lstm加注意力机制python

下面是一个使用LSTM和注意力机制的Python代码的示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class LSTMAttention(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers):
        super(LSTMAttention, 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, 1)

    def attention(self, lstm_output):
        attn_weights = F.softmax(self.fc(lstm_output), dim=1)
        attn_output = torch.bmm(attn_weights.transpose(1, 2), lstm_output)
        return attn_output

    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))
        attn_output = self.attention(out)

        return attn_output

上面的代码定义了一个名为LSTMAttention的模型类，它继承自nn.Module类。该模型包含一个LSTM层和一个注意力机制层。在forward方法中，输入的x经过LSTM层后再经过注意力机制层，最后返回注意力输出。

你可以根据自己的需求修改输入维度、隐藏层大小和层数等参数来创建自定义的LSTM加注意力机制的模型。<em>1</em><em>2</em><em>3</em>
#### 引用[.reference_title]
- *1* *3* [LSTM+注意力机制(Attention)实现时间序列预测(PyTorch版)](https://blog.csdn.net/m0_47256162/article/details/128697592)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}} ] [.reference_item]
- *2* [【Pytorch卷1】=9=注意力机制-LSTM【监督学习】](https://blog.csdn.net/Patrick6789/article/details/122688799)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}} ] [.reference_item]
[ .reference_list ]