Generative Adversarial Nets[Vanilla]

import tensorflow as tfimport randomimport numpy as npimport matplotlib.pyplot as plt%matplotlib inlinefrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("MNIST_data/")#手动下载mnist的4个gz压缩包，放入当前路径的MNIST_data文件夹下面

#先定义卷积和平均池化的函数，这2个就是常见的CNN的卷积和池化操作def conv2d(x, W): ?#input:[batch, in_height, in_width, in_channels] ?#filter:[filter_height, filter_width, in_channels, out_channels] ?return tf.nn.conv2d(input=x, filter=W, strides=[1, 1, 1, 1], padding=‘SAME‘)def avg_pool_2x2(x): ?return tf.nn.avg_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=‘SAME‘)

def discriminator(x_image, reuse=False): ???with tf.variable_scope(‘discriminator‘) as scope: ???????if reuse: ???????????tf.get_variable_scope().reuse_variables() ???????‘‘‘第一层:卷积层和池化层，该层的激活函数为ReLU‘‘‘ ???????#结构为:conv->ReLU->avgPool ???????#卷积层感受野大小5x5，输入channel(或者叫做depth)为1，输出channel为8; 输出的feature map为14*14*8 ???????W_conv1 = tf.get_variable(‘d_wconv1‘, [5, 5, 1, 8], initializer=tf.truncated_normal_initializer(stddev=0.02)) ???????b_conv1 = tf.get_variable(‘d_bconv1‘, [8], initializer=tf.constant_initializer(0)) ???????h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) ???????h_pool1 = avg_pool_2x2(h_conv1) ???????‘‘‘第二层:卷积层和池化层，其他如第一层所述; ‘‘‘ ???????#输出的feature map为7*7*16 ???????W_conv2 = tf.get_variable(‘d_wconv2‘, [5, 5, 8, 16], initializer=tf.truncated_normal_initializer(stddev=0.02)) ???????b_conv2 = tf.get_variable(‘d_bconv2‘, [16], initializer=tf.constant_initializer(0)) ???????h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) ???????h_pool2 = avg_pool_2x2(h_conv2) ???????‘‘‘第三层:一个全连接层，输入维度7*7*16，输出维度32‘‘‘ ???????W_fc1 = tf.get_variable(‘d_wfc1‘, [7 * 7 * 16, 32], initializer=tf.truncated_normal_initializer(stddev=0.02)) ???????b_fc1 = tf.get_variable(‘d_bfc1‘, [32], initializer=tf.constant_initializer(0)) ???????h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*16]) ???????h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) ???????‘‘‘第四层:一个全连接层，输入维度32，输出维度1，用于判别当前输入图片属于real data的概率，此处无激活函数‘‘‘ ???????W_fc2 = tf.get_variable(‘d_wfc2‘, [32, 1], initializer=tf.truncated_normal_initializer(stddev=0.02)) ???????b_fc2 = tf.get_variable(‘d_bfc2‘, [1], initializer=tf.constant_initializer(0)) ???????y_conv=(tf.matmul(h_fc1, W_fc2) + b_fc2) ???return y_conv

