transformer分类器代码

以下是使用Transformer作为分类器的示例代码，使用PyTorch实现：

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

class TransformerClassifier(nn.Module):
    def __init__(self, vocab_size, max_seq_len, num_classes, d_model=256, num_heads=8, num_layers=3):
        super(TransformerClassifier, self).__init__()
        self.embedding = nn.Embedding(vocab_size, d_model)
        self.pos_encoding = PositionalEncoding(d_model, max_seq_len)
        self.transformer_encoder = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(d_model, num_heads),
            num_layers)
        self.fc = nn.Linear(d_model, num_classes)

    def forward(self, x):
        x = self.embedding(x)
        x = self.pos_encoding(x)
        x = x.permute(1, 0, 2)  # (batch_size, seq_len, d_model) -> (seq_len, batch_size, d_model)
        x = self.transformer_encoder(x)
        x = x.mean(dim=0)  # mean pooling over the sequence dimension
        x = self.fc(x)
        return F.log_softmax(x, dim=1)

class PositionalEncoding(nn.Module):
    def __init__(self, d_model, max_seq_len):
        super(PositionalEncoding, self).__init__()
        self.d_model = d_model
        self.pos_encoding = torch.zeros(max_seq_len, d_model)

        position = torch.arange(0, max_seq_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
        self.pos_encoding[:, 0::2] = torch.sin(position * div_term)
        self.pos_encoding[:, 1::2] = torch.cos(position * div_term)
        self.pos_encoding = self.pos_encoding.unsqueeze(0)

    def forward(self, x):
        x = x * math.sqrt(self.d_model)
        seq_len = x.size(1)
        x = x + self.pos_encoding[:, :seq_len, :]
        return x

这个示例代码使用Transformer作为分类器，接受一个整数序列作为输入，输出一个概率分布，表示输入属于各个类别的概率。在这个示例中，我们使用了一个简单的平均池化来将Transformer的输出压缩成一个固定大小的向量，然后再通过一个全连接层将其映射到概率分布。