双向lstm的pytorch代码实现

双向LSTM在PyTorch中的代码实现如下：

import torch
import torch.nn as nn

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

这段代码实现了一个双向LSTM模型，包含了输入大小(input_size)、隐藏层大小(hidden_size)、层数(num_layers)和输出大小(output_size)等参数。在forward函数中，首先初始化了LSTM的初始隐藏状态h0和记忆状态c0，然后将输入序列x输入到LSTM中得到输出out，最后通过全连接层(fc)将最后一个时间步的隐藏状态映射到输出空间中。

以上就是双向LSTM的PyTorch代码实现。你可以根据具体的任务需求进行调整和修改。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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