双重注意力机制LSTM代码

以下是一个使用双重注意力机制LSTM的代码示例：

import torch
import torch.nn as nn

class DualAttentionLSTM(nn.Module):
    def __init__(self, input_size, hidden_size):
        super(DualAttentionLSTM, self).__init__()
        self.hidden_size = hidden_size
        self.lstm = nn.LSTM(input_size, hidden_size)
        self.time_attention = nn.Linear(hidden_size, 1)
        self.element_attention = nn.Linear(hidden_size, 1)
        
    def forward(self, inputs):
        # inputs shape: (sequence_length, batch_size, input_size)
        outputs, hidden = self.lstm(inputs)
        time_weights = torch.softmax(self.time_attention(outputs), dim=0)
        element_weights = torch.softmax(self.element_attention(outputs), dim=1)
        # shape of time_weights: (sequence_length, batch_size, 1)
        # shape of element_weights: (sequence_length, batch_size, hidden_size, 1)
        weighted_outputs = outputs * time_weights * element_weights.squeeze(3)
        # sum over the sequence length dimension
        final_outputs = torch.sum(weighted_outputs, dim=0)
        return final_outputs

这个模型使用了PyTorch框架，包括一个LSTM层和两个线性层，用于计算时间注意力和元素注意力。在前向传播中，输入序列通过LSTM层，然后通过时间注意力和元素注意力层进行加权。最后，将加权的输出相加并返回最终输出。