bilstm和lstm代码区别

BiLSTM和LSTM是两种常用的循环神经网络（RNN）模型，它们在处理序列数据时具有一定的差异。下面是它们的代码区别：

1. LSTM（长短期记忆网络）代码示例：

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):
        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

2. BiLSTM（双向长短期记忆网络）代码示例：

import torch
import torch.nn as nn

class BiLSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(BiLSTMModel, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.bilstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, bidirectional=True)
        self.fc = nn.Linear(hidden_size * 2, output_size)

    def forward(self, x):
        h0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size).to(x.device)
        c0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size).to(x.device)
        out, _ = self.bilstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out

上述代码中，LSTMModel是一个单向LSTM模型，而BiLSTMModel是一个双向LSTM模型。它们的区别在于BiLSTMModel中的LSTM层是双向的，即同时考虑了正向和反向的序列信息。在代码中，双向LSTM的隐藏状态和细胞状态的维度是单向LSTM的两倍，并且在最后的全连接层中，使用了双向LSTM的隐藏状态进行预测。