GAN网络架构分析
上图即为GAN的逻辑架构，其中的noise vector就是特征向量z，real images就是输入变量x，标签的标准比较简单（二分类么），real的就是tf.ones，fake的就是tf.zeros。
网络具体形状大体如上，具体数值有所调整，生成器过程为：噪声向量-全连接-卷积-卷积-卷积，辨别器过程：图片-卷积-卷积-全连接-全连接。
和预想的不同，实际上数据在生成器中并不是从无到有由小变大的过程，而是由3136(56*56)经过正常卷积步骤下降为28*28的过程。
实现如下：
import datetime
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('../../Mnist_data') """测试数据""" # sample_image = mnist.train.next_batch(1)[0]
# print(sample_image.shape)
# sample_image = sample_image.reshape([28, 28])
# plt.imshow(sample_image, cmap='Greys') """分辨器""" def discriminator(images, reuse=None):
with tf.variable_scope(tf.get_variable_scope(), reuse=reuse) as scope:
# 卷积 + 激活 + 池化
d_w1 = tf.get_variable('d_w1',[5,5,1,32],initializer=tf.truncated_normal_initializer(stddev=0.02))
d_b1 = tf.get_variable('d_b1',[32],initializer=tf.constant_initializer(0))
d1 = tf.nn.conv2d(input=images,filter=d_w1,strides=[1,1,1,1],padding='SAME')
d1 = d1 + d_b1
d1 = tf.nn.relu(d1)
d1 = tf.nn.avg_pool(d1,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME') # 卷积 + 激活 + 池化
d_w2 = tf.get_variable('d_w2',[5,5,32,64],initializer=tf.truncated_normal_initializer(stddev=0.02))
d_b2 = tf.get_variable('d_b2',[64],initializer=tf.constant_initializer(0))
d2 = tf.nn.conv2d(input=d1,filter=d_w2,strides=[1,1,1,1],padding='SAME')
d2 = d2 + d_b2
d2 = tf.nn.relu(d2)
d2 = tf.nn.avg_pool(d2,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME') # 全连接 + 激活
d_w3 = tf.get_variable('d_w3',[7 * 7 * 64,1024],initializer=tf.truncated_normal_initializer(stddev=0.02))
d_b3 = tf.get_variable('d_b3',[1024],initializer=tf.constant_initializer(0))
d3 = tf.reshape(d2,[-1,7 * 7 * 64])
d3 = tf.matmul(d3,d_w3)
d3 = d3 + d_b3
d3 = tf.nn.relu(d3) # 全连接
d_w4 = tf.get_variable('d_w4',[1024,1],initializer=tf.truncated_normal_initializer(stddev=0.02))
d_b4 = tf.get_variable('d_b4',[1],initializer=tf.constant_initializer(0))
d4 = tf.matmul(d3,d_w4) + d_b4 # 最后输出一个非尺度化的值
return d4 """生成器""" def generator(z, batch_size, z_dim, reuse=False):
'''接收特征向量z，由z生成图片''' with tf.variable_scope(tf.get_variable_scope(),reuse=reuse):
# 全连接 + 批正则化 + 激活
# z_dim -> 3136 -> 56*56*1
g_w1 = tf.get_variable('g_w1', [z_dim, 3136], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.02))
g_b1 = tf.get_variable('g_b1', [3136], initializer=tf.truncated_normal_initializer(stddev=0.02))
g1 = tf.matmul(z, g_w1) + g_b1
g1 = tf.reshape(g1, [-1, 56, 56, 1])
g1 = tf.contrib.layers.batch_norm(g1, epsilon=1e-5, scope='bn1')
g1 = tf.nn.relu(g1) # 卷积 + 批正则化 + 激活
g_w2 = tf.get_variable('g_w2',[3,3,1,z_dim / 2],dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.02))
g_b2 = tf.get_variable('g_b2',[z_dim / 2],initializer=tf.truncated_normal_initializer(stddev=0.02))
g2 = tf.nn.conv2d(g1,g_w2,strides=[1,2,2,1],padding='SAME')
