*My post explains float_power().
square() can get the 0D or more D tensor of squared zero or more elements, getting the 0D or more D tensor of zero or more elements as shown below:
*Memos:
-
square()
can be used with torch or a tensor. - The 1st argument(
input
) withtorch
or using a tensor(Required-Type:tensor
ofint
,float
,complex
orbool
). - There is
out
argument withtorch
(Optional-Default:None
-Type:tensor
): *Memos:-
out=
must be used. -
My post explains
out
argument.
-
import torch
my_tensor = torch.tensor(-3)
torch.square(input=my_tensor)
my_tensor.square()
# tensor(9)
my_tensor = torch.tensor([-3, 1, -2, 3, 5, -5, 0, -4])
torch.square(input=my_tensor)
# tensor([9, 1, 4, 9, 25, 25, 0, 16])
my_tensor = torch.tensor([[-3, 1, -2, 3],
[5, -5, 0, -4]])
torch.square(input=my_tensor)
# tensor([[9, 1, 4, 9],
# [25, 25, 0, 16]])
my_tensor = torch.tensor([[[-3, 1], [-2, 3]],
[[5, -5], [0, -4]]])
torch.square(input=my_tensor)
# tensor([[[9, 1], [4, 9]],
# [[25, 25], [0, 16]]])
my_tensor = torch.tensor([[[-3., 1.], [-2., 3.]],
[[5., -5.], [0., -4.]]])
torch.square(input=my_tensor)
# tensor([[[9., 1.], [4., 9.]],
# [[25., 25.], [0., 16.]]])
my_tensor = torch.tensor([[[-3.+0.j, 1.+0.j], [-2.+0.j, 3.+0.j]],
[[5.+0.j, -5.+0.j], [0.+0.j, -4.+0.j]]])
torch.square(input=my_tensor)
# tensor([[[9.-0.j, 1.+0.j], [4.-0.j, 9.+0.j]],
# [[25.+0.j, 25.-0.j], [0.+0.j, 16.-0.j]]])
my_tensor = torch.tensor([[[True, False], [True, False]],
[[False, True], [False, True]]])
torch.square(input=my_tensor)
# tensor([[[1, 0], [1, 0]],
# [[0, 1], [0, 1]]])
pow() can get the 0D or more D tensor of zero or more powers from two of the 0D or more D tensors of zero or more elements or the 0D or more D tensor of zero or more elements and a scalar as shown below:
*Memos:
-
pow()
can be used withtorch
or a tensor. - The 1st argument(
input
) withtorch
(Required-Type:tensor
orscalar
ofint
,float
orcomplex
) or using a tensor(Required-Type:tensor
ofint
,float
orcomplex
). *torch
must use a scalar withoutinput=
. - The 2nd argument with
torch
or the 1st argument with a tensor isexponent
(Required-Type:tensor
orscalar
ofint
,float
orcomplex
). *A negative scalar cannot be used. - There is
out
argument withtorch
(Optional-Default:None
-Type:tensor
): *Memos:-
out=
must be used. -
My post explains
out
argument.
-
- The combination of a scalar(
input
or a tensor) and a scalar(exponent
) cannot be used. - The combination of a tensor(
input
(bool
) or a tensor(bool
)) and a scalar(exponent
(bool
)) works.
import torch
tensor1 = torch.tensor(-3)
tensor2 = torch.tensor([-4, -3, -2, -1, 0, 1, 2, 3])
torch.pow(input=tensor1, exponent=tensor2)
tensor1.pow(exponent=tensor2)
# tensor([0, 0, 0, 0, 1, -3, 9, -27])
torch.pow(-3, exponent=tensor2)
# tensor([0, 0, 0, 0, 1, -3, 9, -27])
torch.pow(input=tensor1, exponent=3)
# tensor(-27)
tensor1 = torch.tensor([-3, 1, -2, 3, 5, -5, 0, -4])
tensor2 = torch.tensor([-4, -3, -2, -1, 0, 1, 2, 3])
torch.pow(input=tensor1, exponent=tensor2)
# tensor([0, 1, 0, 0, 1, -5, 0, -64])
torch.pow(-3, exponent=tensor2)
# tensor([0, 0, 0, 0, 1, -3, 9, -27])
torch.pow(input=tensor1, exponent=3)
# tensor([-27, 1, -8, 27, 125, -125, 0, -64])
tensor1 = torch.tensor([[-3, 1, -2, 3], [5, -5, 0, -4]])
tensor2 = torch.tensor([0, 1, 2, 3])
torch.pow(input=tensor1, exponent=tensor2)
# tensor([[1, 1, 4, 27], [1, -5, 0, -64]])
torch.pow(-3, exponent=tensor2)
# tensor([1, -3, 9, -27])
torch.pow(input=tensor1, exponent=3)
# tensor([[-27, 1, -8, 27], [125, -125, 0, -64]])
tensor1 = torch.tensor([[[-3, 1], [-2, 3]],
[[5, -5], [0, -4]]])
tensor2 = torch.tensor([2, 3])
torch.pow(input=tensor1, exponent=tensor2)
# tensor([[[9, 1], [4, 27]],
# [[25, -125], [0, -64]]])
torch.pow(-3, exponent=tensor2)
# tensor([9, -27])
torch.pow(input=tensor1, exponent=3)
# tensor([[[-27, 1], [-8, 27]],
# [[125, -125], [0, -64]]])
tensor1 = torch.tensor([[[-3., 1.], [-2., 3.]],
[[5., -5.], [0., -4.]]])
tensor2 = torch.tensor([2., 3.])
torch.pow(input=tensor1, exponent=tensor2)
# tensor([[[9., 1.], [4., 27.]],
# [[25., -125.], [0., -64.]]])
torch.pow(-3., exponent=tensor2)
# tensor([9., -27.])
torch.pow(input=tensor1, exponent=3.)
# tensor([[[-27., 1.], [-8., 27.]],
# [[125., -125.], [0., -64.]]])
tensor1 = torch.tensor([[[-3.+0.j, 1.+0.j], [-2.+0.j, 3.+0.j]],
[[5.+0.j, -5.+0.j], [0.+0.j, -4.+0.j]]])
tensor2 = torch.tensor([2.+0.j, 3.+0.j])
torch.pow(input=tensor1, exponent=tensor2)
# tensor([[[9.0000+1.5736e-06j, 1.0000+0.0000e+00j],
# [4.0000+6.9938e-07j, 27.0000+0.0000e+00j]],
# [[25.0000+0.0000e+00j, -125.0000-2.9812e-06j],
# [0.0000-0.0000e+00j, -64.0000-1.5264e-06j]]])
torch.pow(-3.+0.j, exponent=tensor2)
# tensor([9.0000+1.5736e-06j, -27.0000-6.4394e-07j])
torch.pow(input=tensor1, exponent=3.+0.j)
# tensor([[[-27.+0.j, 1.+0.j],
# [-8.+0.j, 27.+0.j]],
# [[125.+0.j, -125.+0.j],
# [0.+0.j, -64.+0.j]]])
my_tensor = torch.tensor([[[True, False], [True, False]],
[[False, True], [False, True]]])
torch.pow(input=my_tensor, exponent=True)
# tensor([[[True, False], [True, False]],
# [[False, True], [False, True]]])
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