A3T-GCN的注意力机制
时间: 2023-11-29 14:46:55 浏览: 79
A3T-GCN是一种用于交通预测的神经网络模型,它在T-GCN的基础上引入了注意力机制。注意力机制可以调整不同时间点的重要性,并收集全局时间信息,以提高预测精度。具体来说,A3T-GCN使用了多头注意力机制,将输入的时间序列数据分成多个头,每个头都可以关注不同的时间点。在每个头中,A3T-GCN使用了自注意力机制,以便每个时间点都可以关注到其他时间点的信息。通过这种方式,A3T-GCN可以获取交通状态的全局变化趋势,获得更精准的预测精度。
下面是A3T-GCN注意力机制的示例代码:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadAttention(nn.Module):
def __init__(self, d_model, n_head):
super(MultiHeadAttention, self).__init__()
self.n_head = n_head
self.d_k = d_model // n_head
self.d_v = d_model // n_head
self.q_linear = nn.Linear(d_model, d_model)
self.v_linear = nn.Linear(d_model, d_model)
self.k_linear = nn.Linear(d_model, d_model)
self.out = nn.Linear(d_model, d_model)
def forward(self, q, k, v, mask=None):
bs = q.size(0)
# perform linear operation and split into N heads
k = self.k_linear(k).view(bs, -1, self.n_head, self.d_k)
q = self.q_linear(q).view(bs, -1, self.n_head, self.d_k)
v = self.v_linear(v).view(bs, -1, self.n_head, self.d_v)
# transpose to get dimensions bs * N * sl * d_model
k = k.transpose(1,2)
q = q.transpose(1,2)
v = v.transpose(1,2)
# calculate attention using function we will define next
scores = attention(q, k, v, self.d_k, mask, self.n_head)
concat = scores.transpose(1,2).contiguous().view(bs, -1, self.n_head * self.d_v)
# apply a final linear operation to get the output
output = self.out(concat)
return output
def attention(q, k, v, d_k, mask=None, n_head=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = scores.masked_fill(mask == 0, -1e9)
scores = F.softmax(scores, dim=-1)
scores = scores.view(scores.size(0), n_head, scores.size(1), scores.size(2))
output = torch.matmul(scores, v)
return output
```