def generator(z, batch_size, z_dim, reuse=False): ???#z:输入的噪音向量 ???with tf.variable_scope(‘generator‘) as scope: ???????if reuse: ???????????tf.get_variable_scope().reuse_variables() ???????g_dim = 64 #生成器第一层的channel个数 ????????c_dim = 1 #输出的颜色空间维度 (MNIST 是灰度图片，所以 c_dim = 1) ???????s = 28 #图片的输出尺寸 ???????s2, s4, s8, s16 = int(s/2), int(s/4), int(s/8), int(s/16) #为了缓慢的上采样，变化尽可能的小。分别为14，7，3，2 ???????‘‘‘输入z是基于随机采样生成的，即噪音输入‘‘‘ ???????#h0 的维度:[ batch_size, 2, 2, 25]，所以z的维度为[batch_size, 100] ???????h0 = tf.reshape(z, [batch_size, s16+1, s16+1, 25]) ???????h0 = tf.nn.relu(h0) ???????????????‘‘‘第一个解卷积层，采用conv2d_transpose实现‘‘‘ ???????#先定义权重和偏置, ???????#H_conv1的维度:[batch_size, 3, 3, 256] ???????output1_shape = [batch_size, s8, s8, g_dim*4]#[batch_size,3,3,64] ???????W_conv1 = tf.get_variable(‘g_wconv1‘, [5, 5, output1_shape[-1], int(h0.get_shape()[-1])], ??????????????????????????????????initializer=tf.truncated_normal_initializer(stddev=0.1)) ???????#b_conv1 = tf.get_variable(‘g_bconv1‘, [output1_shape[-1]], initializer=tf.constant_initializer(.1)) ???????#采用conv2d_transpose实现解卷积，并加上BN，ReLU ???????#conv2d_transpose: ???????# ????参数1 input(h0) - [batch, height, width, in_channels]或者batch, in_channels, height, width] ???????# ????参数2 filter(W_conv1) - [height, width, output_channels, in_channels] ???????H_conv1 = tf.nn.conv2d_transpose(h0, W_conv1, output_shape=output1_shape, strides=[1, 2, 2, 1], padding=‘SAME‘) ???????#H_conv1 ?= tf.reshape(tf.nn.bias_add(H_conv1, b_conv1), H_conv1.get_shape()) ???????H_conv1 = tf.contrib.layers.batch_norm(inputs = H_conv1, center=True, scale=True, is_training=True, scope="g_bn1") ???????H_conv1 = tf.nn.relu(H_conv1) ???????????????‘‘‘第二个解卷积层‘‘‘ ???????#H_conv2的维度:[batch_size, 6, 6, 128] ???????output2_shape = [batch_size, s4 - 1, s4 - 1, g_dim*2] ???????W_conv2 = tf.get_variable(‘g_wconv2‘, [5, 5, output2_shape[-1], int(H_conv1.get_shape()[-1])], ??????????????????????????????????initializer=tf.truncated_normal_initializer(stddev=0.1)) ???????#b_conv2 = tf.get_variable(‘g_bconv2‘, [output2_shape[-1]], initializer=tf.constant_initializer(.1)) ???????H_conv2 = tf.nn.conv2d_transpose(H_conv1, W_conv2, output_shape=output2_shape, strides=[1, 2, 2, 1], padding=‘SAME‘) ???????#H_conv2 = tf.reshape(tf.nn.bias_add(H_conv2, b_conv2), H_conv2.get_shape()) ???????H_conv2 = tf.contrib.layers.batch_norm(inputs = H_conv2, center=True, scale=True, is_training=True, scope="g_bn2") ???????H_conv2 = tf.nn.relu(H_conv2) ???????????????‘‘‘第三个解卷积层‘‘‘ ???????#H_conv3的维度:[batch_size, 12, 12, 64] ???????output3_shape = [batch_size, s2 - 2, s2 - 2, g_dim*1] ???????W_conv3 = tf.get_variable(‘g_wconv3‘, [5, 5, output3_shape[-1], int(H_conv2.get_shape()[-1])], ??????????????????????????????????initializer=tf.truncated_normal_initializer(stddev=0.1)) ???????#b_conv3 = tf.get_variable(‘g_bconv3‘, [output3_shape[-1]], initializer=tf.constant_initializer(.1)) ???????H_conv3 = tf.nn.conv2d_transpose(H_conv2, W_conv3, output_shape=output3_shape, strides=[1, 2, 2, 1], padding=‘SAME‘) ???????#H_conv3 = tf.reshape(tf.nn.bias_add(H_conv3, b_conv3 ), H_conv3.get_shape()) ???????H_conv3 = tf.contrib.layers.batch_norm(inputs = H_conv3, center=True, scale=True, is_training=True, scope="g_bn3") ???????H_conv3 = tf.nn.relu(H_conv3) ??????????????‘‘‘第四个解卷积层‘‘‘ ???????#H_conv4的维度:[batch_size, 28, 28, 1] ???????output4_shape = [batch_size, s, s, c_dim] ???????W_conv4 = tf.get_variable(‘g_wconv4‘, [5, 5, output4_shape[-1], int(H_conv3.get_shape()[-1])], ??????????????????????????????????initializer=tf.truncated_normal_initializer(stddev=0.1)) ???????#b_conv4 = tf.get_variable(‘g_bconv4‘, [output4_shape[-1]], initializer=tf.constant_initializer(.1)) ???????H_conv4 = tf.nn.conv2d_transpose(H_conv3, W_conv4, output_shape=output4_shape, strides=[1, 2, 2, 1], padding=‘VALID‘) ???????#H_conv4 = tf.reshape(tf.nn.bias_add(H_conv4, b_conv4), H_conv4.get_shape()) ???????H_conv4 = tf.nn.tanh(H_conv4) ???????????return H_conv4

