宜配屋

原始生成对抗网络Generative Adversarial Networks GAN包含生成器Generator和判别器Discriminator，数据有真实数据groundtruth，还有需要网络生成的“fake”数据，目的是网络生成的fake数据可以“骗过”判别器，让判别器认不出来，就是让判别器分不清进入的数据是真实数据还是fake数据。总的来说是：判别器区分真实数据和fake数据的能力越强越好；生成器生成的数据骗过判别器的能力越强越好，这个是矛盾的，所以只能交替训练网络。

需要搭建生成器网络和判别器网络，训练的时候交替训练。

首先训练判别器的参数，固定生成器的参数，让判别器判断生成器生成的数据，让其和0接近，让判别器判断真实数据，让其和1接近；

接着训练生成器的参数，固定判别器的参数，让生成器生成的数据进入判别器，让判断结果和1接近。生成器生成数据需要给定随机初始值

线性版：

import torch
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
from torchvision import transforms
from torch import optim
import torch.nn as nn
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.gridspec as gridspec
 
def showimg(images,count):
 images=images.detach().numpy()[0:16,:]
 images=255*(0.5*images+0.5)
 images = images.astype(np.uint8)
 grid_length=int(np.ceil(np.sqrt(images.shape[0])))
 plt.figure(figsize=(4,4))
 width = int(np.sqrt((images.shape[1])))
 gs = gridspec.GridSpec(grid_length,grid_length,wspace=0,hspace=0)
 # gs.update(wspace=0, hspace=0)
 print('starting...')
 for i, img in enumerate(images):
 ax = plt.subplot(gs[i])
 ax.set_xticklabels([])
 ax.set_yticklabels([])
 ax.set_aspect('equal')
 plt.imshow(img.reshape([width,width]),cmap = plt.cm.gray)
 plt.axis('off')
 plt.tight_layout()
 print('showing...')
 plt.tight_layout()
 plt.savefig('./GAN_Image/%d.png'%count, bbox_inches='tight')
 
def loadMNIST(batch_size): #MNIST图片的大小是28*28
 trans_img=transforms.Compose([transforms.ToTensor()])
 trainset=MNIST('./data',train=True,transform=trans_img,download=True)
 testset=MNIST('./data',train=False,transform=trans_img,download=True)
 # device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 trainloader=DataLoader(trainset,batch_size=batch_size,shuffle=True,num_workers=10)
 testloader = DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=10)
 return trainset,testset,trainloader,testloader
 
class discriminator(nn.Module):
 def __init__(self):
 super(discriminator,self).__init__()
 self.dis=nn.Sequential(
  nn.Linear(784,300),
  nn.LeakyReLU(0.2),
  nn.Linear(300,150),
  nn.LeakyReLU(0.2),
  nn.Linear(150,1),
  nn.Sigmoid()
 )
 def forward(self, x):
 x=self.dis(x)
 return x
 
class generator(nn.Module):
 def __init__(self,input_size):
 super(generator,self).__init__()
 self.gen=nn.Sequential(
  nn.Linear(input_size,150),
  nn.ReLU(True),
  nn.Linear(150,300),
  nn.ReLU(True),
  nn.Linear(300,784),
  nn.Tanh()
 )
 def forward(self, x):
 x=self.gen(x)
 return x
 
if __name__=="__main__":
 criterion=nn.BCELoss()
 num_img=100
 z_dimension=100
 D=discriminator()
 G=generator(z_dimension)
 trainset, testset, trainloader, testloader = loadMNIST(num_img) # data
 d_optimizer=optim.Adam(D.parameters(),lr=0.0003)
 g_optimizer=optim.Adam(G.parameters(),lr=0.0003)
 '''
 交替训练的方式训练网络
 先训练判别器网络D再训练生成器网络G
 不同网络的训练次数是超参数
 也可以两个网络训练相同的次数
 这样就可以不用分别训练两个网络
 '''
 count=0
 #鉴别器D的训练,固定G的参数
 epoch = 100
 gepoch = 1
 for i in range(epoch):
 for (img, label) in trainloader:
  # num_img=img.size()[0]
  real_img=img.view(num_img,-1)#展开为28*28=784
  real_label=torch.ones(num_img)#真实label为1
  fake_label=torch.zeros(num_img)#假的label为0
 
