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Pytorch使用MNIST數(shù)據(jù)集實現(xiàn)基礎(chǔ)GAN和DCGAN詳解

更新時間：2020年01月10日 10:06:33 作者：shiheyingzhe

今天小編就為大家分享一篇Pytorch使用MNIST數(shù)據(jù)集實現(xiàn)基礎(chǔ)GAN和DCGAN詳解，具有很好的參考價值，希望對大家有所幫助。一起跟隨小編過來看看吧

原始生成對抗網(wǎng)絡(luò)Generative Adversarial Networks GAN包含生成器Generator和判別器Discriminator，數(shù)據(jù)有真實數(shù)據(jù)groundtruth，還有需要網(wǎng)絡(luò)生成的“fake”數(shù)據(jù)，目的是網(wǎng)絡(luò)生成的fake數(shù)據(jù)可以“騙過”判別器，讓判別器認不出來，就是讓判別器分不清進入的數(shù)據(jù)是真實數(shù)據(jù)還是fake數(shù)據(jù)?？偟膩碚f是：判別器區(qū)分真實數(shù)據(jù)和fake數(shù)據(jù)的能力越強越好；生成器生成的數(shù)據(jù)騙過判別器的能力越強越好，這個是矛盾的，所以只能交替訓(xùn)練網(wǎng)絡(luò)。

需要搭建生成器網(wǎng)絡(luò)和判別器網(wǎng)絡(luò)，訓(xùn)練的時候交替訓(xùn)練。

首先訓(xùn)練判別器的參數(shù)，固定生成器的參數(shù)，讓判別器判斷生成器生成的數(shù)據(jù)，讓其和0接近，讓判別器判斷真實數(shù)據(jù)，讓其和1接近；

接著訓(xùn)練生成器的參數(shù)，固定判別器的參數(shù)，讓生成器生成的數(shù)據(jù)進入判別器，讓判斷結(jié)果和1接近。生成器生成數(shù)據(jù)需要給定隨機初始值

線性版：

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圖片的大小是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)
 '''
 交替訓(xùn)練的方式訓(xùn)練網(wǎng)絡(luò)
 先訓(xùn)練判別器網(wǎng)絡(luò)D再訓(xùn)練生成器網(wǎng)絡(luò)G
 不同網(wǎng)絡(luò)的訓(xùn)練次數(shù)是超參數(shù)
 也可以兩個網(wǎng)絡(luò)訓(xùn)練相同的次數(shù)
 這樣就可以不用分別訓(xùn)練兩個網(wǎng)絡(luò)
 '''
 count=0
 #鑒別器D的訓(xùn)練,固定G的參數(shù)
 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)#展開為28*28=784
  real_label=torch.ones(num_img)#真實label為1
  fake_label=torch.zeros(num_img)#假的label為0
 
  #compute loss of real_img
  real_out=D(real_img) #真實圖片送入判別器D輸出0~1
  d_loss_real=criterion(real_out,real_label)#得到loss
  real_scores=real_out#真實圖片放入判別器輸出越接近1越好
 
  #compute loss of fake_img
  z=torch.randn(num_img,z_dimension)#隨機生成向量
  fake_img=G(z)#將向量放入生成網(wǎng)絡(luò)G生成一張圖片
  fake_out=D(fake_img)#判別器判斷假的圖片
  d_loss_fake=criterion(fake_out,fake_label)#假的圖片的loss
  fake_scores=fake_out#假的圖片放入判別器輸出越接近0越好
 
  #D bp and optimize
  d_loss=d_loss_real+d_loss_fake
  d_optimizer.zero_grad() #判別器D的梯度歸零
  d_loss.backward() #反向傳播
  d_optimizer.step() #更新判別器D參數(shù)
 
  #生成器G的訓(xùn)練compute loss of fake_img
  for j in range(gepoch):
  fake_label = torch.ones(num_img) # 真實label為1
  z = torch.randn(num_img, z_dimension) # 隨機生成向量
  fake_img = G(z) # 將向量放入生成網(wǎng)絡(luò)G生成一張圖片
  output = D(fake_img) # 經(jīng)過判別器得到結(jié)果
  g_loss = criterion(output, fake_label)#得到假的圖片與真實標(biāo)簽的loss
  #bp and optimize
  g_optimizer.zero_grad() #生成器G的梯度歸零
  g_loss.backward() #反向傳播
  g_optimizer.step()#更新生成器G參數(shù)
 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

這里的圖分別是 epoch為0、50、100、150、190的運行結(jié)果，可以看到圖片中的數(shù)字并不單一

卷積版 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圖片的大小是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)
 '''
 交替訓(xùn)練的方式訓(xùn)練網(wǎng)絡(luò)
 先訓(xùn)練判別器網(wǎng)絡(luò)D再訓(xùn)練生成器網(wǎng)絡(luò)G
 不同網(wǎng)絡(luò)的訓(xùn)練次數(shù)是超參數(shù)
 也可以兩個網(wǎng)絡(luò)訓(xùn)練相同的次數(shù)，
 這樣就可以不用分別訓(xùn)練兩個網(wǎng)絡(luò)
 '''
 count=0
 #鑒別器D的訓(xùn)練,固定G的參數(shù)
 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()#真實label為1
  fake_label=Variable(torch.zeros(num_img)).cuda()#假的label為0
 
  #compute loss of real_img
  real_out=D(img) #真實圖片送入判別器D輸出0~1
  d_loss_real=criterion(real_out,real_label)#得到loss
  real_scores=real_out#真實圖片放入判別器輸出越接近1越好
 
  #compute loss of fake_img
  z=Variable(torch.randn(num_img,z_dimension)).cuda()#隨機生成向量
  fake_img=G(z)#將向量放入生成網(wǎng)絡(luò)G生成一張圖片
  fake_out=D(fake_img)#判別器判斷假的圖片
  d_loss_fake=criterion(fake_out,fake_label)#假的圖片的loss
  fake_scores=fake_out#假的圖片放入判別器輸出越接近0越好
 
  #D bp and optimize
  d_loss=d_loss_real+d_loss_fake
  d_optimizer.zero_grad() #判別器D的梯度歸零
  d_loss.backward() #反向傳播
  d_optimizer.step() #更新判別器D參數(shù)
 
  #生成器G的訓(xùn)練compute loss of fake_img
  for j in range(gepoch):
  fake_label = Variable(torch.ones(num_img)).cuda() # 真實label為1
  z = Variable(torch.randn(num_img, z_dimension)).cuda() # 隨機生成向量
  fake_img = G(z) # 將向量放入生成網(wǎng)絡(luò)G生成一張圖片
  output = D(fake_img) # 經(jīng)過判別器得到結(jié)果
  g_loss = criterion(output, fake_label)#得到假的圖片與真實標(biāo)簽的loss
  #bp and optimize
  g_optimizer.zero_grad() #生成器G的梯度歸零
  g_loss.backward() #反向傳播
  g_optimizer.step()#更新生成器G參數(shù)
  # 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

這里的gepoch設(shè)置為1，運行39次的結(jié)果是：