Hi,
This is regarding the behavior of torch.maximum and torch.minimum functions.
Here is an example:
Let a be and scalar.
Currently when computing torch.maximum(x, a), if x > a then the gradient is 1, and if x < a then the gradient is 0. BUT if x = a then the gradient is 0.5.
The same is true for torch.minimum.
Are the mathematical reasons for the 0.5 gradient when x = a? or is it for numerical stability issues?
Thanks,