lstm+transformer python代码

LSTM（长短期记忆网络）和Transformer是深度学习中常用的序列模型，它们可以用于自然语言处理任务。在这里，我们将简要展示如何结合LSTM和Transformer的示例Python代码，这通常用于构建编码-解码模型，如机器翻译。首先安装必要的库（如TensorFlow或PyTorch）。

# 使用PyTorch示例
import torch
import torch.nn as nn

class LSTM_Transformer(nn.Module):
    def __init__(self, input_dim, emb_dim, hid_dim, n_layers, dropout, pad_idx, device):
        super().__init__()
        
        self.device = device
        
        # LSTM层
        self.lstm = nn.LSTM(input_dim, hid_dim, n_layers, bidirectional=True, dropout=dropout)
        
        # Transformer部分
        self.transformer = nn.Transformer(hid_dim*2, emb_dim, n_heads=8, dropout=dropout)
        self.linear = nn.Linear(emb_dim, output_dim)  # 输出维度取决于具体任务

        # 初始化权重矩阵
        self.embedding = nn.Embedding(input_dim, emb_dim, padding_idx=pad_idx)

    def forward(self, src):
        embedded = self.embedding(src)   # 输入序列嵌入
        packed_embedded = nn.utils.rnn.pack_padded_sequence(embedded, src.eq(self.pad_idx).sum(dim=0), enforce_sorted=False)
        packed_output, (hidden, cell) = self.lstm(packed_embedded)  # LSTM编码
        hidden = torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim=1)  # 双向LSTM取最后两层的隐藏状态
        hidden = hidden.permute(1, 0, 2)  # 为了适应Transformer输入格式
        output = self.transformer(hidden)  # Transformer解码
        output = self.linear(output)       # 应用线性变换得到最终预测
        
        return output

# 假设你已经有了输入和输出维度（input_dim, output_dim），以及其他超参数设置
model = LSTM_Transformer(input_dim, emb_dim, hid_dim, n_layers, dropout, pad_idx, device='cuda' if torch.cuda.is_available() else 'cpu')