注意力机制代码pytorch
时间: 2023-07-01 09:28:15 浏览: 53
以下是一个简单的注意力机制的代码示例,使用PyTorch实现:
```python
import torch
import torch.nn as nn
class Attention(nn.Module):
def __init__(self, hidden_size):
super(Attention, self).__init__()
self.hidden_size = hidden_size
self.attn = nn.Linear(self.hidden_size * 2, hidden_size)
self.v = nn.Parameter(torch.rand(hidden_size))
def forward(self, hidden, encoder_outputs):
"""
:param hidden: the previous hidden state of the decoder, shape=(1, batch_size, hidden_size)
:param encoder_outputs: the outputs from the encoder, shape=(max_length, batch_size, hidden_size)
:return: attention weights, shape=(batch_size, 1, max_length)
"""
max_len = encoder_outputs.size(0)
h = hidden.repeat(max_len, 1, 1).transpose(0, 1)
encoder_outputs = encoder_outputs.transpose(0, 1)
attn_energies = self.score(h, encoder_outputs)
return nn.functional.softmax(attn_energies, dim=2).unsqueeze(1)
def score(self, hidden, encoder_outputs):
energy = torch.tanh(self.attn(torch.cat([hidden, encoder_outputs], dim=2)))
energy = energy.transpose(1, 2)
v = self.v.repeat(encoder_outputs.size(0), 1).unsqueeze(1)
energy = torch.bmm(v, energy)
return energy
```
这里实现的是一个简单的Bahdanau Attention,其中 `hidden_size` 是隐藏层的维度。`forward` 方法接收一个先前的隐藏状态 `hidden` 和来自编码器的输出 `encoder_outputs`,并返回注意力权重。`score` 方法计算注意力权重的能量。