batch_size = 16z_dimensions = 100tf.reset_default_graph() #sess = tf.Session()‘‘‘设定real data和噪音的占位数据 ‘‘‘x_placeholder = tf.placeholder("float", shape = [None,28,28,1]) #real data的输入z_placeholder = tf.placeholder(tf.float32, [None, z_dimensions]) #输入到生成器中的噪音‘‘‘基于编写好的判别器和生成器建立关系 ‘‘‘Dx = discriminator(x_placeholder) ??????????????????????????????????????#判别器，对real data的判别概率(unnormalized)Gz = generator(z_placeholder, batch_size, z_dimensions) #生成器，基于噪音数据，生成伪造数据Dg = discriminator(Gz, reuse=True) ???????????????????????????????????#判别器，对生成器生成的伪造图片的判别概率 (unnormalized) ‘‘‘生成器的loss定义 ‘‘‘#‘‘‘对伪造图片判别结果的loss值: <判别结果, ?期望其为1>之间的交叉熵值 ‘‘‘g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=Dg, labels=tf.ones_like(Dg)))‘‘‘判别器的loss定义 ‘‘‘#‘‘‘对真实图片判别结果的loss值: <判别结果，本身为1>之间的交叉熵值 ‘‘‘d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=Dx, labels=tf.ones_like(Dx)))#‘‘‘对伪造图片判别结果loss值: <判别结果，本身为0>之间的交叉熵值 ‘‘‘d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=Dg, labels=tf.zeros_like(Dg)))#上述两个loss值相加d_loss = d_loss_real + d_loss_fake‘‘‘从graph中提取所有可以训练的变量，并区分出判别器的变量和生成器的变量 ‘‘‘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]‘‘‘调用SGD进行判别器和生成器loss的迭代训练 ‘‘‘with tf.variable_scope(tf.get_variable_scope(), reuse=False): ???trainerD = tf.train.AdamOptimizer().minimize(d_loss, var_list=d_vars) ???trainerG = tf.train.AdamOptimizer().minimize(g_loss, var_list=g_vars)sess.run(tf.global_variables_initializer())iterations = 3000for i in range(iterations): ???‘‘‘生成噪音数据和读取真实数据 ‘‘‘ ???z_batch = np.random.normal(-1, 1, size=[batch_size, z_dimensions])#生成噪音数据 ???real_image_batch = mnist.train.next_batch(batch_size)#提取真实图片的minibatch并进行reshape ???real_image_batch = np.reshape(real_image_batch[0],[batch_size,28,28,1]) ??‘‘‘训练判别器，生成器 ‘‘‘ ???_,dLoss = sess.run([trainerD, d_loss],feed_dict={z_placeholder:z_batch,x_placeholder:real_image_batch}) #判别器 ???_,gLoss = sess.run([trainerG,g_loss],feed_dict={z_placeholder:z_batch}) #生成器‘‘‘训练结束之后，利用训练好的生成器，生成图片 ‘‘‘sample_image = generator(z_placeholder, 1, z_dimensions, reuse=True)z_batch = np.random.normal(-1, 1, size=[1, z_dimensions])temp = (sess.run(sample_image, feed_dict={z_placeholder: z_batch}))my_i = temp.squeeze()plt.imshow(my_i, cmap=‘gray_r‘)