transformer如何embedding

Transformer的embedding部分可以通过以下步骤实现：

1. 首先，将输入序列进行编码。这可以通过将输入序列中的每个元素映射到一个低维的向量表示来完成。常用的方法是使用一个可学习的嵌入层（embedding layer）来实现这一点。

2. 接下来，为输入序列中的每个位置添加位置编码。位置编码是一个向量，用于表示输入序列中每个位置的相对位置信息。常用的方法是使用正弦和余弦函数来生成位置编码。

3. 然后，将编码后的输入序列输入到Transformer的多层自注意力机制（multi-head self-attention）中。自注意力机制可以捕捉输入序列中不同位置之间的依赖关系。

4. 在自注意力机制的输出上应用前馈神经网络（feed-forward neural network）进行特征提取和维度变换。

5. 最后，将经过特征提取的输出序列进行池化操作，得到一个固定长度的向量表示。这个向量表示可以用作分类任务的输入。

下面是一个示例代码，演示了如何在Transformer中进行embedding：

import torch
import torch.nn as nn

class TransformerEmbedding(nn.Module):
    def __init__(self, input_dim, hidden_dim, num_layers, num_heads):
        super(TransformerEmbedding, self).__init__()
        
        self.embedding = nn.Embedding(input_dim, hidden_dim)
        self.position_encoding = PositionalEncoding(hidden_dim)
        self.transformer = Transformer(hidden_dim, num_layers, num_heads)
        self.pooling = nn.AdaptiveAvgPool1d(1)
        
    def forward(self, x):
        embedded = self.embedding(x)
        encoded = self.position_encoding(embedded)
        transformed = self.transformer(encoded)
        pooled = self.pooling(transformed.permute(0, 2, 1))
        
        return pooled.squeeze(2)

class PositionalEncoding(nn.Module):
    def __init__(self, hidden_dim, max_len=5000):
        super(PositionalEncoding, self).__init__()
        
        self.dropout = nn.Dropout(p=0.1)
        
        pe = torch.zeros(max_len, hidden_dim)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, hidden_dim, 2).float() * (-math.log(10000.0) / hidden_dim))
        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 Transformer(nn.Module):
    def __init__(self, hidden_dim, num_layers, num_heads):
        super(Transformer, self).__init__()
        
        self.layers = nn.ModuleList([TransformerLayer(hidden_dim, num_heads) for _ in range(num_layers)])
        
    def forward(self, x):
        for layer in self.layers:
            x = layer(x)
        return x

class TransformerLayer(nn.Module):
    def __init__(self, hidden_dim, num_heads):
        super(TransformerLayer, self).__init__()
        
        self.self_attention = nn.MultiheadAttention(hidden_dim, num_heads)
        self.feed_forward = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim * 4),
            nn.ReLU(),
            nn.Linear(hidden_dim * 4, hidden_dim)
        )
        self.layer_norm1 = nn.LayerNorm(hidden_dim)
        self.layer_norm2 = nn.LayerNorm(hidden_dim)
        
    def forward(self, x):
        attended, _ = self.self_attention(x, x, x)
        x = x + attended
        x = self.layer_norm1(x)
        
        fed_forward = self.feed_forward(x)
        x = x + fed_forward
        x = self.layer_norm2(x)
        
        return x