DCGAN应用的简单示例（基于MNIST手写数据集的训练）

1. 说明

	已经是第三篇了，前面说了那么多，这一篇来实战，采用的是TENSORFLOW。使用DCGAN对MNIST数据集进行训练的简单示例。注意，篇幅有限，代码中省略了一些具体的实现细节，与原理一一对应。

2. 代码

import torchvision
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms, datasets

# 定义生成器
class Generator(nn.Module):
    # 省略具体实现

# 定义判别器
class Discriminator(nn.Module):
    # 省略具体实现

# 加载MNIST数据集
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])
trainset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)

# 初始化模型和优化器
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
netG = Generator().to(device)
netD = Discriminator().to(device)
criterion = nn.BCELoss()
optimizerG = optim.Adam(netG.parameters(), lr=0.0002, betas=(0.5, 0.999))
optimizerD = optim.Adam(netD.parameters(), lr=0.0002, betas=(0.5, 0.999))

# 训练模型
for epoch in range(num_epochs):
    for i, data in enumerate(trainloader, 0):
        real, _ = data
        real = real.to(device)
        # 训练判别器
        optimizerD.zero_grad()
        fake = netG(torch.randn(batch_size, nz, 1, 1, device=device))
        real_label = torch.full((batch_size,), 1, device=device)
        fake_label = torch.full((batch_size,), 0, device=device)
        real_loss = criterion(netD(real), real_label)
        fake_loss = criterion(netD(fake.detach()), fake_label)
        d_loss = real_loss + fake_loss
        d_loss.backward()
        optimizerD.step()
        # 训练生成器
        optimizerG.zero_grad()
        fake_label.fill_(1)
        g_loss = criterion(netD(fake), fake_label)
        g_loss.backward()
        optimizerG.step()
        # 打印训练信息
        if i % 100 == 0:
            print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f'
                  % (epoch, num_epochs, i, len(trainloader),
                     d_loss.item(), g_loss.item()))

# 展示生成的图像
noise = torch.randn(batch_size, nz, 1, 1, device=device)
fake = netG(noise)
imshow(torchvision.utils.make_grid(fake.cpu().detach()))