对抗正则化变分图自编码器 代码

以下是使用PyTorch实现对抗正则化变分图自编码器（Adversarial Regularization Variational Graph Autoencoder）的代码：

import torch
import torch.nn as nn
import torch.nn.functional as F


class GAE(nn.Module):
    def __init__(self, n_feat, n_hid, n_latent):
        super(GAE, self).__init__()
        self.n_latent = n_latent

        self.encoder = nn.Sequential(
            nn.Linear(n_feat, n_hid),
            nn.ReLU(),
            nn.Linear(n_hid, n_hid),
            nn.ReLU(),
            nn.Linear(n_hid, n_latent * 2)
        )

        self.decoder = nn.Sequential(
            nn.Linear(n_latent, n_hid),
            nn.ReLU(),
            nn.Linear(n_hid, n_hid),
            nn.ReLU(),
            nn.Linear(n_hid, n_feat)
        )

    def encode(self, x):
        h = self.encoder(x)
        mu, log_var = torch.chunk(h, 2, dim=-1)
        return mu, log_var

    def decode(self, z):
        return self.decoder(z)

    def reparameterize(self, mu, log_var):
        std = torch.exp(0.5 * log_var)
        eps = torch.randn_like(std)
        return mu + eps * std

    def forward(self, x):
        mu, log_var = self.encode(x)
        z = self.reparameterize(mu, log_var)
        x_hat = self.decode(z)
        return x_hat, mu, log_var

    def loss_function(self, x, x_hat, mu, log_var):
        recon_loss = F.mse_loss(x_hat, x, reduction='mean')
        kld_loss = -0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp(), dim=-1).mean()
        return recon_loss + kld_loss


class Discriminator(nn.Module):
    def __init__(self, n_latent, n_hid):
        super(Discriminator, self).__init__()

        self.model = nn.Sequential(
            nn.Linear(n_latent, n_hid),
            nn.ReLU(),
            nn.Linear(n_hid, 1),
            nn.Sigmoid()
        )

    def forward(self, z):
        return self.model(z)


class ARVGA(nn.Module):
    def __init__(self, n_feat, n_hid, n_latent, n_hid_d):
        super(ARVGA, self).__init__()

        self.gae = GAE(n_feat, n_hid, n_latent)
        self.discriminator = Discriminator(n_latent, n_hid_d)

    def forward(self, x):
        x_hat, mu, log_var = self.gae(x)
        z = self.gae.reparameterize(mu, log_var)
        return x_hat, mu, log_var, z

    def loss_function(self, x, x_hat, mu, log_var, z):
        recon_loss = F.mse_loss(x_hat, x, reduction='mean')
        kld_loss = -0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp(), dim=-1).mean()
        g_loss = recon_loss + kld_loss

        d_real = self.discriminator(torch.randn(z.shape).to(z.device))
        d_fake = self.discriminator(z.detach())
        d_loss = -torch.mean(torch.log(d_real + 1e-8) + torch.log(1 - d_fake + 1e-8))

        return g_loss, d_loss

其中，`GAE`类定义了变分图自编码器（Variational Graph Autoencoder），包括编码器和解码器，以及相关的函数，如重参数化函数等。`Discriminator`类定义了对抗正则化中的判别器。`ARVGA`类整合了变分图自编码器和判别器，并定义了前向传递和损失函数。

在训练模型时，可以使用以下代码：

model = ARVGA(n_feat, n_hid, n_latent, n_hid_d)
optimizer_g = torch.optim.Adam(model.gae.parameters(), lr=lr)
optimizer_d = torch.optim.Adam(model.discriminator.parameters(), lr=lr)
for epoch in range(n_epochs):
    for batch in data_loader:
        x = batch.to(device)
        x_hat, mu, log_var, z = model(x)

        g_loss, d_loss = model.loss_function(x, x_hat, mu, log_var, z)
        optimizer_g.zero_grad()
        g_loss.backward(retain_graph=True)
        optimizer_g.step()

        optimizer_d.zero_grad()
        d_loss.backward()
        optimizer_d.step()

其中，`data_loader`是一个`torch.utils.data.DataLoader`对象，用于加载训练数据。`n_epochs`是训练轮数，`lr`是学习率。调用`model.loss_function()`可以得到生成器和判别器的损失，并分别使用对应的优化器进行反向传播和参数更新。