Shortcuts

torch.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 :attr:`input’s dtype, and negative infinity is replaced with the least finite value representable by input’s dtype.

Parameters
  • input (Tensor) – the input tensor.

  • nan (Number, optional) – the value to replace NaNs with. Default is zero.

  • posinf (Number, optional) – if a Number, the value to replace positive infinity values with. If None, positive infinity values are replaced with the greatest finite value representable by input’s dtype. Default is None.

  • neginf (Number, optional) – if a Number, the value to replace negative infinity values with. If None, negative infinity values are replaced with the lowest finite value representable by input’s dtype. Default is None.

Keyword Arguments

out (Tensor, optional) – the output tensor.

Example:

>>> x = torch.tensor([float('nan'), float('inf'), -float('inf'), 3.14])
>>> torch.nan_to_num(x)
tensor([ 0.0000e+00,  3.4028e+38, -3.4028e+38,  3.1400e+00])
>>> torch.nan_to_num(x, nan=2.0)
tensor([ 2.0000e+00,  3.4028e+38, -3.4028e+38,  3.1400e+00])
>>> torch.nan_to_num(x, nan=2.0, posinf=1.0)
tensor([ 2.0000e+00,  1.0000e+00, -3.4028e+38,  3.1400e+00])

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources