pytorch如何搭建gan网络

在PyTorch中实现GAN网络通常需要定义两个模型：生成器和判别器。以下是一个简单的GAN网络的示例代码：

import torch
import torch.nn as nn

# 定义生成器模型
class Generator(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(Generator, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, output_size)
        self.relu = nn.ReLU()
        self.tanh = nn.Tanh()

    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.tanh(self.fc3(x))
        return x

# 定义判别器模型
class Discriminator(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(Discriminator, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, output_size)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.sigmoid(self.fc1(x))
        x = self.sigmoid(self.fc2(x))
        x = self.sigmoid(self.fc3(x))
        return x

# 定义损失函数和优化器
criterion = nn.BCELoss()
generator = Generator(input_size, hidden_size, output_size)
discriminator = Discriminator(input_size, hidden_size, output_size)
g_optimizer = torch.optim.Adam(generator.parameters(), lr=learning_rate)
d_optimizer = torch.optim.Adam(discriminator.parameters(), lr=learning_rate)

# 训练GAN网络
for epoch in range(num_epochs):
    for i, images in enumerate(train_loader):
        # 训练生成器
        z = torch.randn(batch_size, input_size)
        fake_images = generator(z)
        d_fake = discriminator(fake_images)
        g_loss = criterion(d_fake, torch.ones_like(d_fake))
        g_optimizer.zero_grad()
        g_loss.backward()
        g_optimizer.step()

        # 训练判别器
        real_images = images.view(-1, input_size)
        d_real = discriminator(real_images)
        d_loss_real = criterion(d_real, torch.ones_like(d_real))
        d_loss_fake = criterion(d_fake, torch.zeros_like(d_fake))
        d_loss = d_loss_real + d_loss_fake
        d_optimizer.zero_grad()
        d_loss.backward()
        d_optimizer.step()

在训练过程中，生成器网络将随机噪声作为输入，生成虚假图像，而判别器网络将真实图像和虚假图像作为输入，尝试区分它们的真伪。损失函数的目标是最小化生成器输出的虚假图像与真实图像之间的差异，并最大化判别器对真实和虚假图像的分类准确性。通过交替训练生成器和判别器，模型将逐渐学会生成更真实的图像。