torch.isnan — PyTorch 1.10.1 documentation
pytorch.org › docs › stabletorch.isnan(input) → Tensor. Returns a new tensor with boolean elements representing if each element of input is NaN or not. Complex values are considered NaN when either their real and/or imaginary part is NaN. Parameters. input ( Tensor) – the input tensor. Returns. A boolean tensor that is True where input is NaN and False elsewhere.
torch.nan_to_num — PyTorch 1.10.1 documentation
pytorch.org › docs › stabletorch.nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None) → Tensor. Replaces NaN, positive infinity, and negative infinity values in input with the values specified by nan, posinf, and neginf, respectively. By default, NaN s are replaced with zero, positive infinity is replaced with the greatest finite value representable by ...
Gradient value is nan - PyTorch Forums
discuss.pytorch.org › t › gradient-value-is-nanAug 05, 2020 · Thanks for the answer. Actually I am trying to perform an adversarial attack where I don’t have to perform any training. The strange thing happening is when I calculate my gradients over an original input I get tensor([0., 0., 0., …, nan, nan, nan]) as result but if I made very small changes to my input the gradients turn out to perfect in the range of tensor(0.0580) and tensor(-0.0501)..
torch.nan_to_num — PyTorch 1.10.1 documentation
https://pytorch.org/docs/stable/generated/torch.nan_to_num.htmltorch.nan_to_num¶ torch. nan_to_num (input, nan = 0.0, posinf = None, neginf = None, *, out = None) → Tensor ¶ Replaces NaN, positive infinity, and negative infinity values in input with the values specified by nan, posinf, and neginf, respectively.By default, NaN s are replaced with zero, positive infinity is replaced with the greatest finite value representable by input ’s dtype, and ...