生成式对抗网络在元生成模型中的应用

    import torch
    import torch.nn as nn
    import torch.optim as optim
    from torch.autograd import Variable
    
    # 生成器网络
    class Generator(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(Generator, self).__init__()
        self.map1 = nn.Linear(input_size, hidden_size)
        self.map2 = nn.Linear(hidden_size, output_size)
        self.activation = nn.ReLU()
    
    def forward(self, x):
        x = self.map1(x)
        x = self.activation(x)
        x = self.map2(x)
        return x
    
    # 判别器网络
    class Discriminator(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(Discriminator, self).__init__()
        self.map1 = nn.Linear(input_size, hidden_size)
        self.map2 = nn.Linear(hidden_size, output_size)
        self.activation = nn.Sigmoid()
    
    def forward(self, x):
        x = self.map1(x)
        x = self.activation(x)
        x = self.map2(x)
        return x
    
    # 元生成对抗网络训练
    def meta_gan_train(train_loader, test_loader, device):
    # 初始化生成器和判别器
    G = Generator(input_size=100, hidden_size=256, output_size=784).to(device)
    D = Discriminator(input_size=784, hidden_size=256, output_size=1).to(device)
    
    # 定义优化器
    G_optimizer = optim.Adam(G.parameters(), lr=0.0002)
    D_optimizer = optim.Adam(D.parameters(), lr=0.0002)
    
    # 训练过程
    for epoch in range(100):
        # 在meta-training集上进行对抗训练
        for i, (real_samples, _) in enumerate(train_loader):
            real_samples = real_samples.view(-1, 784).to(device)
    
            # 训练判别器
            D_optimizer.zero_grad()
            real_outputs = D(real_samples)
            noise = torch.randn(real_samples.size(0), 100).to(device)
            fake_samples = G(noise)
            fake_outputs = D(fake_samples.detach())
            d_loss = -torch.mean(torch.log(real_outputs) + torch.log(1 - fake_outputs))
            d_loss.backward()
            D_optimizer.step()
    
            # 训练生成器
            G_optimizer.zero_grad()
            fake_outputs = D(fake_samples)
            g_loss = -torch.mean(torch.log(fake_outputs))
            g_loss.backward()
            G_optimizer.step()
    
        # 在新任务(test_loader)上进行fine-tuning
        for i, (real_samples, _) in enumerate(test_loader):
            real_samples = real_samples.view(-1, 784).to(device)
            noise = torch.randn(real_samples.size(0), 100).to(device)
            fake_samples = G(noise)
            # 对G和D进行fine-tuning
    
    return G, D
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读