It seems that torch.sum(x, keepdim=True, dim=...)
provides more control over x.sum()
, when x
is tensor
.
However, which form is more appropriate to be used in customized loss functions that are used in backpropagation
?
And, how about speed?
It seems that torch.sum(x, keepdim=True, dim=...)
provides more control over x.sum()
, when x
is tensor
.
However, which form is more appropriate to be used in customized loss functions that are used in backpropagation
?
And, how about speed?
Hi,
As of latest version, they actually have exactely the same arguments. They will do the exact same thing with the autograd.