生死看淡，不服就GAN(六)----用DCGAN生成马的彩色图片
1. 首先我们需要的一组真实样本集来自cifar10，因此先制作一个读取cifar10的脚本。
"""
-------------------------------------------------------生死看淡，不服就GAN-------------------------------------------------------------------------
PROJECT: PreProcess
Author: Ephemeroptera
Date:2019-3-19
QQ:605686962
"""
import numpy as np
import TFRecordTools
import matplotlib.pyplot as plt
# 数据集归一化
def NORMALIZATION(data):
from sklearn.preprocessing import MinMaxScaler
minmax = MinMaxScaler()
# 归一化
data2 = minmax.fit_transform(data)
return data2
# 获取cifar10 指定数据集
"""
kind：
0:飞机 1:汽车 2:鸟 3:猫 4:鹿
5:狗 6:狐狸 7:马 8:船 9:卡车
"""
def GetCifar10Data(CifarPath, kind):
import pickle
# 打开文件
fo = open(CifarPath, 'rb')
# 加载文件
cifar10_dict = pickle.load(fo, encoding='bytes')
# cifar10_n标签集
cifar10_label = cifar10_dict.get(b'labels')
# cifar10_n数据集
cifar10_data = cifar10_dict.get(b'data')
# 提取指定类数据
L = [label for label in cifar10_label if label == kind]
C = [cifar10_data[label[0]] for label in enumerate(cifar10_label) if label[1] == kind]
# 转化为np数组
C = np.array(C)
L = np.array(L)
# 关闭文件
fo.close()
print('成功读取cifar10：%s --类别：%d 数据' % (CifarPath, kind))
return C,L
# 读取全部数据集
def GetCifar10AllData(kind):
C, L = GetCifar10Data(r'./cifar-10-batches-py/data_batch_1', kind)
for i in range(2, 6):
filename = './/cifar-10-batches-py//data_batch_' + str(i)
# 读取batch_n文件
data, label = GetCifar10Data(filename, kind)
# 拼接
C = np.concatenate((C, data))
L = np.concatenate((L, label))
return C,L
if __name__ == '__main__':
# 取某一类
C,L = GetCifar10AllData(7)
# 图像归一化
C = NORMALIZATION(C)
# 显示
imgs = C[-26:-1].reshape(-1,3,32,32).transpose((0,2,3,1))
fig, axes = plt.subplots(figsize=(7, 7), nrows=5, ncols=5, sharex=True, sharey=True)
for ax,img in zip(axes.flatten(),imgs):
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
ax.imshow(img)
plt.show()
# 存入TFR
TFRecordTools.SaveByTFRecord(C,L,r'./TFR/class7',5)
在该脚本中指定cifar路径和种类（马：7）再运行，读取cifar内容并以TFRecord格式保存，TFRecord是tensorflow便捷的数据集读取格式，上述依赖TFRecordTools脚本下载链接：
https://download.csdn.net/download/ephemeroptera/11088005
可视化如下：
如图所示，将马的数据集分割成5个TFR文件保存
2.DCGAN的搭建，代码如下（已给出详细注释）
"""
-------------------------------------------------------生死看淡，不服就GAN-------------------------------------------------------------------------
PROJECT: CIFAR10_DCGAN
Author: Ephemeroptera
Date:2019-3-19
QQ:605686962
"""
# 导入包
import numpy as np
import tensorflow as tf
import pickle
import TFRecordTools
import time
############################################### 设置参数 ####################################################################################
real_shape = [-1,32,32,3] # 真实样本尺寸
data_total = 5000 # 真实样本个数
batch_size = 64 # 批大小
noise_size = 128 # 噪声维度
max_iters = 10000 #的最大迭代次数
learning_rate = 0.0002 # 学习率
smooth = 0.1 # 标签平滑参数（label*(1-smooth)）
beta1 = 0.4 #ADAM参数
CRITIC_NUM = 1 # 每次迭代判别器训练次数
############################################# 定义生成器和判别器 #############################################################################
# 定义生成器（32x32图片）
def Generator_DC_32x32(z, channel, is_train=True):
"""
:param z: 噪声信号，tensor类型
:param channnel: 生成图片的通道数
:param is_train: 是否为训练状态，该参数主要用于作为batch_normalization方法中的参数使用(训练时候开启)
