机器翻译 transformer代码

Transformer是一种用于机器翻译的深度学习模型，它在2017年被提出，并在机器翻译任务上取得了很好的效果。下面是一个简单的Transformer代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class Transformer(nn.Module):
    def __init__(self, input_vocab_size, output_vocab_size, max_seq_len, d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward, dropout):
        super(Transformer, self).__init__()

        self.embedding = nn.Embedding(input_vocab_size, d_model)
        self.pos_encoder = PositionalEncoding(d_model, dropout)
        self.transformer = nn.Transformer(d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward, dropout)
        self.fc = nn.Linear(d_model, output_vocab_size)

    def forward(self, src, tgt):
        src = self.embedding(src)
        src = self.pos_encoder(src)

        tgt = self.embedding(tgt)
        tgt = self.pos_encoder(tgt)

        output = self.transformer(src, tgt)
        output = self.fc(output)

        return F.log_softmax(output, dim=-1)

class PositionalEncoding(nn.Module):
    def __init__(self, d_model, dropout=0.1, max_len=5000):
        super(PositionalEncoding, self).__init__()
        self.dropout = nn.Dropout(p=dropout)

        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0).transpose(0, 1)
        self.register_buffer('pe', pe)

    def forward(self, x):
        x = x + self.pe[:x.size(0), :]
        return self.dropout(x)

这段代码定义了一个Transformer模型，包括了Embedding层、位置编码层、Transformer层和全连接层。其中，`input_vocab_size`和`output_vocab_size`分别表示输入和输出的词汇表大小，`max_seq_len`表示最大序列长度，`d_model`表示模型的维度，`nhead`表示多头注意力的头数，`num_encoder_layers`和`num_decoder_layers`表示编码器和解码器的层数，`dim_feedforward`表示前馈网络的隐藏层维度，`dropout`表示dropout的概率。

在`forward`方法中，首先对输入进行词嵌入和位置编码，然后通过Transformer层进行编码和解码，最后通过全连接层输出结果。