双向Transformer编码器

双向Transformer编码器是指在Transformer架构中，编码器可以同时考虑输入序列中前后两个方向的信息。这是通过在编码器中使用自注意力机制实现的，自注意力机制可以让编码器在处理每个输入时，同时考虑输入序列中其他位置的信息。

下面是一个双向Transformer编码器的示例代码：

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, dropout):
        super(TransformerEncoder, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.num_heads = num_heads
        self.dropout = dropout

        self.embedding = nn.Embedding(input_size, hidden_size)
        self.pos_embedding = nn.Embedding(1000, hidden_size)
        self.layers = nn.ModuleList([TransformerEncoderLayer(hidden_size, num_heads, dropout) for _ in range(num_layers)])
        self.dropout_layer = nn.Dropout(dropout)

    def forward(self, input_seq):
        seq_len, batch_size = input_seq.size()
        pos_seq = torch.arange(seq_len, device=input_seq.device).unsqueeze(1).repeat(1, batch_size)
        input_embedded = self.embedding(input_seq) + self.pos_embedding(pos_seq)

        output = input_embedded
        for layer in self.layers:
            output = layer(output)

        return output