"""
# 训练时生成器不允许复用
with tf.variable_scope("generator", reuse=(not is_train)):
# layer1: noise_dim --> 4*4*512 --> 4x4x512 -->BN+relu
layer1 = tf.layers.dense(z, 4 * 4 * 512)
layer1 = tf.reshape(layer1, [-1, 4, 4, 512])
layer1 = tf.layers.batch_normalization(layer1, training=is_train,)
layer1 = tf.nn.relu(layer1)
# layer1 = tf.nn.dropout(layer1, keep_prob=0.8)# dropout
# layer2: deconv(ks=3x3,s=2,padding=same):4x4x512 --> 8x8x256 --> BN+relu
layer2 = tf.layers.conv2d_transpose(layer1, 256, 3, strides=2, padding='same',
kernel_initializer=tf.random_normal_initializer(0, 0.02),
bias_initializer=tf.random_normal_initializer(0, 0.02))
layer2 = tf.layers.batch_normalization(layer2, training=is_train)
layer2 = tf.nn.relu(layer2)
# layer2 = tf.nn.dropout(layer2, keep_prob=0.8)# dropout
# layer3: deconv(ks=3x3,s=2,padding=same):8x8x256 --> 16x16x128 --> BN+relu
layer3 = tf.layers.conv2d_transpose(layer2, 128, 3, strides=2, padding='same',
kernel_initializer=tf.random_normal_initializer(0, 0.02),
bias_initializer=tf.random_normal_initializer(0, 0.02))
layer3 = tf.layers.batch_normalization(layer3, training=is_train)
layer3 = tf.nn.relu(layer3)
# layer3 = tf.nn.dropout(layer3, keep_prob=0.8)# dropout
# layer4: deconv(ks=3x3,s=2,padding=same):16x16x128 --> 32x32x64--> BN+relu
layer4 = tf.layers.conv2d_transpose(layer3, 64, 3, strides=2, padding='same',
kernel_initializer=tf.random_normal_initializer(0, 0.02),
bias_initializer=tf.random_normal_initializer(0, 0.02))
layer4 = tf.layers.batch_normalization(layer4, training=is_train)
layer4 = tf.nn.relu(layer4)
# layer4 = tf.nn.dropout(layer3, keep_prob=0.8)# dropout
# logits: deconv(ks=3x3,s=2,padding=same):32x32x64 --> 32x32x3
logits = tf.layers.conv2d_transpose(layer4, channel, 3, strides=1, padding='same',
kernel_initializer=tf.random_normal_initializer(0, 0.02),
bias_initializer=tf.random_normal_initializer(0, 0.02))
# outputs
outputs = tf.tanh(logits)
return logits,outputs
# 定义判别器（32x32）
def Discriminator_DC_32x32(inputs_img, reuse=False, GAN = False,GP= False,alpha=0.2):
"""
@param inputs_img: 输入图片，tensor类型
@param reuse:判别器复用
@param GP: 使用WGAN-GP时关闭BN
@param alpha: Leaky ReLU系数
"""
with tf.variable_scope("discriminator", reuse=reuse):
# layer1: conv(ks=3x3,s=2,padding=same)+lrelu -->32x32x3 to 16x16x128
layer1 = tf.layers.conv2d(inputs_img, 128, 3, strides=2, padding='same')
if GP is False:
layer1 = tf.layers.batch_normalization(layer1, training=True)
layer1 = tf.nn.leaky_relu(layer1,alpha=alpha)
# layer1 = tf.nn.dropout(layer1, keep_prob=0.8)
# layer2: conv(ks=3x3,s=2,padding=same)+BN+lrelu -->16x16x128 to 8x8x256
layer2 = tf.layers.conv2d(layer1, 256, 3, strides=2, padding='same')
if GP is False:
layer2 = tf.layers.batch_normalization(layer2, training=True)
layer2 = tf.nn.leaky_relu(layer2, alpha=alpha)
# layer2 = tf.nn.dropout(layer2, keep_prob=0.8)
# layer3: conv(ks=3x3,s=2,padding=same)+BN+lrelu -->8x8x256 to 4x4x512
layer3 = tf.layers.conv2d(layer2, 512, 3, strides=2, padding='same')
if GP is False:
layer3 = tf.layers.batch_normalization(layer3, training=True)
layer3 = tf.nn.leaky_relu(layer3, alpha=alpha)
