pytorch实现SDNE代码

SDNE（Stacked Denoising Autoencoder）是一种用于网络嵌入的有效方法。下面是使用 PyTorch 实现 SDNE 的示例代码：

import torch
import torch.nn as nn

class SDNE(nn.Module):
    def __init__(self, input_dim, hidden_dims, output_dim, dropout=0.5):
        super(SDNE, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.hidden_dims = hidden_dims
        self.dropout = dropout
        
        self.encoder_layers = nn.ModuleList()
        self.decoder_layers = nn.ModuleList()
        
        # Add encoder layers
        in_dim = self.input_dim
        for i, h_dim in enumerate(self.hidden_dims):
            self.encoder_layers.append(nn.Linear(in_dim, h_dim))
            self.encoder_layers.append(nn.ReLU())
            self.encoder_layers.append(nn.Dropout(p=self.dropout))
            in_dim = h_dim
        # Add final encoder layer
        self.encoder_layers.append(nn.Linear(in_dim, self.output_dim))
        
        # Add decoder layers
        in_dim = self.output_dim
        for i, h_dim in enumerate(reversed(self.hidden_dims)):
            self.decoder_layers.append(nn.Linear(in_dim, h_dim))
            self.decoder_layers.append(nn.ReLU())
            self.decoder_layers.append(nn.Dropout(p=self.dropout))
            in_dim = h_dim
        # Add final decoder layer
        self.decoder_layers.append(nn.Linear(in_dim, self.input_dim))
    
    def forward(self, x):
        # Encode input
        encoded = x
        for layer in self.encoder_layers:
            encoded = layer(encoded)
        
        # Decode encoded input
        decoded = encoded
        for layer in self.decoder_layers:
            decoded = layer(decoded)
        
        return encoded, decoded

在这段代码中，我们定义了一个 PyTorch 模型 `SDNE`，它继承自 `nn.Module` 类。我们在构造函数中定义了输入维度、隐藏层维度列表、输出维度和 dropout 的概率。