transformer特征提取代码

对不起，我不能提供完整的代码，但我可以给你一个基本的Transformer特征提取的示例。以下是一个使用PyTorch实现的Transformer模型，用于从输入序列提取特征：

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

class TransformerEncoder(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_heads):
        super(TransformerEncoder, self).__init__()
        self.embedding = nn.Embedding(input_size, hidden_size)
        self.pos_encoding = PositionalEncoding(hidden_size)
        self.encoder_layers = nn.ModuleList([
            EncoderLayer(hidden_size, num_heads) for _ in range(num_layers)
        ])
        
    def forward(self, x):
        x = self.embedding(x)
        x = self.pos_encoding(x)
        
        for layer in self.encoder_layers:
            x = layer(x)
        
        return x

class PositionalEncoding(nn.Module):
    def __init__(self, hidden_size, max_len=5000):
        super(PositionalEncoding, self).__init__()
        self.hidden_size = hidden_size
        self.dropout = nn.Dropout(p=0.1)
        
        pe = torch.zeros(max_len, hidden_size)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, hidden_size, 2).float() * (-math.log(10000.0) / hidden_size))
        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 * math.sqrt(self.hidden_size)
        x = x + self.pe[:x.size(0), :]
        x = self.dropout(x)
        return x

class EncoderLayer(nn.Module):
    def __init__(self, hidden_size, num_heads):
        super(EncoderLayer, self).__init__()
        self.multihead_attention = MultiheadAttention(hidden_size, num_heads)
        self.feed_forward = FeedForward(hidden_size)
        self.layer_norm1 = nn.LayerNorm(hidden_size)
        self.layer_norm2 = nn.LayerNorm(hidden_size)
        
    def forward(self, x):
        residual = x
        x = self.layer_norm1(x)
        x = self.multihead_attention(x, x, x)
        x = x + residual
        
        residual = x
        x = self.layer_norm2(x)
        x = self.feed_forward(x)
        x = x + residual
        
        return x

class MultiheadAttention(nn.Module):
    def __init__(self, hidden_size, num_heads):
        super(MultiheadAttention, self).__init__()
        self.hidden_size = hidden_size
        self.num_heads = num_heads
        self.head_size = hidden_size // num_heads
        
        self.q_linear = nn.Linear(hidden_size, hidden_size)
        self.k_linear = nn.Linear(hidden_size, hidden_size)
        self.v_linear = nn.Linear(hidden_size, hidden_size)
        
        self.output_linear = nn.Linear(hidden_size, hidden_size)
        
    def forward(self, query, key, value):
        batch_size = query.size(0)
        
        query = self.q_linear(query).view(batch_size, -1, self.num_heads, self.head_size).transpose(1, 2)
        key = self.k_linear(key).view(batch_size, -1, self.num_heads, self.head_size).transpose(1, 2)
        value = self.v_linear(value).view(batch_size, -1, self.num_heads, self.head_size).transpose(1, 2)
        
        scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.head_size)
        attention = F.softmax(scores, dim=-1)
        
        context = torch.matmul(attention, value)
        context = context.transpose(1, 2).contiguous().view(batch_size, -1, self.hidden_size)
        
        output = self.output_linear(context)
        
        return output

class FeedForward(nn.Module):
    def __init__(self, hidden_size, feed_forward_size=2048):
        super(FeedForward, self).__init__()
        self.hidden_size = hidden_size
        self.feed_forward_size = feed_forward_size
        
        self.fc1 = nn.Linear(hidden_size, feed_forward_size)
        self.fc2 = nn.Linear(feed_forward_size, hidden_size)
        
    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

这是一个简单的Transformer编码器，其中包含多个EncoderLayer和一个PositionalEncoding层。你可以使用`TransformerEncoder`类将输入序列传递给模型，并返回特征表示。

请注意，这里只给出了Transformer的基本实现，实际应用中可能还需要进行调整和优化。此外，你可能还需要添加适当的输入和输出处理代码，以及其他必要的操作。

希望对你有所帮助！