帮我写一个用pytorch版本的条件GAN网络

好的，我可以回答这个问题。以下是一个使用 PyTorch 实现的条件 GAN 网络的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
from torchvision.transforms import transforms
from torchvision.utils import save_image

# 定义生成器
class Generator(nn.Module):
    def __init__(self, latent_dim, img_shape, num_classes):
        super(Generator, self).__init__()

        self.label_emb = nn.Embedding(num_classes, num_classes)
        self.model = nn.Sequential(
            nn.Linear(latent_dim + num_classes, 128),
            nn.BatchNorm1d(128, .8),
            nn.LeakyReLU(.2, inplace=True),
            nn.Linear(128, 256),
            nn.BatchNorm1d(256, .8),
            nn.LeakyReLU(.2, inplace=True),
            nn.Linear(256, 512),
            nn.BatchNorm1d(512, .8),
            nn.LeakyReLU(.2, inplace=True),
            nn.Linear(512, int(torch.prod(torch.tensor(img_shape)))),
            nn.Tanh()
        )

    def forward(self, noise, labels):
        gen_input = torch.cat((self.label_emb(labels), noise), -1)
        img = self.model(gen_input)
        img = img.view(img.size(), *img_shape)
        return img

# 定义判别器
class Discriminator(nn.Module):
    def __init__(self, img_shape, num_classes):
        super(Discriminator, self).__init__()

        self.label_emb = nn.Embedding(num_classes, num_classes)
        self.model = nn.Sequential(
            nn.Linear(num_classes + int(torch.prod(torch.tensor(img_shape))), 512),
            nn.LeakyReLU(.2, inplace=True),
            nn.Linear(512, 256),
            nn.LeakyReLU(.2, inplace=True),
            nn.Linear(256, 1),
            nn.Sigmoid(),
        )

    def forward(self, img, labels):
        d_in = img.view(img.size(), -1)
        d_in = torch.cat((d_in, self.label_emb(labels)), -1)
        validity = self.model(d_in)
        return validity

# 定义训练函数
def train(generator, discriminator, dataloader, num_epochs, latent_dim, num_classes, device):
    adversarial_loss = nn.BCELoss()

    optimizer_G = optim.Adam(generator.parameters(), lr=.0002, betas=(.5, .999))
    optimizer_D = optim.Adam(discriminator.parameters(), lr=.0002, betas=(.5, .999))

    for epoch in range(num_epochs):
        for i, (imgs, labels) in enumerate(dataloader):

            # 训练判别器
            optimizer_D.zero_grad()

            real_imgs = imgs.to(device)
            labels = labels.to(device)
            batch_size = real_imgs.size()

            valid = torch.ones(batch_size, 1).to(device)
            fake = torch.zeros(batch_size, 1).to(device)

            z = torch.randn(batch_size, latent_dim).to(device)
            gen_labels = torch.randint(, num_classes, (batch_size,)).to(device)
            gen_imgs = generator(z, gen_labels)

            real_loss = adversarial_loss(discriminator(real_imgs, labels), valid)
            fake_loss = adversarial_loss(discriminator(gen_imgs.detach(), gen_labels), fake)
            d_loss = (real_loss + fake_loss) / 2

            d_loss.backward()
            optimizer_D.step()

            # 训练生成器
            optimizer_G.zero_grad()

            z = torch.randn(batch_size, latent_dim).to(device)
            gen_labels = torch.randint(, num_classes, (batch_size,)).to(device)
            gen_imgs = generator(z, gen_labels)

            g_loss = adversarial_loss(discriminator(gen_imgs, gen_labels), valid)

            g_loss.backward()
            optimizer_G.step()

            # 打印训练信息
            batches_done = epoch * len(dataloader) + i
            if batches_done % 400 == :
                print(
                    "[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]"
                    % (epoch, num_epochs, i, len(dataloader), d_loss.item(), g_loss.item())
                )

        # 保存生成的图像
        if epoch % 5 == :
            save_image(gen_imgs.data[:25], "images/%d.png" % epoch, nrow=5, normalize=True)

# 设置超参数
latent_dim = 100
img_shape = (1, 28, 28)
num_classes = 10
num_epochs = 200
batch_size = 64
lr = .0002
b1 = .5
b2 = .999

# 加载数据集
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((.5,), (.5,))
])
dataset = MNIST(".", train=True, download=True, transform=transform)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

# 初始化模型和优化器
generator = Generator(latent_dim, img_shape, num_classes)
discriminator = Discriminator(img_shape, num_classes)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
generator.to(device)
discriminator.to(device)

# 训练模型
train(generator, discriminator, dataloader, num_epochs, latent_dim, num_classes, device)

这个示例代码实现了一个基于 MNIST 数据集的条件 GAN 网络，可以生成手写数字图像。如果你想要使用其他数据集，可以根据需要修改代码。