g2 = g2 + g_b2
g2 = tf.contrib.layers.batch_norm(g2,epsilon=1e-5,scope='bn2')
g2 = tf.nn.relu(g2)
g2 = tf.image.resize_images(g2,[56,56]) # 卷积 + 批正则化 + 激活
g_w3 = tf.get_variable('g_w3',[3,3,z_dim / 2,z_dim / 4],dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.02))
g_b3 = tf.get_variable('g_b3',[z_dim / 4],initializer=tf.truncated_normal_initializer(stddev=0.02))
g3 = tf.nn.conv2d(g2,g_w3,strides=[1,2,2,1],padding='SAME')
g3 = g3 + g_b3
g3 = tf.contrib.layers.batch_norm(g3,epsilon=1e-5,scope='bn3')
g3 = tf.nn.relu(g3)
g3 = tf.image.resize_images(g3,[56,56]) # 卷积 + 激活
g_w4 = tf.get_variable('g_w4',[1,1,z_dim / 4,1],dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.02))
g_b4 = tf.get_variable('g_b4',[1],initializer=tf.truncated_normal_initializer(stddev=0.02))
g4 = tf.nn.conv2d(g3,g_w4,strides=[1,2,2,1],padding='SAME')
g4 = g4 + g_b4
g4 = tf.sigmoid(g4) # 输出g4的维度: batch_size x 28 x 28 x 1
return g4
逻辑实现如下，不同组成部分的loss值是分开计算的：
"""逻辑架构""" tf.reset_default_graph()
batch_size = 50
z_dimensions = 100 z_placeholder = tf.placeholder(tf.float32, [None, z_dimensions], name='z_placeholder')
x_placeholder = tf.placeholder(tf.float32, shape = [None,28,28,1], name='x_placeholder') Gz = generator(z_placeholder, batch_size, z_dimensions) # 根据z生成伪造图片
Dx = discriminator(x_placeholder) # 辨别器辨别真实图片
Dg = discriminator(Gz, reuse=True) # 辨别器辨别伪造图片 #discriminator 的loss 分为两部分
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = Dx, labels = tf.ones_like(Dx)))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = Dg, labels = tf.zeros_like(Dg)))
d_loss=d_loss_real + d_loss_fake
# Generator的目标是生成尽可能真实的图像，所以计算Dg和1的loss
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = Dg, labels = tf.ones_like(Dg)))
优化器部分有一些注意点：
"""优化部分""" # 由于训练时生成器和辨别器是分开训练的，
# 所以不同的训练过程对应的优化参数是要做区分的
tvars = tf.trainable_variables() d_vars = [var for var in tvars if 'd_' in var.name]
g_vars = [var for var in tvars if 'g_' in var.name] d_trainer_real = tf.train.AdamOptimizer(0.0003).minimize(d_loss_real, var_list=d_vars)
d_trainer_fake = tf.train.AdamOptimizer(0.0003).minimize(d_loss_fake, var_list=d_vars)
d_trainer = tf.train.AdamOptimizer(0.0003).minimize(d_loss, var_list=d_vars)
g_trainer = tf.train.AdamOptimizer(0.0001).minimize(g_loss, var_list=g_vars)
入注释所说，训练不同的位置，优化不同的参数，不可以混淆，所以这里就涉及了tf变量提取的手法，结果展示如下：
import pprint
pp = pprint.PrettyPrinter()
pp.pprint(d_vars)
pp.pprint(g_vars) [<tf.Variable 'd_w1:0' shape=(5, 5, 1, 32) dtype=float32_ref>,
<tf.Variable 'd_b1:0' shape=(32,) dtype=float32_ref>,
<tf.Variable 'd_w2:0' shape=(5, 5, 32, 64) dtype=float32_ref>,
<tf.Variable 'd_b2:0' shape=(64,) dtype=float32_ref>,
<tf.Variable 'd_w3:0' shape=(3136, 1024) dtype=float32_ref>,
<tf.Variable 'd_b3:0' shape=(1024,) dtype=float32_ref>,
