*Memos:
- My post explains gt() and lt().
- My post explains ge() and le().
- My post explains isclose() and equal().
eq() can check if the zero or more elements of the 1st 0D or more D tensor are equal to the zero or more elements of the 2nd 0D or more D tensor element-wise, getting the 0D or more D tensor of zero or more elements as shown below:
*Memos:
-
eq()
can be used with torch or a tensor. - The 1st argument(
input
) withtorch
or using a tensor(Required-Type:tensor
ofint
,float
,complex
orbool
). - The 2nd argument with
torch
or the 1st argument with a tensor isother
(Required-Type:tensor
orscalar
ofint
,float
,complex
orbool
). - There is
out
argument withtorch
(Optional-Default:None
-Type:tensor
): *Memos:-
out=
must be used. -
My post explains
out
argument.
-
- The result is the higher D tensor which has more elements.
import torch
tensor1 = torch.tensor([5, 0, 3])
tensor2 = torch.tensor([7, 0, 3])
torch.eq(input=tensor1, other=tensor2)
tensor1.eq(other=tensor2)
torch.eq(input=tensor2, other=tensor1)
# tensor([False, True, True])
tensor1 = torch.tensor(5)
tensor2 = torch.tensor([[3, 5, 4],
[6, 3, 5]])
torch.eq(input=tensor1, other=tensor2)
torch.eq(input=tensor2, other=tensor1)
# tensor([[False, True, False],
# [False, False, True]])
torch.eq(input=tensor1, other=3)
# tensor(False)
torch.eq(input=tensor2, other=3)
# tensor([[True, False, False],
# [False, True, False]])
tensor1 = torch.tensor([5, 0, 3])
tensor2 = torch.tensor([[5, 5, 5],
[0, 0, 0],
[3, 3, 3]])
torch.eq(input=tensor1, other=tensor2)
torch.eq(input=tensor2, other=tensor1)
# tensor([[True, False, False],
# [False, True, False],
# [False, False, True]])
torch.eq(input=tensor1, other=3)
# tensor([False, False, True])
torch.eq(input=tensor2, other=3)
# tensor([[False, False, False],
# [False, False, False],
# [True, True, True]])
tensor1 = torch.tensor([5., 0., 3.])
tensor2 = torch.tensor([[5., 5., 5.],
[0., 0., 0.],
[3., 3., 3.]])
torch.eq(input=tensor1, other=tensor2)
# tensor([[True, False, False],
# [False, True, False],
# [False, False, True]])
torch.eq(input=tensor1, other=3.)
# tensor([False, False, True])
tensor1 = torch.tensor([5.+0.j, 0.+0.j, 3.+0.j])
tensor2 = torch.tensor([[5.+0.j, 5.+0.j, 5.+0.j],
[0.+0.j, 0.+0.j, 0.+0.j],
[3.+0.j, 3.+0.j, 3.+.0j]])
torch.eq(input=tensor1, other=tensor2)
# tensor([[True, False, False],
# [False, True, False],
# [False, False, True]])
torch.eq(input=tensor1, other=3.+0.j)
# tensor([False, False, True])
tensor1 = torch.tensor([True, False, True])
tensor2 = torch.tensor([[True, False, True],
[False, True, False],
[True, False, True]])
torch.eq(input=tensor1, other=tensor2)
# tensor([[True, True, True],
# [False, False, False],
# [True, True, True]])
torch.eq(input=tensor1, other=True)
# tensor([True, False, True])
ne() can check if the zero or more elements of the 1st 0D or more D tensor are not equal to the zero or more elements of the 2nd 0D or more D tensor element-wise, getting the 0D or more D tensor of zero or more elements as shown below:
*Memos:
-
ne()
can be used withtorch
or a tensor. - The 1st argument(
input
) withtorch
or using a tensor(Required-Type:tensor
ofint
,float
,complex
orbool
). - The 2nd argument with
torch
or the 1st argument with a tensor isother
(Required-Type:tensor
orscalar
ofint
,float
,complex
orbool
). - There is
out
argument withtorch
(Optional-Default:None
-Type:tensor
): *Memos:-
out=
must be used. -
My post explains
out
argument.
-
-
not_equal() is the alias of
ne()
.
import torch
tensor1 = torch.tensor([5, 0, 3])
tensor2 = torch.tensor([7, 0, 3])
torch.ne(input=tensor1, other=tensor2)
tensor1.ne(other=tensor2)
torch.ne(input=tensor2, other=tensor1)
# tensor([True, False, False])
tensor1 = torch.tensor(5)
tensor2 = torch.tensor([[3, 5, 4],
[6, 3, 5]])
torch.ne(input=tensor1, other=tensor2)
torch.ne(input=tensor2, other=tensor1)
# tensor([[True, False, True],
# [True, True, False]])
torch.ne(input=tensor1, other=3)
# tensor(True)
torch.ne(input=tensor2, other=3)
# tensor([[False, True, True],
# [True, False, True]])
tensor1 = torch.tensor([5, 0, 3])
tensor2 = torch.tensor([[5, 5, 5],
[0, 0, 0],
[3, 3, 3]])
torch.ne(input=tensor1, other=tensor2)
torch.ne(input=tensor2, other=tensor1)
# tensor([[False, True, True],
# [True, False, True],
# [True, True, False]])
torch.ne(input=tensor1, other=3)
# tensor([True, True, False])
torch.ne(input=tensor2, other=3)
# tensor([[True, True, True],
# [True, True, True],
# [False, False, False]])
tensor1 = torch.tensor([5., 0., 3.])
tensor2 = torch.tensor([[5., 5., 5.],
[0., 0., 0.],
[3., 3., 3.]])
torch.ne(input=tensor1, other=tensor2)
# tensor([[False, True, True],
# [True, False, True],
# [True, True, False]])
torch.ne(input=tensor1, other=3.)
# tensor([True, True, False])
tensor1 = torch.tensor([5.+0.j, 0.+0.j, 3.+0.j])
tensor2 = torch.tensor([[5.+0.j, 5.+0.j, 5.+0.j],
[0.+0.j, 0.+0.j, 0.+0.j],
[3.+0.j, 3.+0.j, 3.+.0j]])
torch.ne(input=tensor1, other=tensor2)
# tensor([[False, True, True],
# [True, False, True],
# [True, True, False]])
torch.ne(input=tensor1, other=3.+0.j)
# tensor([True, True, False])
tensor1 = torch.tensor([True, False, True])
tensor2 = torch.tensor([[True, False, True],
[False, True, False],
[True, False, True]])
torch.ne(input=tensor1, other=tensor2)
# tensor([[False, False, False],
# [True, True, True],
# [False, False, False]])
torch.ne(input=tensor1, other=True)
# tensor([False, True, False])
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