We read every piece of feedback, and take your input very seriously.
To see all available qualifiers, see our documentation.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Hey! I was interested into why are you using tanh here:
attn_src = torch.matmul(F.tanh(h_prime), self.a_src) # bs x n_head x n x 1
in BatchMultiHeadGraphAttention, get_layers.py. Did it stabilize the training? Is it some form of feature normalization?
The text was updated successfully, but these errors were encountered:
Thanks for pointing our this.
Yes, in the original GAT paper, they don't have the tanh activation. But we found that it helps our training a little.
Sorry, something went wrong.
No branches or pull requests
Hey! I was interested into why are you using tanh here:
attn_src = torch.matmul(F.tanh(h_prime), self.a_src) # bs x n_head x n x 1
in BatchMultiHeadGraphAttention, get_layers.py. Did it stabilize the training? Is it some form of feature normalization?
The text was updated successfully, but these errors were encountered: