pytorch 代码实现bilstm-self-attention

可以使用以下代码实现bilstm-self-attention：

import torch
import torch.nn as nn

class BiLSTM_SelfAttention(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, num_classes):
super(BiLSTM_SelfAttention, 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, bidirectional=True)
self.fc = nn.Linear(hidden_size*2, num_classes)
self.softmax = nn.Softmax(dim=1)
self.attention = nn.Linear(hidden_size*2, 1)

def forward(self, x):
h = torch.zeros(self.num_layers*2, x.size(), self.hidden_size).to(device)
c = torch.zeros(self.num_layers*2, x.size(), self.hidden_size).to(device)
out, _ = self.lstm(x, (h, c))
attention_weights = self.softmax(self.attention(out))
context_vector = torch.sum(attention_weights * out, dim=1)
out = self.fc(context_vector)
return out

其中，input_size 表示输入特征的维度，hidden_size 表示 LSTM 的隐藏层维度，num_layers 表示 LSTM 的层数，num_classes 表示分类的类别数。