CausalVAE的具体代码

Causal Variational Autoencoder (CausalVAE)是一种结合了无监督学习和因果推断的技术，在处理有因果结构的数据时很有用。这种模型尝试从观测数据中恢复潜在的因果变量。以下是一个简单的CausalVAE的基本框架，使用PyTorch作为例子：

import torch
import torch.nn as nn

class Encoder(nn.Module):
    # 假设输入大小为( batch_size, input_dim )
    def __init__(self, input_dim, hidden_dim, z_dim):
        super(Encoder, self).__init__()
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.fc_mu = nn.Linear(hidden_dim, z_dim)
        self.fc_logvar = nn.Linear(hidden_dim, z_dim)

    def forward(self, x):
        h = torch.relu(self.fc1(x))
        mu = self.fc_mu(h)
        logvar = self.fc_logvar(h)
        return mu, logvar

class Decoder(nn.Module):
    def __init__(self, z_dim, output_dim, hidden_dim):
        super(Decoder, self).__init__()
        self.fc1 = nn.Linear(z_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, output_dim)

    def forward(self, z):
        h = torch.relu(self.fc1(z))
        out = torch.sigmoid(self.fc2(h))
        return out

class CausalVAE(nn.Module):
    def __init__(self, encoder, decoder):
        super(CausalVAE, self).__init__()
        self.encoder = encoder
        self.decoder = decoder

    def forward(self, x):
        mu, logvar = self.encoder(x)
        std = torch.exp(0.5 * logvar)
        eps = torch.randn_like(std)
        z = mu + eps * std
        recon_x = self.decoder(z)
        return recon_x, mu, logvar

这只是一个基础模板，实际应用中可能还需要处理更复杂的条件和因果图。