Comments (1)
I think in this line of code, x is processed by mutil-attention.
Each att(x,adj) calculate one head of attention score. And these heads are concated and sent to another layer whose output's dimension is equal to the kinds of label.
In this case the layer can be simular as the mlp layer in tradition attention algorithm.
You can search the "multi-attention" for more infomation.
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Related Issues (20)
- according to the definition of softmax this line maybe wrong HOT 1
- Model instability
- How to implement batch training? HOT 24
- How to implement the GAT model to a regression problem? **Particularly the design of labels**
- Getting this error!
- transform to other scope dataset
- How to visualize the learned Attention?
- The result score is acc=84.6
- The bias is not necessary?
- Why use plural title?
- Expected object of type torch.cuda.LongTensor but found type torch.LongTensor for argument #1 'indices'
- Parameter containing nan
- can GAT convert to caffemodel?
- runtime error HOT 1
- About DataParallel , multi gpu
- Why batch training? HOT 2
- error HOT 1
- How to apply this model to extract graph features from multiple graphs? HOT 3
- pyGAT?pyG GAT? HOT 1
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