pytorch LSTM编解码

以下是使用PyTorch实现LSTM编解码的示例代码：

import torch
import torch.nn as nn

class Encoder(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers=1):
        super(Encoder, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)

    def forward(self, x):
        # 初始化隐状态和细胞状态
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)

        # 前向传播LSTM
        out, (hn, cn) = self.lstm(x, (h0, c0))
        return out, hn, cn

class Decoder(nn.Module):
    def __init__(self, input_size, hidden_size, output_size, num_layers=1):
        super(Decoder, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x, hn, cn):
        # 初始化隐状态和细胞状态
        h0 = hn
        c0 = cn

        # 前向传播LSTM
        out, (hn, cn) = self.lstm(x, (h0, c0))
        out = self.fc(out)
        return out, hn, cn

class Seq2Seq(nn.Module):
    def __init__(self, input_size, hidden_size, output_size, num_layers=1):
        super(Seq2Seq, self).__init__()
        self.encoder = Encoder(input_size, hidden_size, num_layers)
        self.decoder = Decoder(input_size, hidden_size, output_size, num_layers)

    def forward(self, x, y):
        # 编码器前向传播
        out, hn, cn = self.encoder(x)

        # 解码器前向传播
        out, hn, cn = self.decoder(y, hn, cn)
        return out

上述代码实现了一个简单的Seq2Seq模型，其中编码器使用LSTM对输入序列进行编码，解码器使用LSTM对输出序列进行解码。可以根据需要调整模型的输入输出大小、隐藏层大小和层数等参数。