CGAN的全拼是Conditional Generative Adversarial Networks，条件生成对抗网络，在初始GAN的基础上增加了图片的相应信息。

网站的建设成都创新互联专注网站定制,经验丰富,不做模板,主营网站定制开发.小程序定制开发,H5页面制作!给你焕然一新的设计体验!已为成都火锅店设计等企业提供专业服务。

这里用传统的卷积方式实现CGAN。

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 pickle
import copy
 
import matplotlib.gridspec as gridspec
import os
 
def save_model(model, filename): #保存为CPU中可以打开的模型
 state = model.state_dict()
 x=state.copy()
 for key in x: 
  x[key] = x[key].clone().cpu()
 torch.save(x, filename)
 
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)
 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()
#  plt.tight_layout()
 plt.savefig(r'./CGAN/images/%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, 10),
   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=110
 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 = 119
 gepoch = 1
 for i in range(epoch):
  for (img, label) in trainloader:
   labels_onehot = np.zeros((num_img,10))
   labels_onehot[np.arange(num_img),label.numpy()]=1
#    img=img.view(num_img,-1)
#    img=np.concatenate((img.numpy(),labels_onehot))
#    img=torch.from_numpy(img)
   img=Variable(img).cuda()
   real_label=Variable(torch.from_numpy(labels_onehot).float()).cuda()#真实label为1
   fake_label=Variable(torch.zeros(num_img,10)).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):
    z =torch.randn(num_img, 100) # 随机生成向量
    z=np.concatenate((z.numpy(),labels_onehot),axis=1)
    z=Variable(torch.from_numpy(z).float()).cuda()
    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, real_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
    temp=real_label
  if (i%10==0) and (i!=0):
   print(i)
   torch.save(G.state_dict(),r'./CGAN/Generator_cuda_%d.pkl'%i)
   torch.save(D.state_dict(), r'./CGAN/Discriminator_cuda_%d.pkl' % i)
   save_model(G, r'./CGAN/Generator_cpu_%d.pkl'%i) #保存为CPU中可以打开的模型
   save_model(D, r'./CGAN/Discriminator_cpu_%d.pkl'%i) #保存为CPU中可以打开的模型
  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()))
  temp=temp.to('cpu')
  _,x=torch.max(temp,1)
  x=x.numpy()
  print(x[[6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96]])
  showimg(fake_img,count)
  plt.show()
  count += 1

另外有需要云服务器可以了解下创新互联scvps.cn，海内外云服务器15元起步，三天无理由+7*72小时售后在线，公司持有idc许可证，提供“云服务器、裸金属服务器、高防服务器、香港服务器、美国服务器、虚拟主机、免备案服务器”等云主机租用服务以及企业上云的综合解决方案，具有“安全稳定、简单易用、服务可用性高、性价比高”等特点与优势，专为企业上云打造定制，能够满足用户丰富、多元化的应用场景需求。

网站栏目：Pytorch使用MNIST数据集实现CGAN和生成指定的数字方式-创新互联
文章路径：http://scgulin.cn/article/eiedj.html