logical_xor() and logical_not() in PyTorch

This content originally appeared on DEV Community and was authored by Super Kai (Kazuya Ito)

*My post explains logical_and() and logical_or().

logical_xor() can do logical XOR with two of the 0D or more D tensors of zero or more elements, getting the 0D or more D tensor of zero or more boolean values as shown below:

*Memos:

logical_xor() can be used with torch or a tensor.
The 1st argument(input) with torch or using a tensor(Required-Type:tensor of int, float, complex or bool).
The 2nd argument with torch or the 1st argument with a tensor is other(Required-Type:tensor of int, float, complex or bool).
There is out argument with torch(Optional-Type:tensor): *Memos:
- out= must be used.
- My post explains out argument.
Zero is True and nonzero is False.

import torch

tensor1 = torch.tensor(True)
tensor2 = torch.tensor([[True, False, True, False],
                        [False, True, False, True]])
torch.logical_xor(input=tensor1, other=tensor2)
tensor1.logical_xor(other=tensor2)
torch.logical_xor(input=tensor2, other=tensor1)
# tensor([[False, True, False, True],
#         [True, False, True, False]])

tensor1 = torch.tensor(False)
tensor2 = torch.tensor([[True, False, True, False],
                        [False, True, False, True]])
torch.logical_xor(input=tensor1, other=tensor2)
torch.logical_xor(input=tensor2, other=tensor1)
# tensor([[True, False, True, False],
#         [False, True, False, True]])

tensor1 = torch.tensor([True, False])
tensor2 = torch.tensor([[[True, False], [True, False]],
                        [[False, True], [False, True]]])
torch.logical_xor(input=tensor1, other=tensor2)
torch.logical_xor(input=tensor2, other=tensor1)
# tensor([[[False, False], [False, False]],
#         [[True, True], [True, True]]])

tensor1 = torch.tensor([7, 0])
tensor2 = torch.tensor([[[1, 0], [-1, 0]],
                        [[0, 2], [0, -2]]])
torch.logical_xor(input=tensor1, other=tensor2)
torch.logical_xor(input=tensor2, other=tensor1)
# tensor([[[False, False], [False, False]],
#         [[True, True], [True, True]]])

tensor1 = torch.tensor([7.3, 0.3])
tensor2 = torch.tensor([[[1.3, 0.3], [-1.0, 0.0]],
                        [[0.3, 2.3], [0.0, -2.0]]])
torch.logical_xor(input=tensor1, other=tensor2)
torch.logical_xor(input=tensor2, other=tensor1)
# tensor([[[False, False], [False, True]],
#         [[False, False], [True, False]]])

tensor1 = torch.tensor([7.+3.j, 0.+3.j])
tensor2 = torch.tensor([[[1.+3.j, 0.+3.j], [-1.+0.j, 0.+0.j]],
                        [[0.+3.j, 2.+3.j], [0.+0.j, -2.+0.j]]])
torch.logical_xor(input=tensor1, other=tensor2)
torch.logical_xor(input=tensor2, other=tensor1)
# tensor([[[False, False], [False, True]],
#         [[False, False], [True, False]]])

logical_not() can do logical NOT with the 0D or more D tensor of zero or more elements, getting the 0D or more D tensor of zero or more boolean values as shown below:

*Memos:

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

import torch

my_tensor = torch.tensor(True)

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

my_tensor = torch.tensor([True, False, True, False])

torch.logical_not(input=my_tensor)
# tensor([False, True, False, True])

my_tensor = torch.tensor([[True, False, True, False],
                          [False, True, False, True]])
torch.logical_not(input=my_tensor)
# tensor([[False, True, False, True],
#         [True, False, True, False]])

my_tensor = torch.tensor([[[True, False], [True, False]],
                          [[False, True], [False, True]]])
torch.logical_not(input=my_tensor)
# tensor([[[False, True], [False, True]],
#         [[True, False], [True, False]]])

my_tensor = torch.tensor([[[1, 0], [-1, 0]],
                          [[0, 2], [0, -2]]])
torch.logical_not(input=my_tensor)
# tensor([[[False, True], [False, True]],
#         [[True, False], [True, False]]])

my_tensor = torch.tensor([[[1.3, 0.3], [-1.0, 0.0]],
                          [[0.3, 2.3], [0.0, -2.0]]])
torch.logical_not(input=my_tensor)
# tensor([[[False, False], [False, True]],
#         [[False, False], [ True, False]]])

my_tensor  = torch.tensor([[[1.+3.j, 0.+3.j], [-1.+0.j, 0.+0.j]],
                           [[0.+3.j, 2.+3.j], [0.+0.j, -2.+0.j]]])
torch.logical_not(input=my_tensor)
# tensor([[[False, False], [False, True]],
#         [[False, False], [True, False]]])

This content originally appeared on DEV Community and was authored by Super Kai (Kazuya Ito)

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