*Memos:
ge() can check the zero or more elements of the 1st 0D or more D tensor are greater than or 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:
-
ge()
can be used with torch or a tensor. - The 1st argument(
input
) withtorch
or using a tensor(Required-Type:tensor
ofint
,float
orbool
). - The 2nd argument with
torch
or the 1st argument with a tensor isother
(Required-Type:tensor
orscalar
ofint
,float
orbool
). - There is
out
argument withtorch
(Optional-Type:tensor
): *Memos:-
out=
must be used. -
My post explains
out
argument.
-
-
greater_equal() is the alias of
ge()
.
import torch
tensor1 = torch.tensor([5, 0, 3])
tensor2 = torch.tensor([[3, 5, 4],
[6, 3, 5]])
torch.ge(input=tensor1, other=tensor2)
tensor1.ge(other=tensor2)
# tensor([[True, False, False],
# [False, False, False]])
torch.ge(input=tensor2, other=tensor1)
# tensor([[False, True, True],
# [True, True, True]])
torch.ge(input=tensor1, other=3)
# tensor([True, False, True])
torch.ge(input=tensor2, other=3)
# tensor([[True, True, True],
# [True, True, True]])
tensor1 = torch.tensor([5., 0., 3.])
tensor2 = torch.tensor([[3., 5., 4.],
[6., 3., 5.]])
torch.ge(input=tensor1, other=tensor2)
# tensor([[True, False, False],
# [False, False, False]])
torch.ge(input=tensor1, other=3.)
# tensor([True, False, True])
tensor1 = torch.tensor([True, False, True])
tensor2 = torch.tensor([[True, False, True],
[False, True, False]])
torch.ge(input=tensor1, other=tensor2)
# tensor([[True, True, True],
# [True, False, True]])
torch.ge(input=tensor1, other=True)
# tensor([True, False, True])
le() can check the zero or more elements of the 1st 0D or more D tensor are less than or 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 element as shown below:
*Memos:
-
le()
can be used withtorch
or a tensor. - The 1st argument(
input
) withtorch
or using a tensor(Required-Type:tensor
ofint
,float
orbool
). - The 2nd argument with
torch
or the 1st argument with a tensor isother
(Required-Type:tensor
orscalar
ofint
,float
orbool
). - There is
out
argument withtorch
(Optional-Type:tensor
): *Memos:-
out=
must be used. -
My post explains
out
argument.
-
-
less_equal() is the alias of
le()
.
import torch
tensor1 = torch.tensor([5, 0, 3])
tensor2 = torch.tensor([[3, 5, 4],
[6, 3, 5]])
torch.le(input=tensor1, other=tensor2)
tensor1.le(other=tensor2)
# tensor([[False, True, True],
# [True, True, True]])
torch.le(input=tensor2, other=tensor1)
# tensor([[True, False, False],
# [False, False, False]])
torch.le(input=tensor1, other=3)
# tensor([False, True, True])
torch.le(input=tensor2, other=3)
# tensor([[True, False, False],
# [False, True, False]])
tensor1 = torch.tensor([5., 0., 3.])
tensor2 = torch.tensor([[3., 5., 4.],
[6., 3., 5.]])
torch.le(input=tensor1, other=tensor2)
# tensor([[False, True, True],
# [True, True, True]])
torch.le(input=tensor1, other=3.)
# tensor([False, True, True])
tensor1 = torch.tensor([True, False, True])
tensor2 = torch.tensor([[True, False, True],
[False, True, False]])
torch.le(input=tensor1, other=tensor2)
# tensor([[True, True, True],
# [False, True, False]])
torch.le(input=tensor1, other=True)
# tensor([True, True, True])
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