attention lstm代码实现

以下是一个简单的 Attention LSTM 的代码实现：

import torch
import torch.nn as nn

class AttentionLSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers):
        super(AttentionLSTM, 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 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))
        out = out[:, -1, :]
        out = self.fc(out)
        out = torch.sigmoid(out)
        out = out.unsqueeze(1)
        attn_weights = torch.bmm(out, x)
        attn_weights = torch.softmax(attn_weights, dim=1)
        out = torch.bmm(attn_weights, out)
        return out

希望这个代码能够帮到你！