positive() can just get the same tensor as the input tensor which is the 0D or more D tensor of zero or more elements as shown below:
*Memos:
-
positive()
can be used with torch or a tensor. - The 1st argument(
input
) withtorch
or using a tensor(Required-Type:tensor
ofint
,float
orcomplex
).
import torch
my_tensor = torch.tensor(7)
torch.positive(input=my_tensor)
my_tensor.positive()
# tensor(7)
my_tensor = torch.tensor([7, -1, 5, -7, -9, -3, 0, 6])
torch.positive(input=my_tensor)
# tensor([7, -1, 5, -7, -9, -3, 0, 6])
my_tensor = torch.tensor([[7, -1, 5, -7],
[-9, -3, 0, 6]])
torch.positive(input=my_tensor)
# tensor([[7, -1, 5, -7],
# [-9, -3, 0, 6]])
my_tensor = torch.tensor([[[7, -1], [5, -7]],
[[-9, -3], [0, 6]]])
torch.positive(input=my_tensor)
# tensor([[[7, -1], [5, -7]],
# [[-9, -3], [0, 6]]])
my_tensor = torch.tensor([[[7., -1.], [5., -7.]],
[[-9., -3.], [0., 6.]]])
torch.positive(input=my_tensor)
# tensor([[[7., -1.], [5., -7.]],
# [[-9., -3.], [0., 6.]]])
my_tensor = torch.tensor([[[7.+0.j, -1.+0.j], [5.+0.j, -7.+0.j]],
[[-9.+0.j, -3.+0.j], [0.+0.j, 6.+0.j]]])
torch.positive(input=my_tensor)
# tensor([[[7.+0.j, -1.+0.j],
# [5.+0.j, -7.+0.j]],
# [[-9.+0.j, -3.+0.j],
# [0.+0.j, 6.+0.j]]])
neg() can get the 0D or more D tensor of the zero or more elements changed from +
to -
and from -
to +
from the 0D or more D tensor of zero or more elements as shown below:
*Memos:
-
neg()
can be used withtorch
or a tensor. - The 1st argument(
input
) withtorch
or using a tensor(Required-Type:tensor
ofint
,float
orcomplex
). - There is
out
argument withtorch
(Optional-Default:None
-Type:tensor
): *Memos:-
out=
must be used. -
My post explains
out
argument.
-
-
negative() is the alias of
neg()
.
import torch
my_tensor = torch.tensor(7)
torch.neg(input=my_tensor)
my_tensor.neg()
# tensor(-7)
my_tensor = torch.tensor([7, -1, 5, -7, -9, -3, 0, 6])
torch.neg(input=my_tensor)
# tensor([-7, 1, -5, 7, 9, 3, 0, -6])
my_tensor = torch.tensor([[7, -1, 5, -7],
[-9, -3, 0, 6]])
torch.neg(input=my_tensor)
# tensor([[-7, 1, -5, 7],
# [9, 3, 0, -6]])
my_tensor = torch.tensor([[[7, -1], [5, -7]],
[[-9, -3], [0, 6]]])
torch.neg(input=my_tensor)
# tensor([[[-7, 1], [-5, 7]],
# [[9, 3], [0, -6]]])
my_tensor = torch.tensor([[[7., -1.], [5., -7.]],
[[-9., -3.], [0., 6.]]])
torch.neg(input=my_tensor)
# tensor([[[-7., 1.], [-5., 7.]],
# [[9., 3.], [-0., -6.]]])
my_tensor = torch.tensor([[[7.+0.j, -1.+0.j], [5.+0.j, -7.+0.j]],
[[-9.+0.j, -3.+0.j], [0.+0.j, 6.+0.j]]])
torch.neg(input=my_tensor)
# tensor([[[-7.+0.j, 1.+0.j], [-5.+0.j, 7.+0.j]],
# [[9.+0.j, 3.+0.j], [0.+0.j, -6.+0.j]]])
copysign() can get the 0D or more D tensor of the zero or more floating point numbers changed +
and -
by other
tensor from two of 0D or more D tensors as shown below:
*Memos:
-
copysign()
can be used withtorch
or a tensor. - The 1st argument(
input
) withtorch
or using a tensor(Required-Type:tensor
ofint
,float
,bool
). - The 2st argument with
torch
or the 1st argument isother
(Required-Type:tensor
orscalar
ofint
,float
orbool
). *The sign(+
or-
) is applied to the returned tensor. - There is
out
argument withtorch
(Optional-Default:None
-Type:tensor
): *Memos:-
out=
must be used. -
My post explains
out
argument.
-
import torch
tensor1 = torch.tensor([[7., -1., 5., -7.],
[-9., -3., 0., 6.]])
tensor2 = torch.tensor([-1., 0., 1., 2.])
torch.copysign(input=tensor1, other=tensor2)
tensor1.copysign(other=tensor2)
# tensor([[-7., 1., 5., 7.],
# [-9., 3., 0., 6.]])
torch.copysign(input=tensor1, other=2.)
# tensor([[7., 1., 5., 7.],
# [9., 3., 0., 6.]])
tensor1 = torch.tensor([[[7., -1.], [5., -7.]],
[[-9., -3.], [0., 6.]]])
tensor2 = torch.tensor(-1.)
torch.copysign(input=tensor1, other=tensor2)
# tensor([[[-7., -1.], [-5., -7.]],
# [[-9., -3.], [-0., -6.]]])
torch.copysign(input=tensor1, other=2.)
# tensor([[[7., 1.], [5., 7.]],
# [[9., 3.], [0., 6.]]])
tensor1 = torch.tensor([[[7, -1], [5, -7]],
[[-9, -3], [0, 6]]])
tensor2 = torch.tensor(-1)
torch.copysign(input=tensor1, other=tensor2)
torch.copysign(input=tensor1, other=-1)
# tensor([[[-7., -1.], [-5., -7.]],
# [[-9., -3.], [-0., -6.]]])
torch.copysign(input=tensor1, other=2)
# tensor([[[7., 1.], [5., 7.]],
# [[9., 3.], [0., 6.]]])
tensor1 = torch.tensor([[[True, False], [True, False]],
[[False, True], [False, True]]])
tensor2 = torch.tensor(True)
torch.copysign(input=tensor1, other=tensor2)
torch.copysign(input=tensor1, other=False)
# tensor([[[1., 0.], [1., 0.]],
# [[0., 1.], [0., 1.]]])
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