lstm代码实现pytorch

可以参考以下代码实现LSTM模型：

import torch
import torch.nn as nn

class LSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(LSTMModel, 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):
        # Initialize hidden state
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        
        # Initialize cell state                
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)

        # Forward propagate LSTM
        out, _ = self.lstm(x, (h0, c0))  

        # Decode the hidden state of the last time step
        out = self.fc(out[:, -1, :])
        return out

其中，`input_size`为输入特征的维度，`hidden_size`为LSTM隐层特征的维度，`num_layers`为LSTM网络的层数，`output_size`为模型输出的维度。在`forward`函数中，我们先通过`zeros`函数初始化LSTM的隐藏状态和单元状态，然后将输入数据`x`传入LSTM网络，返回序列的输出和最后一个时间步的隐藏状态。最后，我们通过线性全连接层将隐藏状态映射到最终输出。