写一段pytorch条件生成对抗网络代码

好的，以下是一个简单的PyTorch条件生成对抗网络的代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable

# 定义生成器网络
class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.label_emb = nn.Embedding(num_classes, num_classes)
        self.fc = nn.Sequential(
            nn.Linear(latent_dim + num_classes, 128),
            nn.BatchNorm1d(128),
            nn.ReLU(True),
            nn.Linear(128, 256),
            nn.BatchNorm1d(256),
            nn.ReLU(True),
            nn.Linear(256, 512),
            nn.BatchNorm1d(512),
            nn.ReLU(True),
            nn.Linear(512, 784),
            nn.Tanh()
        )

    def forward(self, noise, labels):
        gen_input = torch.cat((self.label_emb(labels), noise), -1)
        out = self.fc(gen_input)
        out = out.view(out.size(0), 1, 28, 28)
        return out

# 定义判别器网络
class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.label_embedding = nn.Embedding(num_classes, num_classes)
        self.fc = nn.Sequential(
            nn.Linear(num_classes + 784, 512),
            nn.ReLU(),
            nn.Linear(512, 256),
            nn.ReLU(),
            nn.Linear(256, 1),
            nn.Sigmoid()
        )

    def forward(self, img, labels):
        d_in = torch.cat((img.view(img.size(0), -1), self.label_embedding(labels)), -1)
        out = self.fc(d_in)
        return out

# 定义一些超参数
num_epochs = 200
batch_size = 100
learning_rate = 0.0002

# 创建网络实例和优化器
generator = Generator()
discriminator = Discriminator()
optimizer_G = optim.Adam(generator.parameters(), lr=learning_rate)
optimizer_D = optim.Adam(discriminator.parameters(), lr=learning_rate)

# 训练循环
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(data_loader):
        # 准备数据
        real_images = Variable(images)
        labels = Variable(labels)
        real_labels = Variable(torch.ones(batch_size))
        fake_labels = Variable(torch.zeros(batch_size))
        noise = Variable(torch.randn(batch_size, latent_dim))

        # 训练判别器
        optimizer_D.zero_grad()
        real_output = discriminator(real_images, labels)
        d_loss_real = criterion(real_output, real_labels)
        d_loss_real.backward()
        fake_images = generator(noise, labels)
        fake_output = discriminator(fake_images, labels)
        d_loss_fake = criterion(fake_output, fake_labels)
        d_loss_fake.backward()
        d_loss = d_loss_real + d_loss_fake
        optimizer_D.step()

        # 训练生成器
        optimizer_G.zero_grad()
        fake_images = generator(noise, labels)
        fake_output = discriminator(fake_images, labels)
        g_loss = criterion(fake_output, real_labels)
        g_loss.backward()
        optimizer_G.step()

注意这只是一个简单的示例，实际应用中可能需要进行更多的调整和优化。