*Memos:

My post explains isfinite(), isreal() and isin().
My post explains is_floating_point(), is_complex() and is_nonzero().

isnan() can check if the zero or more elements of a 0D or more D tensor are NaN, getting the 0D or more D tensor of zero or more boolean values shown below:

*Memos:

isnan() can be used with torch or a tensor.
The 1st argument with torch or using a tensor is input(Required-Type:tensor of int, float, complex or bool).

import torch

my_tensor = torch.tensor(float('nan'))

torch.isnan(input=my_tensor)
my_tensor.isnan()
# tensor(True)

my_tensor = torch.tensor([float('nan'),
                          -5,
                          float('inf'),
                          8.,
                          float('-inf'),
                          3.+0.j,
                          3.+7.j,
                          True])
torch.isnan(input=my_tensor)
# tensor([True, False, False, False, False, False, False, False])

my_tensor = torch.tensor([[float('nan'), -5, float('inf'), 8.],
                          [float('-inf'), 3.+0.j, 3.+7.j, True]])
torch.isnan(input=my_tensor)
# tensor([[True, False, False, False],
#         [False, False, False, False]])

my_tensor = torch.tensor([[[float('nan'), -5], [float('inf'), 8.]],
                          [[float('-inf'), 3.+0.j], [3.+7.j, True]]])
torch.isnan(input=my_tensor)
# tensor([[[True, False], [False, False]],
#         [[False, False], [False, False]]])

isinf() can check if the zero or more elements of a 0D or more D tensor are infinity, getting the 0D or more D tensor of zero or more boolean values as shown below:

*Memos:

isinf() can be used with torch or a tensor.
The 1st argument with torch or using a tensor is input(Required-Type:tensor of int, float, complex or bool).

import torch

my_tensor = torch.tensor(float('nan'))

torch.isinf(input=my_tensor)
my_tensor.isinf()
# tensor(False)

my_tensor = torch.tensor([float('nan'),
                          -5,
                          float('inf'),
                          8.,
                          float('-inf'),
                          3.+0.j,
                          3.+7.j,
                          True])
torch.isinf(input=my_tensor)
# tensor([False, False, True, False, True, False, False, False])

my_tensor = torch.tensor([[float('nan'), -5, float('inf'), 8.],
                          [float('-inf'), 3.+0.j, 3.+7.j, True]])
torch.isinf(input=my_tensor)
# tensor([[False, False, True, False],
#         [True, False, False, False]])

my_tensor = torch.tensor([[[float('nan'), -5], [float('inf'), 8.]],
                          [[float('-inf'), 3.+0.j], [3.+7.j, True]]])
torch.isinf(input=my_tensor)
# tensor([[[False, False], [True, False]],
#         [[True, False], [False, False]]])

isposinf() can if check the zero or more elements of a 0D or more D tensor are positive infinity, getting the 0D or more D tensor of zero or more boolean values as shown below:

*Memos:

isposinf() can be used with torch or a tensor.
The 1st argument with torch or using a tensor is input(Required-Type:tensor of int, float or bool).
There is out argument with torch(Optional-Type:tensor): *Memos:
- out= must be used.
- My post explains out argument.

import torch

my_tensor = torch.tensor(float('nan'))

torch.isposinf(input=my_tensor)
my_tensor.isposinf()
# tensor(False)

my_tensor = torch.tensor([float('nan'),
                          -5,
                          float('inf'),
                          8.,
                          float('-inf'),
                          3.,
                          3.,
                          True])
torch.isposinf(input=my_tensor)
# tensor([False, False, True, False, False, False, False, False])

my_tensor = torch.tensor([[float('nan'), -5, float('inf'), 8.],
                          [float('-inf'), 3., 3., True]])
torch.isposinf(input=my_tensor)
# tensor([[False, False, True, False],
#         [False, False, False, False]])

my_tensor = torch.tensor([[[float('nan'), -5], [float('inf'), 8.]],
                          [[float('-inf'), 3.], [3., True]]])
torch.isposinf(input=my_tensor)
# tensor([[[False, False], [True, False]],
#         [[False, False], [False, False]]])

isneginf() can if check the zero or more elements of a 0D or more D tensor are negative infinity, getting the 0D or more D tensor of zero or more boolean values as shown below:

*Memos:

isneginf() can be used with torch or a tensor.
The 1st argument with torch or using a tensor is input(Required-Type:tensor of int, float or bool).
There is out argument with torch(Optional-Type:tensor): *Memos:
- out= must be used.
- My post explains out argument.

import torch

my_tensor = torch.tensor(float('nan'))

torch.isneginf(input=my_tensor)
my_tensor.isneginf()
# tensor(False)

my_tensor = torch.tensor([float('nan'),
                          -5,
                          float('inf'),
                          8.,
                          float('-inf'),
                          3.,
                          3.,
                          True])
torch.isneginf(input=my_tensor)
# tensor([False, False, False, False, True, False, False, False])

my_tensor = torch.tensor([[float('nan'), -5, float('inf'), 8.],
                          [float('-inf'), 3., 3., True]])
torch.isneginf(input=my_tensor)
# tensor([[False, False, False, False],
#         [True, False, False, False]])

my_tensor = torch.tensor([[[float('nan'), -5], [float('inf'), 8.]],
                          [[float('-inf'), 3.], [3., True]]])
torch.isneginf(input=my_tensor)
# tensor([[[False, False], [False, False]],
#         [[True, False], [False, False]]])

isnan(), isinf(), isposinf() and isneginf() in PyTorch