帮我用pytorch写一个适合时间序列任务的attention 点击复制后,将打开C知道体验页
时间: 2023-05-26 21:01:44 浏览: 46
A:好的,以下是一个适合时间序列任务的Attention模型的PyTorch实现:
```python
import torch
import torch.nn as nn
class TimeSeriesAttention(nn.Module):
def __init__(self, input_size, hidden_size):
super(TimeSeriesAttention, self).__init__()
self.hidden_size = hidden_size
self.query_layer = nn.Linear(input_size, hidden_size, bias=False)
self.key_layer = nn.Linear(input_size, hidden_size, bias=False)
self.value_layer = nn.Linear(input_size, hidden_size, bias=False)
self.softmax = nn.Softmax(dim=-1)
def forward(self, encoder_outputs):
batch_size, seq_len, input_size = encoder_outputs.size()
query = self.query_layer(encoder_outputs).view(batch_size, seq_len, self.hidden_size)
key = self.key_layer(encoder_outputs).view(batch_size, seq_len, self.hidden_size)
value = self.value_layer(encoder_outputs).view(batch_size, seq_len, self.hidden_size)
scores = torch.bmm(query, key.transpose(1, 2))
attention_weights = self.softmax(scores)
context_vector = torch.bmm(attention_weights, value)
return context_vector
```
其中,`query_layer`、`key_layer`和`value_layer`分别是用来将输入序列(即encoder_outputs)映射到不同的空间中的三个线性层,其输出的维度均为 `[batch_size, seq_len, hidden_size]`。然后,计算`scores`矩阵,并经过softmax操作得到`attention_weights`,再利用其与`value`做矩阵乘法,得到最终的`context_vector`。最后将`context_vector`返回即可。
使用例子:
```python
encoder_outputs = torch.randn(32, 10, 256) # 输入序列大小为[32, 10, 256]
attention = TimeSeriesAttention(input_size=256, hidden_size=128)
context_vector = attention(encoder_outputs) # 输出[32, 1, 128]的context_vector
```