*My post explains torch.nan and torch.inf.
nan_to_num() can get the 0D or more D tensor of zero or more elements, replacing zero or more NaNs(Not a Numbers), positive infinities and negative infinities with zero or more zeros, the greatest finities and the least finities respectively(Default) or specified values from the 0D or more D tensor of zero or more elements as shown below:
*Memos:
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nan_to_num()can be used with torch or a tensor. - The 1st argument(
input) withtorchor using a tensor(Required-Type:tensorofint,float,complexorbool). - The 2nd argument with
torchor the 1st argument with a tensor isnan(Optional-Default:Zero-Type:int,floatorbool). - The 3rd argument with
torchor the 2nd argument with a tensor isposinf(Optional-Default:The greatest finite-Type:int,floatorbool). - The 4th argument with
torchor the 2nd argument with a tensor isneginf(Optional-Default:The lowest finite-Type:int,floatorbool). - There is
outargument withtorch(Optional-Default:None-Type:tensor): *Memos:-
out=must be used. -
My post explains
outargument.
-
import torch
my_tensor = torch.tensor([-torch.inf, 7., -5., torch.inf,
8., torch.nan, torch.inf, torch.nan])
torch.nan_to_num(input=my_tensor)
my_tensor.nan_to_num()
# tensor([-3.4028e+38, 7.0000e+00, -5.0000e+00, 3.4028e+38,
# 8.0000e+00, 0.0000e+00, 3.4028e+3, 0.0000e+00])
torch.nan_to_num(input=my_tensor, nan=2., posinf=-6., neginf=9.)
# tensor([9., 7., -5., -6., 8., 2., -6., 2.])
my_tensor = torch.tensor([[-torch.inf, 7., -5., torch.inf],
[8., torch.nan, torch.inf, torch.nan]])
torch.nan_to_num(input=my_tensor, nan=2., posinf=-6., neginf=9.)
# tensor([[9., 7., -5., -6.],
# [8., 2., -6., 2.]])
my_tensor = torch.tensor([[[-torch.inf, 7.], [-5., torch.inf]],
[[8., torch.nan], [torch.inf, torch.nan]]])
torch.nan_to_num(input=my_tensor, nan=2., posinf=-6., neginf=9.)
# tensor([[[9., 7.], [-5., -6.]],
# [[8., 2.], [-6., 2.]]])
my_tensor = torch.tensor([complex(-torch.inf, torch.inf), 7.+0.j,
-5.+0.j, complex(torch.inf, -torch.inf),
8.+0.j, complex(torch.nan, torch.nan),
complex(torch.inf), complex(torch.nan)])
torch.nan_to_num(input=my_tensor)
# tensor([-3.4028e+38+3.4028e+38j, 7.0000e+00+0.0000e+00j,
# -5.0000e+00+0.0000e+00j, 3.4028e+38-3.4028e+38j,
# 8.0000e+00+0.0000e+00j, 0.0000e+00+0.0000e+00j,
# 3.4028e+38+0.0000e+00j, 0.0000e+00+0.0000e+00j])
torch.nan_to_num(input=my_tensor, nan=2., posinf=-6., neginf=9.)
# tensor([9.-6.j, 7.+0.j,
# -5.+0.j, -6.+9.j,
# 8.+0.j, 2.+2.j,
# -6.+0.j, 2.+0.j])
my_tensor = torch.tensor([[complex(-torch.inf, torch.inf), 7.+0.j,
-5.+0.j, complex(torch.inf, -torch.inf)],
[8.+0.j, complex(torch.nan, torch.nan),
complex(torch.inf), complex(torch.nan)]])
torch.nan_to_num(input=my_tensor, nan=2., posinf=-6., neginf=9.)
# tensor([[9.-6.j, 7.+0.j,
# -5.+0.j, -6.+9.j],
# [8.+0.j, 2.+2.j,
# -6.+0.j, 2.+0.j]])
my_tensor = torch.tensor([[[complex(-torch.inf, torch.inf), 7.+0.j],
[-5.+0.j, complex(torch.inf, -torch.inf)]],
[[8.+0.j, complex(torch.nan, torch.nan)],
[complex(torch.inf), complex(torch.nan)]]])
torch.nan_to_num(input=my_tensor, nan=2., posinf=-6., neginf=9.)
# tensor([[[9.-6.j, 7.+0.j],
# [-5.+0.j, -6.+9.j]],
# [[8.+0.j, 2.+2.j],
# [-6.+0.j, 2.+0.j]]])
my_tensor = torch.tensor([[[0, 1], [2, 3]], [[4, 5], [6, 7]]])
torch.nan_to_num(input=my_tensor, nan=2, posinf=-6, neginf=9)
# tensor([[[0, 1], [2, 3]], [[4, 5], [6, 7]]])
my_tensor = torch.tensor([[[True, False], [True, False]],
[[False, True], [False, True]]])
torch.nan_to_num(input=my_tensor, nan=True, posinf=False, neginf=True)
# tensor([[[True, False], [True, False]],
# [[False, True], [False, True]]])
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