想要一段self attention的代码

自注意力代码的实现非常复杂，但可以用以下代码实现：import torch
import torch.nn as nnclass SelfAttention(nn.Module):
def __init__(self, hidden_size):
super(SelfAttention, self).__init__()
self.hidden_size = hidden_size
self.attention = nn.Linear(hidden_size, 1) def forward(self, encoder_outputs):
energy = self.attention(encoder_outputs)
weights = torch.softmax(energy.squeeze(-1), dim=1)
weighted = weights * encoder_outputs
context_vector = torch.sum(weighted, dim=1)
return context_vector, weights

相关问题

写一段代码pytorch实现self-attention

好的，下面是一个简单的 PyTorch 实现 self-attention 的代码：

import torch
import torch.nn as nn

class SelfAttention(nn.Module):
    def __init__(self, hidden_size):
        super(SelfAttention, self).__init__()
        self.hidden_size = hidden_size
        self.query = nn.Linear(hidden_size, hidden_size)
        self.key = nn.Linear(hidden_size, hidden_size)
        self.value = nn.Linear(hidden_size, hidden_size)
        self.softmax = nn.Softmax(dim=2)
        
    def forward(self, x):
        query = self.query(x)
        key = self.key(x)
        value = self.value(x)
        scores = torch.matmul(query, key.transpose(-2, -1)) / torch.sqrt(torch.tensor(self.hidden_size))
        scores = self.softmax(scores)
        output = torch.matmul(scores, value)
        
        return output

这个 self-attention 实现使用了三个线性层来计算查询、键和值。然后，通过矩阵乘法来计算得分，并通过 softmax 函数进行归一化。最后，将得分与值相乘得到输出。

生成一段attention代码

这里提供一个 PyTorch 中使用的自注意力机制（self-attention）的代码示例：

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

class SelfAttention(nn.Module):
    def __init__(self, hidden_size, num_heads):
        super(SelfAttention, self).__init__()
        self.hidden_size = hidden_size
        self.num_heads = num_heads
        self.head_size = hidden_size // num_heads

        self.query = nn.Linear(hidden_size, hidden_size, bias=False)
        self.key = nn.Linear(hidden_size, hidden_size, bias=False)
        self.value = nn.Linear(hidden_size, hidden_size, bias=False)

        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x):
        batch_size, seq_len, hidden_size = x.size()
        residual = x

        # Project inputs to Q, K, V
        q = self.query(x).view(batch_size, seq_len, self.num_heads, self.head_size).transpose(1, 2)  # (batch_size, num_heads, seq_len, head_size)
        k = self.key(x).view(batch_size, seq_len, self.num_heads, self.head_size).transpose(1, 2)  # (batch_size, num_heads, seq_len, head_size)
        v = self.value(x).view(batch_size, seq_len, self.num_heads, self.head_size).transpose(1, 2)  # (batch_size, num_heads, seq_len, head_size)

        # Compute dot product attention
        attention_scores = torch.matmul(q, k.transpose(-2, -1)) / self.head_size**0.5  # (batch_size, num_heads, seq_len, seq_len)
        attention_probs = self.softmax(attention_scores)

        # Apply attention weights to V
        attention_output = torch.matmul(attention_probs, v)  # (batch_size, num_heads, seq_len, head_size)

        # Reshape and concatenate attention outputs
        attention_output = attention_output.transpose(1, 2).contiguous().view(batch_size, seq_len, self.hidden_size)
        output = attention_output + residual

        return output

这个自注意力机制包含了查询（query）、键（key）和值（value）三个线性变换，以及一个 Softmax 操作，实现了将输入序列中每个位置的信息与其他位置的信息进行交互和整合，从而得到更加全局的表示。这个实现中使用了多头注意力（multi-head attention）的思想，可以进一步提高模型的表现。