lstm和cnn结合代码pytorch

我可以为您提供一些示例代码，以使您的LSTM和CNN在Pytorch中结合使用：import torch
import torch.nn as nnclass RNN_CNN(nn.Module):
def __init__(self, embedding_dim, hidden_dim, vocab_size, tagset_size):
super(RNN_CNN, self).__init__()
self.hidden_dim = hidden_dim
self.word_embeddings = nn.Embedding(vocab_size, embedding_dim)
self.lstm = nn.LSTM(embedding_dim, hidden_dim)
self.cnn = nn.Conv2d(1,1,(3,embedding_dim))
self.hidden2tag = nn.Linear(hidden_dim, tagset_size) def forward(self, sentence):
embeds = self.word_embeddings(sentence)
lstm_out, _ = self.lstm(embeds.view(len(sentence), 1, -1))
cnn_out = self.cnn(embeds.unsqueeze(1))
tag_space = self.hidden2tag(lstm_out.view(len(sentence), -1) + cnn_out.view(len(sentence), -1))
tag_scores = F.log_softmax(tag_space, dim=1)
return tag_scores