seq2seq模型代码

以下是一个简单的 seq2seq 模型代码示例，使用 PyTorch 实现：

import torch
import torch.nn as nn
import torch.optim as optim

class Encoder(nn.Module):
    def __init__(self, input_size, hidden_size):
        super(Encoder, self).__init__()
        self.hidden_size = hidden_size
        self.embedding = nn.Embedding(input_size, hidden_size)
        self.gru = nn.GRU(hidden_size, hidden_size)

    def forward(self, input, hidden):
        embedded = self.embedding(input).view(1, 1, -1)
        output, hidden = self.gru(embedded, hidden)
        return output, hidden

    def init_hidden(self):
        return torch.zeros(1, 1, self.hidden_size)

class Decoder(nn.Module):
    def __init__(self, hidden_size, output_size):
        super(Decoder, self).__init__()
        self.hidden_size = hidden_size
        self.embedding = nn.Embedding(output_size, hidden_size)
        self.gru = nn.GRU(hidden_size, hidden_size)
        self.out = nn.Linear(hidden_size, output_size)
        self.softmax = nn.LogSoftmax(dim=1)

    def forward(self, input, hidden):
        output = self.embedding(input).view(1, 1, -1)
        output = nn.functional.relu(output)
        output, hidden = self.gru(output, hidden)
        output = self.softmax(self.out(output[0]))
        return output, hidden

    def init_hidden(self):
        return torch.zeros(1, 1, self.hidden_size)

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

    def forward(self, input, target, teacher_forcing_ratio=0.5):
        target_length = target.size(0)
        batch_size = 1
        target_vocab_size = self.decoder.out.out_features

        encoder_hidden = self.encoder.init_hidden()

        encoder_outputs = torch.zeros(MAX_LENGTH, self.encoder.hidden_size, device=device)

        for i in range(input.size(0)):
            encoder_output, encoder_hidden = self.encoder(input[i], encoder_hidden)
            encoder_outputs[i] = encoder_output[0, 0]

        decoder_input = torch.tensor([[SOS_token]], device=device)

        decoder_hidden = encoder_hidden

        use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False

        if use_teacher_forcing:
            for i in range(target_length):
                decoder_output, decoder_hidden = self.decoder(decoder_input, decoder_hidden)
                loss += criterion(decoder_output, target[i])
                decoder_input = target[i]
        else:
            for i in range(target_length):
                decoder_output, decoder_hidden = self.decoder(decoder_input, decoder_hidden)
                topv, topi = decoder_output.topk(1)
                decoder_input = topi.squeeze().detach() 
                loss += criterion(decoder_output, target[i])
                if decoder_input.item() == EOS_token:
                    break

        return loss

该模型包括 Encoder 和 Decoder 两个部分，其中 Encoder 将输入序列编码成一个 context 向量，Decoder 则根据 context 向量生成输出序列。在训练过程中，可以使用 teacher forcing 技术，即在 Decoder 的每一步中，都将真实的输出作为下一步的输入，以提高模型的训练效果。