pytorch attention的实例
时间: 2023-09-24 10:06:20 浏览: 47
以下是一个使用PyTorch编写的Attention模型实例:
```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.Linear(hidden_size, 1, bias=False)
def forward(self, hidden, encoder_outputs):
max_len = encoder_outputs.size(0)
batch_size = encoder_outputs.size(1)
# repeat decoder hidden state max_len times
# so that we can concatenate it with encoder_outputs
repeated_hidden = hidden.unsqueeze(0).repeat(max_len, batch_size, 1)
# concatenate hidden state with encoder_outputs
energy = torch.cat((repeated_hidden, encoder_outputs), dim=2)
# calculate attention weights
attn_energies = self.attn(energy) # (max_len, batch_size, hidden_size)
attn_energies = torch.tanh(attn_energies) # (max_len, batch_size, hidden_size)
attn_weights = self.v(attn_energies) # (max_len, batch_size, 1)
attn_weights = attn_weights.squeeze(2) # (max_len, batch_size)
attn_weights = torch.softmax(attn_weights, dim=0) # (max_len, batch_size)
# apply attention weights to encoder outputs
weighted_encoder_outputs = torch.bmm(encoder_outputs.permute(1, 2, 0), attn_weights.permute(1, 0).unsqueeze(2))
weighted_encoder_outputs = weighted_encoder_outputs.squeeze(2) # (batch_size, hidden_size)
# concatenate weighted_encoder_outputs with decoder hidden state
context_vector = torch.cat((weighted_encoder_outputs, hidden), dim=1) # (batch_size, hidden_size * 2)
return context_vector, attn_weights
```
此模型接受Decoder的hidden state和Encoder的输出作为输入,并返回context vector和attention weights。注意力权重计算使用线性层和tanh激活函数,最后使用softmax函数进行标准化。最后,使用batch matrix multiplication计算加权的Encoder输出。