DA注意力机制python代码

根据引用和引用中的内容，DA（Dual Attention）注意力机制是一种基于Seq2Seq模型的方法，结合了注意力机制实现时间序列预测。该方法不仅在解码器的输入阶段引入注意力机制，还在编码器阶段引入注意力机制，实现了特征选取和把握时序依赖关系的作用。

根据引用中的代码实现，以下是一个基于DA注意力机制的Python代码示例：

import torch
import torch.nn as nn

class DAAttention(nn.Module):
    def __init__(self, input_size, hidden_size):
        super(DAAttention, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, input_size)

    def forward(self, encoder_outputs, decoder_output):
        """
        :param encoder_outputs: 编码器输出，形状为<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
-  *1* *2* [【时间序列】DA-RNN: 基于双阶段注意力机制的循环神经网络](https://blog.csdn.net/abcdefg90876/article/details/108570860)[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^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"]
-  *3* [注意力机制详解](https://blog.csdn.net/m0_62311817/article/details/125881229)[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^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"]
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