layer3 = tf.reshape(layer3, [-1, 4*4* 512])
# layer3 = tf.nn.dropout(layer2, keep_prob=0.8)
# logits,output:
logits = tf.layers.dense(layer3, 1)
"WGAN:去除sigmoid"
if GAN:
outputs = None
else:
outputs = tf.sigmoid(logits)
return logits, outputs
############################################## 定义计算图（网络） #######################################################
#----------------------输入----------------
inputs_real = tf.placeholder(tf.float32, [None, real_shape[1], real_shape[2], real_shape[3]], name='inputs_real') # 真实样本输入
inputs_noise = tf.placeholder(tf.float32, [None, noise_size], name='inputs_noise') # 生成样本输入
#-------------------生成和判别--------------
# 生成样本
_,g_outputs = Generator_DC_32x32(inputs_noise, real_shape[3], is_train=True) # 训练生成器
_,g_test = Generator_DC_32x32(inputs_noise, real_shape[3], is_train=False) # 测试生成器
# 判别样本
d_logits_real, _ = Discriminator_DC_32x32(inputs_real) #识别真样本
d_logits_fake, _ = Discriminator_DC_32x32(g_outputs, reuse=True) ##识别假样本
#------------定义原始GAN的损失函数--------------
# 生成器loss
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
labels=tf.ones_like(d_logits_fake) * (1 - smooth)))
# 判别器loss_real
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
labels=tf.ones_like(d_logits_real) * (1 - smooth)))
# 判别器loss_fake
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,labels=tf.zeros_like(d_logits_fake)))
# 判别器loss
d_loss = tf.add(d_loss_real, d_loss_fake)
#-------------------训练模型-----------------
# 分别获取生成器和判别器的变量空间
train_vars = tf.trainable_variables()
g_vars = [var for var in train_vars if var.name.startswith("generator")]
d_vars = [var for var in train_vars if var.name.startswith("discriminator")]
# Optimizer
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):# 保证BN白化先完成
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars) # 训练生成器
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars) # 训练判别器
############################################# 调用TFRecord读取数据 #####################################################
# 读取TFR,不打乱文件顺序，指定数据类型，开启多线程
[data,label] = TFRecordTools.ReadFromTFRecord(sameName= r'.\TFR\class7-*',isShuffle= False,datatype= tf.float64,
labeltype= tf.int32,isMultithreading= True)
# 批量处理，送入队列数据，指定数据大小，打乱数据项，设置批次大小64
[data_batch,label_batch] = TFRecordTools.DataBatch(data,label,dataSize= 32*32*3,labelSize= 1,
isShuffle= True,batchSize= 64)
############################################### 迭代 ###################################################################
# 存储训练过程中生成日志
GenLog = []
# 存储loss
losses = []
# 保存生成器变量(仅保存生成器模型，保存最近5个)
saver = tf.train.Saver(var_list=[var for var in tf.trainable_variables()
if var.name.startswith("generator")],max_to_keep=5)
# 定义批预处理
def batch_preprocess(data_batch):
# 提取批数据
batch = sess.run(data_batch)
# 整理成RGB（Nx32x32x3）
batch_images = np.reshape(batch, [-1, 3, 32, 32]).transpose((0, 2, 3, 1)) # (-1,32,32,3)
# scale to -1, 1
batch_images = batch_images * 2 - 1
return batch_images
# 生成相关目录保存生成信息
def GEN_DIR():
import os
if not os.path.isdir('ckpt'):
print('文件夹ckpt未创建，现在在当前目录下创建..')
os.mkdir('ckpt')
if not os.path.isdir('trainLog'):
print('文件夹ckpt未创建，现在在当前目录下创建..')