  #compute loss of real_img
  real_out=D(real_img) #真实图片送入判别器D输出0~1
  d_loss_real=criterion(real_out,real_label)#得到loss
  real_scores=real_out#真实图片放入判别器输出越接近1越好
 
  #compute loss of fake_img
  z=torch.randn(num_img,z_dimension)#随机生成向量
  fake_img=G(z)#将向量放入生成网络G生成一张图片
  fake_out=D(fake_img)#判别器判断假的图片
  d_loss_fake=criterion(fake_out,fake_label)#假的图片的loss
  fake_scores=fake_out#假的图片放入判别器输出越接近0越好
 
  #D bp and optimize
  d_loss=d_loss_real+d_loss_fake
  d_optimizer.zero_grad() #判别器D的梯度归零
  d_loss.backward() #反向传播
  d_optimizer.step() #更新判别器D参数
 
  #生成器G的训练compute loss of fake_img
  for j in range(gepoch):
  fake_label = torch.ones(num_img) # 真实label为1
  z = torch.randn(num_img, z_dimension) # 随机生成向量
  fake_img = G(z) # 将向量放入生成网络G生成一张图片
  output = D(fake_img) # 经过判别器得到结果
  g_loss = criterion(output, fake_label)#得到假的图片与真实标签的loss
  #bp and optimize
  g_optimizer.zero_grad() #生成器G的梯度归零
  g_loss.backward() #反向传播
  g_optimizer.step()#更新生成器G参数
 print('Epoch [{}/{}], d_loss: {:.6f}, g_loss: {:.6f} '
   'D real: {:.6f}, D fake: {:.6f}'.format(
  i, epoch, d_loss.data[0], g_loss.data[0],
  real_scores.data.mean(), fake_scores.data.mean()))
 showimg(fake_img,count)
 # plt.show()
 count += 1

这里的图分别是 epoch为0、50、100、150、190的运行结果，可以看到图片中的数字并不单一

卷积版 Deep Convolutional Generative Adversarial Networks：

import torch
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
from torchvision import transforms
from torch import optim
import torch.nn as nn
import matplotlib.pyplot as plt
import numpy as np
from torch.autograd import Variable
 
import matplotlib.gridspec as gridspec
import os
 
def showimg(images,count):
 images=images.to('cpu')
 images=images.detach().numpy()
 images=images[[6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96]]
 images=255*(0.5*images+0.5)
 images = images.astype(np.uint8)
 grid_length=int(np.ceil(np.sqrt(images.shape[0])))
 plt.figure(figsize=(4,4))
 width = images.shape[2]
 gs = gridspec.GridSpec(grid_length,grid_length,wspace=0,hspace=0)
 print(images.shape)
 for i, img in enumerate(images):
 ax = plt.subplot(gs[i])
 ax.set_xticklabels([])
 ax.set_yticklabels([])
 ax.set_aspect('equal')
 plt.imshow(img.reshape(width,width),cmap = plt.cm.gray)
 plt.axis('off')
 plt.tight_layout()
# print('showing...')
 plt.tight_layout()
# plt.savefig('./GAN_Imaget/%d.png'%count, bbox_inches='tight')
 
def loadMNIST(batch_size): #MNIST图片的大小是28*28
 trans_img=transforms.Compose([transforms.ToTensor()])
 trainset=MNIST('./data',train=True,transform=trans_img,download=True)
 testset=MNIST('./data',train=False,transform=trans_img,download=True)
 # device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 trainloader=DataLoader(trainset,batch_size=batch_size,shuffle=True,num_workers=10)
 testloader = DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=10)
 return trainset,testset,trainloader,testloader
 
class discriminator(nn.Module):
 def __init__(self):
 super(discriminator,self).__init__()
 self.dis=nn.Sequential(
  nn.Conv2d(1,32,5,stride=1,padding=2),
  nn.LeakyReLU(0.2,True),
  nn.MaxPool2d((2,2)),
 
  nn.Conv2d(32,64,5,stride=1,padding=2),
  nn.LeakyReLU(0.2,True),
  nn.MaxPool2d((2,2))
 )
 self.fc=nn.Sequential(
  nn.Linear(7 * 7 * 64, 1024),
  nn.LeakyReLU(0.2, True),
  nn.Linear(1024, 1),
  nn.Sigmoid()
 )
 def forward(self, x):
 x=self.dis(x)
 x=x.view(x.size(0),-1)
 x=self.fc(x)
 return x
 
