GAN训练结果D损失: nan，访问: 50% G损失: nan

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

# 假设Generator和Discriminator已经定义
generator = Generator()
discriminator = Discriminator()

# 权重初始化
generator.apply(weights_init)
discriminator.apply(weights_init)

# 优化器
optimizer_G = optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))
optimizer_D = optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))

# 数据加载
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])
dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
dataloader = DataLoader(dataset, batch_size=64, shuffle=True)

# 训练循环
for epoch in range(num_epochs):
    for i, (real_images, _) in enumerate(dataloader):
        # 训练判别器
        optimizer_D.zero_grad()
        real_labels = torch.ones(real_images.size(0), 1)
        fake_labels = torch.zeros(real_images.size(0), 1)
        
        real_outputs = discriminator(real_images)
        d_loss_real = criterion(real_outputs, real_labels)
        d_loss_real.backward()
        
        z = torch.randn(real_images.size(0), latent_dim)
        fake_images = generator(z)
        fake_outputs = discriminator(fake_images.detach())
        d_loss_fake = criterion(fake_outputs, 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_outputs = discriminator(fake_images)
        g_loss = criterion(fake_outputs, real_labels)
        g_loss.backward()
        optimizer_G.step()
        
        # 打印损失
        if (i+1) % 100 == 0:
            print(f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{len(dataloader)}], d_loss: {d_loss.item():.4f}, g_loss: {g_loss.item():.4f}')