lstm-transformer 代码

### LSTM-Transformer 混合模型代码实现 为了构建LSTM-Transformer混合模型，可以先利用LSTM来提取局部时间特征，再通过Transformer捕捉更复杂的长期依赖关系。下面是一个简单的PyTorch框架下的实现例子。 #### 导入库并定义辅助函数 ```python import torch from torch import nn, optim import torch.nn.functional as F ``` #### 定义LSTM模块 创建一个基于LSTM的编码器用于初步的信息抽取： ```python class LSTMEncoder(nn.Module): def __init__(self, input_size, hidden_size, num_layers=1): super(LSTMEncoder, self).__init__() self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True) def forward(self, x): output, (h_n, c_n) = self.lstm(x) return h_n[-1].unsqueeze(0), c_n[-1].unsqueeze(0) ``` #### 构建Transformer部分 接着设计带有自注意力机制的Transformer组件，它能够进一步处理由LSTM传递过来的数据流： ```python class TransformerDecoder(nn.Module): def __init__(self, d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward): super(TransformerDecoder, self).__init__() self.transformer = nn.Transformer(d_model=d_model, nhead=nhead, num_encoder_layers=num_encoder_layers, num_decoder_layers=num_decoder_layers, dim_feedforward=dim_feedforward) def forward(self, src, tgt): memory = self.transformer.encoder(src.permute(1, 0, 2)) out = self.transformer.decoder(tgt.permute(1, 0, 2), memory).permute(1, 0, 2) return out ``` #### 组合成完整的网络结构 最后组合上述两个主要部件形成最终的混合模型架构： ```python class HybridModel(nn.Module): def __init__(self, lstm_input_dim, lstm_hidden_dim, trans_d_model, trans_nhead, trans_num_encoders, trans_num_decoders, trans_ffn_dim): super(HybridModel, self).__init__() self.lstm_encoder = LSTMEncoder(lstm_input_dim, lstm_hidden_dim) self.transformer_decoder = TransformerDecoder(trans_d_model, trans_nhead, trans_num_encoders, trans_num_decoders, trans_ffn_dim) def forward(self, inputs_lstm, inputs_transformer): _, last_state = self.lstm_encoder(inputs_lstm) outputs = self.transformer_decoder(last_state.repeat(1,inputs_transformer.size()[1],1), inputs_transformer) return outputs ``` 此段代码展示了如何将两种不同类型的神经元连接起来工作，在实践中可能还需要根据具体应用场景调整细节参数配置[^2]。