基于pytorch写出用于时序预测的添加注意力机制的Seq2Seq模型

可以使用Pytorch中的nn.Module类来构建Seq2Seq模型。首先，需要定义编码器和解码器。编码器将输入序列编码为隐藏状态，而解码器将隐藏状态解码为输出序列。注意力机制可以在解码器中实现。

具体实现方法如下:

import torch
import torch.nn as nn

class Seq2Seq(nn.Module):
    def __init__(self, encoder, decoder):
        super(Seq2Seq, self).__init__()
        self.encoder = encoder
        self.decoder = decoder

    def forward(self, src, trg, src_len, trg_len):
        encoder_hidden = self.encoder(src, src_len)
        return self.decoder(trg, encoder_hidden, trg_len)

class Encoder(nn.Module):
    def __init__(self, vocab_size, emb_size, hidden_size, num_layers, dropout):
        super(Encoder, self).__init__()
        self.embedding = nn.Embedding(vocab_size, emb_size)
        self.lstm = nn.LSTM(emb_size, hidden_size, num_layers, dropout=dropout, bidirectional=True)

    def forward(self, src, src_len):
        embedded = self.embedding(src)
        packed_embedded = nn.utils.rnn.pack_padded_sequence(embedded, src_len)
        packed_output, (hidden, cell) = self.lstm(packed_embedded)
        output, _ = nn.utils.rnn.pad_packed_sequence(packed_output)
        return hidden, cell

class AttentionDecoder(nn.Module):
    def __init__(self, vocab_size, emb_size, hidden_size, num_layers, dropout):
        super(AttentionDecoder, self).__init__()
        self.embedding = nn.Embedding(vocab_size, emb_size)
        self.lstm = nn.LSTM(emb_size + hidden_size, hidden_size, num_layers, dropout=dropout)
        self.linear = nn.Linear(hidden_size * 2, hidden_size)
        self.out = nn.Linear(hidden_size, vocab_size)
        self.attn = nn.Linear(hidden_size * 2, hidden_size)
        self.v = nn.Linear(hidden_size,