lstm attention代码

以下是一个基于LSTM和Attention机制的文本分类模型的代码示例：

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

class LSTMAttention(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, dropout):
        super().__init__()

        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, bidirectional=True)
        self.fc = nn.Linear(hidden_dim * 2, output_dim)
        self.dropout = nn.Dropout(dropout)
        self.attention = nn.Linear(hidden_dim * 2, 1)

    def forward(self, text):
        embedded = self.dropout(self.embedding(text))

        outputs, (hidden, cell) = self.lstm(embedded)

        # Attention mechanism
        attention_weights = F.softmax(self.attention(outputs), dim=0)
        weighted_outputs = torch.sum(outputs * attention_weights, dim=0)

        # Concatenate the final forward and backward hidden state
        hidden = torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1)

        # Apply dropout
        hidden = self.dropout(hidden)

        # Feed the hidden state through the fully connected layer
        output = self.fc(hidden)

        return output

该模型接受一个整数序列作为输入，并输出一个指定大小的输出。它包括一个嵌入层、一个双向LSTM层、一个注意力层、一个全连接层和一个dropout层。在前向传递时，我们首先通过嵌入层传递输入序列，然后通过双向LSTM层获得输出和隐藏状态。我们使用注意力机制来计算加权输出，然后将最终的前向和后向隐藏状态连接在一起，并通过全连接层进行分类。最后，我们使用dropout层来减少过拟合。