用pytorch写LSTM代码

好的，我可以回答这个问题。以下是使用PyTorch实现LSTM的基本代码：

import torch
import torch.nn as nn

class LSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(LSTM, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.output_size = output_size
        
        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):
        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)
        
        out, _ = self.lstm(x, (h0,c0))
        out = self.fc(out[:, -1, :])
        
        return out

其中，`input_size`是输入特征的维度，`hidden_size`是LSTM隐藏状态的维度，`num_layers`是LSTM的层数，`output_size`是输出特征的维度。`self.lstm`使用PyTorch内置的LSTM模块，将其输入作为参数传入即可。`self.fc`是一个全连接层，将最后一个时间步的隐藏状态映射到输出维度。`forward`函数中，`h0`和`c0`是LSTM的初始隐藏状态和记忆状态，使用全零矩阵初始化。`out`是LSTM最终的输出，取最后一个时间步的输出作为全连接层的输入，输出预测结果。