<tf.Variable 'd_w4:0' shape=(1024, 1) dtype=float32_ref>,
<tf.Variable 'd_b4:0' shape=(1,) dtype=float32_ref>] [<tf.Variable 'g_w1:0' shape=(100, 3136) dtype=float32_ref>,
<tf.Variable 'g_b1:0' shape=(3136,) dtype=float32_ref>,
<tf.Variable 'g_w2:0' shape=(3, 3, 1, 50) dtype=float32_ref>,
<tf.Variable 'g_b2:0' shape=(50,) dtype=float32_ref>,
<tf.Variable 'g_w3:0' shape=(3, 3, 50, 25) dtype=float32_ref>,
<tf.Variable 'g_b3:0' shape=(25,) dtype=float32_ref>,
<tf.Variable 'g_w4:0' shape=(1, 1, 25, 1) dtype=float32_ref>,
<tf.Variable 'g_b4:0' shape=(1,) dtype=float32_ref>]
之后是训练过程：
"""迭代训练""" sess = tf.Session()
sess.run(tf.global_variables_initializer()) # 对discriminator的预训练
for i in range(300):
print('.',end='')
z_batch = np.random.normal(0, 1, size=[batch_size, z_dimensions])
real_image_batch = mnist.train.next_batch(batch_size)[0].reshape([batch_size, 28, 28, 1])
# 用real and fake images分别对discriminator训练
_, __, dLossReal, dLossFake = sess.run([d_trainer_real, d_trainer_fake, d_loss_real, d_loss_fake],
{x_placeholder: real_image_batch, z_placeholder: z_batch}) if (i % 100 == 0):
print("\rdLossReal:",dLossReal,"dLossFake:",dLossFake) # 交替训练 generator和discriminator
for i in range(100000):
print('.',end='')
real_image_batch = mnist.train.next_batch(batch_size)[0].reshape([batch_size, 28, 28, 1])
z_batch = np.random.normal(0, 1, size=[batch_size, z_dimensions]) # 用real and fake images同时对discriminator训练
_,dLossReal,dLossFake = sess.run([d_trainer,d_loss_real,d_loss_fake],
{x_placeholder: real_image_batch,z_placeholder: z_batch})
# 训练generator
z_batch = np.random.normal(0,1,size=[batch_size,z_dimensions])
_ = sess.run(g_trainer,feed_dict={z_placeholder: z_batch}) if i % 100 == 0:
# 每 100 iterations, 输出一个生成的图像
print("\rIteration:",i,"at",datetime.datetime.now())
z_batch = np.random.normal(0,1,size=[1,z_dimensions])
generated_images = generator(z_placeholder,1,z_dimensions, reuse=True)
images = sess.run(generated_images,{z_placeholder: z_batch})
plt.imshow(images[0].reshape([28,28]),cmap='Greys')
plt.show()
# 输出discriminator的值
im = images[0].reshape([1,28,28,1])
result = discriminator(x_placeholder, reuse=True)
estimate = sess.run(result,{x_placeholder: im})
print("Estimate:",np.squeeze(estimate))
先预训练分辨器，
然后交替训练分辨器和生成器。
其实是有一点图片可以展示的，但是我的电脑性能太渣（苏菲4），跑了600轮左右的迭代我实在于心不忍了，先搁置吧... 以后有机会回实验室在说，至少原理是体会到了。
共享变量
之前看文档时体会不深，现在大体明白共享变量的存在意义了，它是在设计计算图时考虑的：
同一个变量如果有不同的数据流（计算图中不同的节点在不同的时刻去给同一个节点的同一个输入位置提供数据），
- Variable变量会之间创建两个不同的变量节点去接收不同的数据流
- get_variable变量在reuse为True时会使用同一个变量应付不同的数据流
这也就是共享变量的应用之处。这在上面的程序中体现在判别器的任务，如果接收到的是生成器生成的图像，判别器就尝试优化自己的网络结构来使自己输出0，如果接收到的是来自真实数据的图像，那么就尝试优化自己的网络结构来使自己输出1。也就是说，fake图像和real图像经过判别器的时候，要共享同一套变量，所以TensorFlow引入了变量共享机制，而和正常的卷积网络不同的是这里的fake和real变量并不在同一个计算图节点位置（real图片在x节点处输入，而fake图则在生成器输出节点位置计入计算图）。