I am pretty new to pytorch and currently looking into the softmax function, I would like to adapt the orignally implemented for some small tests.
I have been to the docs but there wasn’t that much of usefull information about the implementation itself.
def __init__(self, dim=None): super(Softmax, self).__init__() self.dim = dim def __setstate__(self, state): self.__dict__.update(state) if not hasattr(self, 'dim'): self.dim = None def forward(self, input): return F.softmax(input, self.dim, _stacklevel=5)
Where can I find the F.softmax impementation? This is probably a C implementation?
One off the things I want to try for instance is the soft-margin softmax described here: Soft-Margin Softmax for Deep Classification
Is the C implementation the best place to start, or would it be easier to write it in python?
Where would be the best place to start?
Thanks in advance!