os.mkdir('trainLog')
# 开启会话
with tf.Session() as sess:
# 生成相关目录
GEN_DIR()
# 初始化变量
init = (tf.global_variables_initializer(), tf.local_variables_initializer())
sess.run(init)
# 开启协调器
coord = tf.train.Coordinator()
# 启动线程
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
time_start = time.time() # 开始计时
for steps in range(max_iters):
steps += 1
# 判别器重复训练设置
if steps < 25 or steps % 500 == 0:
critic_num = CRITIC_NUM
else:
critic_num = CRITIC_NUM
batch_noise = np.random.normal(size=(batch_size, noise_size)) # 高斯噪声
batch_images = batch_preprocess(data_batch)# 真实图像样本
# 重复训练判别器
for i in range(CRITIC_NUM):
_ = sess.run(d_train_opt, feed_dict={inputs_real: batch_images,
inputs_noise: batch_noise})
# 训练生成器
_ = sess.run(g_train_opt, feed_dict={inputs_real: batch_images,
inputs_noise: batch_noise})
# 记录训练信息
if steps % 5 == 1:
# （1）记录损失函数
train_loss_d = d_loss.eval({inputs_real: batch_images,
inputs_noise: batch_noise})
train_loss_g = g_loss.eval({inputs_real: batch_images,
inputs_noise: batch_noise})
losses.append([train_loss_d, train_loss_g,steps])
# （2）记录生成样本
batch_noise = np.random.normal(size=(batch_size, noise_size))
gen_samples = sess.run(g_test, feed_dict={inputs_noise: batch_noise})
genLog = (gen_samples[0:11] + 1) / 2 # 恢复颜色空间(取10张)
GenLog.append(genLog)
# (3)打印信息
print('step {}...'.format(steps),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}...".format(train_loss_g))
# （4）保存生成模型
if steps % 300 ==0:
saver.save(sess, './ckpt/generator.ckpt', global_step=steps)
# 关闭线程
coord.request_stop()
coord.join(threads)
#计时结束：
time_end = time.time()
print('迭代结束，耗时：%.2f秒'%(time_end-time_start))
# 保存信息
# 保存loss记录
with open('./trainLog/loss_variation.loss', 'wb') as l:
losses = np.array(losses)
pickle.dump(losses,l)
print('保存loss信息..')
# 保存生成日志
with open('./trainLog/GenLog.log', 'wb') as g:
pickle.dump(GenLog, g)
print('保存GenLog信息..')
经过10000次迭代
训练过程中保存了G的生成日志，G和D的损失函数（trainLog目录下），以及G的模型（ckpt目录下）
3.查看生成日志和测试生成器
"""
-------------------------------------------------------生死看淡，不服就GAN-------------------------------------------------------------------------
PROJECT: Show
Author: Ephemeroptera
Date:2019-3-19
QQ:605686962
"""
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import pickle
def ImgShow(IMG,index,nums):
"""
:param IMG: 生成样本集合
:param index: 查看某一次训练的下标（列表格式）
:param nums: 显示某一次生成样本的个数
"""
# 定义坐标系
fig, axes = plt.subplots(figsize=( nums+2,len(index)+2), nrows=len(index), ncols=nums, sharey=True, sharex=True)
if len(index) == 1:
for ax,img in zip(axes.flatten(),IMG[index[0]]):
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
ax.imshow(img)
else:
for ax_row, idx in zip(axes, index):
img_row = IMG[idx][0:nums]
for img,ax in zip(img_row,ax_row):
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
ax.imshow(img)
fig.tight_layout(pad=0)
plt.show()
if __name__ == '__main__':
##################################################### 生成日志 ###########################################################################
### (1) 显示最后一批
with open('./trainLog/GenLog.log', 'rb') as f:
#读取生成记录
GenLog = pickle.load(f)
GenLog = np.array(GenLog)
# 显示最后一次
ImgShow(GenLog,[-1],10)
### (2) 显示过程
# 均匀采样10次
epoch_idx0 = np.linspace(1, GenLog.shape[0] - 1, 20)
epoch_idx = [int(i) for i in epoch_idx0]
ImgShow(GenLog,epoch_idx,10)
############################################### 显示损失函数 ##################################################################################
with open(r'./trainLog/loss_variation.loss','rb') as l:
losses = pickle.load(l)
fig, ax = plt.subplots(figsize=(20, 7))
plt.plot(losses.T[2],losses.T[0], label='Discriminator Loss')
plt.plot(losses.T[2],losses.T[1], label='Generator Loss')
plt.title("Training Losses")
plt.legend()
plt.show()
############################################### 验证生成器 ###################################################################################
with tf.Session() as sess:
meta_graph = tf.train.import_meta_graph('./ckpt/generator.ckpt-9000.meta')# 加载模型
meta_graph.restore(sess,tf.train.latest_checkpoint('./ckpt'))# 加载最近一次数据
graph = tf.get_default_graph()
inputs_noise = graph.get_tensor_by_name("inputs_noise:0")# 获取输入占位符
d_outputs_fake = graph.get_tensor_by_name("generator/Tanh:0")
sample_noise= np.random.normal(size=(10, 128))# 生成输入噪声
gen_samples = sess.run(d_outputs_fake,feed_dict={inputs_noise: sample_noise})# 验证模型
gen_samples = [(gen_samples[0:11]+1)/2] # 恢复颜色空间
ImgShow(gen_samples, [0], 10)
最后一次生成样本
训练过程生成日志
损失函数
验证生成器
