找出DCGAN中与神经网络训练过程的五个步骤相对应的代码。

DCGAN中与神经网络训练过程的五个步骤相对应的代码如下：

1. 初始化生成器和判别器的权重和偏置项：

def initialize_weights(model):
    for m in model.modules():
        if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d, nn.Linear)):
            nn.init.normal_(m.weight, 0.0, 0.02)
            nn.init.constant_(m.bias, 0)

2. 输入真实图像并将其传入判别器中进行前向传播：

real_images = data.to(device)
real_labels = torch.ones(batch_size, 1).to(device)
outputs = discriminator(real_images).squeeze()
real_loss = criterion(outputs, real_labels)

3. 使用生成器生成假图像，并将其传入判别器中进行前向传播：

z = torch.randn(batch_size, nz, 1, 1, device=device)
fake_images = generator(z)
fake_labels = torch.zeros(batch_size, 1).to(device)
outputs = discriminator(fake_images.detach()).squeeze()
fake_loss = criterion(outputs, fake_labels)

4. 计算判别器的损失函数并进行反向传播：

d_loss = real_loss + fake_loss
d_loss.backward()
optimizerD.step()

5. 计算生成器的损失函数并进行反向传播：

z = torch.randn(batch_size, nz, 1, 1, device=device)
fake_images = generator(z)
outputs = discriminator(fake_images).squeeze()
g_loss = criterion(outputs, real_labels)
g_loss.backward()
optimizerG.step()