class generator(nn.Module):
 def __init__(self,input_size,num_feature):
 super(generator,self).__init__()
 self.fc=nn.Linear(input_size,num_feature) #1*56*56
 self.br=nn.Sequential(
  nn.BatchNorm2d(1),
  nn.ReLU(True)
 )
 self.gen=nn.Sequential(
  nn.Conv2d(1,50,3,stride=1,padding=1),
  nn.BatchNorm2d(50),
  nn.ReLU(True),
 
  nn.Conv2d(50,25,3,stride=1,padding=1),
  nn.BatchNorm2d(25),
  nn.ReLU(True),
 
  nn.Conv2d(25,1,2,stride=2),
  nn.Tanh()
 )
 def forward(self, x):
 x=self.fc(x)
 x=x.view(x.size(0),1,56,56)
 x=self.br(x)
 x=self.gen(x)
 return x
 
if __name__=="__main__":
 criterion=nn.BCELoss()
 num_img=100
 z_dimension=100
 D=discriminator()
 G=generator(z_dimension,3136) #1*56*56
 trainset, testset, trainloader, testloader = loadMNIST(num_img) # data
 D=D.cuda()
 G=G.cuda()
 d_optimizer=optim.Adam(D.parameters(),lr=0.0003)
 g_optimizer=optim.Adam(G.parameters(),lr=0.0003)
 '''
 交替训练的方式训练网络
 先训练判别器网络D再训练生成器网络G
 不同网络的训练次数是超参数
 也可以两个网络训练相同的次数，
 这样就可以不用分别训练两个网络
 '''
 count=0
 #鉴别器D的训练,固定G的参数
 epoch = 100
 gepoch = 1
 for i in range(epoch):
 for (img, label) in trainloader:
  # num_img=img.size()[0]
  img=Variable(img).cuda()
  real_label=Variable(torch.ones(num_img)).cuda()#真实label为1
  fake_label=Variable(torch.zeros(num_img)).cuda()#假的label为0
 
  #compute loss of real_img
  real_out=D(img) #真实图片送入判别器D输出0~1
  d_loss_real=criterion(real_out,real_label)#得到loss
  real_scores=real_out#真实图片放入判别器输出越接近1越好
 
  #compute loss of fake_img
  z=Variable(torch.randn(num_img,z_dimension)).cuda()#随机生成向量
  fake_img=G(z)#将向量放入生成网络G生成一张图片
  fake_out=D(fake_img)#判别器判断假的图片
  d_loss_fake=criterion(fake_out,fake_label)#假的图片的loss
  fake_scores=fake_out#假的图片放入判别器输出越接近0越好
 
  #D bp and optimize
  d_loss=d_loss_real+d_loss_fake
  d_optimizer.zero_grad() #判别器D的梯度归零
  d_loss.backward() #反向传播
  d_optimizer.step() #更新判别器D参数
 
  #生成器G的训练compute loss of fake_img
  for j in range(gepoch):
  fake_label = Variable(torch.ones(num_img)).cuda() # 真实label为1
  z = Variable(torch.randn(num_img, z_dimension)).cuda() # 随机生成向量
  fake_img = G(z) # 将向量放入生成网络G生成一张图片
  output = D(fake_img) # 经过判别器得到结果
  g_loss = criterion(output, fake_label)#得到假的图片与真实标签的loss
  #bp and optimize
  g_optimizer.zero_grad() #生成器G的梯度归零
  g_loss.backward() #反向传播
  g_optimizer.step()#更新生成器G参数
  # if ((i+1)%1000==0):
  # print("[%d/%d] GLoss: %.5f" % (i + 1, gepoch, g_loss.data[0]))
 print('Epoch [{}/{}], d_loss: {:.6f}, g_loss: {:.6f} '
   'D real: {:.6f}, D fake: {:.6f}'.format(
  i, epoch, d_loss.data[0], g_loss.data[0],
  real_scores.data.mean(), fake_scores.data.mean()))
 showimg(fake_img,count)
 plt.show()
 count += 1

这里的gepoch设置为1，运行39次的结果是：