Transformer识别模型代码

以下是一个基于PyTorch实现的Transformer识别模型代码：

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


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)


class TransformerClassifier(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim, num_layers, num_heads, dropout):
        super(TransformerClassifier, self).__init__()

        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        self.num_layers = num_layers
        self.num_heads = num_heads
        
        self.embedding = nn.Embedding(input_dim, hidden_dim)
        self.pos_encoder = PositionalEncoding(hidden_dim, dropout=dropout)
        encoder_layer = nn.TransformerEncoderLayer(hidden_dim, num_heads, hidden_dim, dropout)
        self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers)
        self.fc = nn.Linear(hidden_dim, output_dim)
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, src):
        # src shape: (sequence_length, batch_size)
        src = self.embedding(src) * math.sqrt(self.hidden_dim)
        src = self.pos_encoder(src)
        output = self.transformer_encoder(src)
        output = output.mean(dim=0)
        output = self.fc(output)
        output = self.dropout(output)
        return output

该模型包含一个Transformer编码器，输入为一个序列，输出为对序列进行分类的结果。其中包含一个Embedding层将输入的词语映射为固定维度的向量，一个位置编码层将位置信息融入到向量中，多个Transformer编码器层对输入的向量进行编码，一个全连接层将编码后的结果映射为分类结果。这个模型可以用于文本分类等任务。