (2*batch_size) * 1 的张量
w3 = tf.Variable(tf.truncated_normal([h1_size, 1], stddev=0.1), name="d_w3", dtype=tf.float32)
b3 = tf.Variable(tf.zeros([1]), name="d_b3", dtype=tf.float32)
h3 = tf.matmul(h2, w3) + b3
# 计算原始图片和生成图片属于真实图片的概率，这里用sigmod函数来计算概率值，属于(0,1)之间
y_data = tf.nn.sigmoid(tf.slice(h3, [0, 0], [batch_size, -1], name=None)) # 大小：batch_size*1
'''
tf.slice(input_, begin, size, name = None)
解释 ：
这个函数的作用是从输入数据input中提取出一块切片,切片的尺寸是size，切片的开始位置是begin。
切片的尺寸size表示输出tensor的数据维度，其中size[i]表示在第i维度上面的元素个数。
'''
y_generated = tf.nn.sigmoid(tf.slice(h3, [batch_size, 0], [-1, -1], name=None)) # 大小：batch_size*1
d_params = [w1, b1, w2, b2, w3, b3]
return y_data, y_generated, d_params
# 开始训练GAN
def train():
mnist=mnistReader(mnistPath="E:/testdata/mnist.pkl")
x_data = tf.placeholder(tf.float32, [batch_size, img_size], name="x_data")
z_prior = tf.placeholder(tf.float32, [batch_size, 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)) # d_loss大小为 batch_size * 1
g_loss = - tf.log(y_generated) # g_loss大小为 batch_size * 1
optimizer = tf.train.AdamOptimizer(0.0001) # 定义优化器，学习率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()
sess = tf.Session()
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) # 删除目录树
os.mkdir(output_path) # 重新创建目录树
# 生成随机噪声
z_sample_val = np.random.normal(0, 1, size=(batch_size, z_size)).astype(np.float32)
# --------开始训练模型---------------------------------------------
for i in range(sess.run(global_step), max_epoch):
for j in range(int(50000/batch_size)):
print ("epoch:%s, iter:%s" % (i, j) )
x_value, _ =mnist.next_train_batch(batch_size=batch_size) # 256 * 784
x_value=np.array(x_value)
x_value = 2 * x_value.astype(np.float32) - 1
z_value = np.random.normal(0, 1, size=(batch_size, z_size)).astype(np.float32)
sess.run(d_trainer,feed_dict={x_data: x_value, z_prior: z_value, keep_prob: np.sum(0.7).astype(np.float32)})
if j % 1 == 0:
sess.run(g_trainer,feed_dict={x_data: x_value, z_prior: z_value, keep_prob: np.sum(0.7).astype(np.float32)})
# 生成一个样本图片
x_gen_val = sess.run(x_generated, feed_dict={z_prior: z_sample_val})
show_result(x_gen_val, os.path.join(output_path, "sample%s.jpg" % i))
# 再次生成一个样本图片
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, os.path.join(output_path, "random_sample%s.jpg" % i))
# 每次迭代保存模型
sess.run(tf.assign(global_step, i + 1))
'''
tf.assign(A, new_number): 这个函数的功能主要是把A的值变为new_number
'''
saver.save(sess, os.path.join(output_path, "model"), global_step=global_step)
# 保存生成的图片结果
def show_result(batch_res, fname, grid_size=(8, 8), grid_pad=5):
# 由于生成器生成的tensor的每个元素值位于(-1,1)之间，这里先变成(0,1)之间的值，并把形状变为像素矩阵
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 # 生成器生成的是0-1的值，所以要乘以255变成像素值
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)
if __name__ == '__main__':
train()
