使用lstm和attention进行时间序列预测的pytorch代码

在PyTorch中，LSTM (长短期记忆网络) 和注意力机制可以结合用于时间序列预测，如文本生成、股票价格预测等。以下是一个简单的示例代码片段，展示了如何构建一个基于LSTM和自注意力机制的模型：

import torch
import torch.nn as nn

class Attention(nn.Module):
    def __init__(self, hidden_size):
        super(Attention, self).__init__()
        self.hidden_size = hidden_size
        self.attn = nn.Linear(hidden_size * 2, hidden_size)
        self.v = nn.Parameter(torch.rand(hidden_size))

    def forward(self, encoder_outputs, decoder_hidden):
        # encoder_outputs形状为(Batch, Seq_len, Hidden_size)
        # decoder_hidden形状为(Batch, Hidden_size)
        seq_len = encoder_outputs.size(1)
        attn_weights = torch.bmm(encoder_outputs, decoder_hidden.unsqueeze(1)).squeeze(1)
        attn_weights = F.softmax(attn_weights, dim=1)

        context = torch.bmm(attn_weights.unsqueeze(1), encoder_outputs).squeeze(1)
        energy = torch.tanh(self.attn(torch.cat((context, decoder_hidden), 1)))
        attention_score = torch.sum(self.v * energy, dim=1).unsqueeze(1)
        return attn_weights, attention_score

class LSTMAttnModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, dropout):
        super(LSTMAttnModel, self).__init__()
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, dropout=dropout)
        self.attention = Attention(hidden_size)
        self.fc_out = nn.Linear(hidden_size, output_size)

    def forward(self, inputs, hidden):
        lstm_output, hidden = self.lstm(inputs, hidden)
        attn_weights, _ = self.attention(lstm_output, hidden[-1])
        weighted_sum = torch.bmm(attn_weights.permute(0, 2, 1), lstm_output)
        output = self.fc_out(weighted_sum.squeeze(1))
        return output, hidden

# 初始化模型参数
input_size = ... # 输入序列特征维度
hidden_size = ... # LSTM隐藏层大小
num_layers = ... # LSTM层数
output_size = ... # 输出序列预测元素数量
dropout = 0.5

model = LSTMAttnModel(input_size, hidden_size, num_layers, dropout)
inputs = ... # 形状为(Batch, Seq_len, Input_size)
hidden = model.init_hidden(batch_size)  # 初始化隐状态

# 进行前向传播并预测
output, _ = model(inputs, hidden)