Currently you need to specify softmax dimension as dim. This is nice, and it will complain if you don’t set the dim. Why are the dimensions -1 and -2 used for?
I expected when dim=-1 this should do softmax on whole tensor. Is this possible?
Would be interesting to know the same quest for the log_softmax also?
Don’t know if softmax directly but you can always reshape go and back.
Thanks, reshaping the tensor definitely works, but I just wanted to check if this is the only way.
I personally prefer explicit code (as long as it doesn’t add a huge overhead) and in this case is pretty readable:
data = data.view(-1).softmax(0).view(*data.shape)
It is even simpler if you don’t care about recovering the previous dimensions:
data = data.view(-1).softmax(0)
Also, dim=-1 refers to last dimension, which is equivalent to dim=data.ndimension()-1.
for the last dimension tip. I forgot that.