square() can square the zero or more values of a 0D or more D tensor as shown below:
*Memos:
-
square()
can be used with torch or a tensor. - The 1st argument(
tensor
ofint
,float
,complex
orbool
) withtorch
or using a tensor(tensor
ofint
,float
,complex
orbool
) isinput
(Required).
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 zero or more powers with the zero or more elements of 2 tensors as shown below:
*Memos:
-
pow()
can be used withtorch
or a tensor. - The 1st argument(
tensor
ofint
,float
,complex
orbool
) withtorch
or using a tensor(tensor
ofint
,float
,complex
orbool
) isinput
(Required). *torch
must use a scalar withoutinput=
. - The 2nd argument(
tensor
ofint
,float
orcomplex
orscalar
ofint
,float
,complex
orbool
) withtorch
or the 1st argument(tensor
ofint
,float
orcomplex
orscalar
ofint
,float
,complex
orbool
) isexponent
(Required). *A negative scalar cannot be used. - The combination of a scalar(
input
) and a scalar(exponent
) cannot be used.
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(input=tensor1, exponent=3)
# tensor(-27)
torch.pow(-3, exponent=tensor2)
# tensor([0, 0, 0, 0, 1, -3, 9, -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(input=tensor1, exponent=3)
# tensor([-27, 1, -8, 27, 125, -125, 0, -64])
torch.pow(-3, exponent=tensor2)
# tensor([0, 0, 0, 0, 1, -3, 9, -27])
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(input=tensor1, exponent=3)
# tensor([[-27, 1, -8, 27], [125, -125, 0, -64]])
torch.pow(-3, exponent=tensor2)
# tensor([1, -3, 9, -27])
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(input=tensor1, exponent=3)
# tensor([[[-27, 1], [-8, 27]],
# [[125, -125], [0, -64]]])
torch.pow(-3, exponent=tensor2)
# tensor([9, -27])
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(input=tensor1, exponent=3.)
# tensor([[[-27., 1.], [-8., 27.]],
# [[125., -125.], [0., -64.]]])
torch.pow(-3., exponent=tensor2)
# tensor([9., -27.])
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(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]]])
torch.pow(-3.+0.j, exponent=tensor2)
# tensor([9.0000+1.5736e-06j, -27.0000-6.4394e-07j])
my_tensor = torch.tensor([[[False, True], [False, True]],
[[False, True], [False, True]]])
torch.pow(input=my_tensor, exponent=False)
# tensor([[[True, True], [True, True]],
# [[True, True], [True, True]]])
float_power() can get the zero or more powers of float
or complex
with the zero or more elements of 2 tensors as shown below:
*Memos:
-
float_power()
can be used withtorch
or a tensor. - The 1st argument(
tensor
ofint
,float
,complex
orbool
) withtorch
or using a tensor(tensor
ofint
,float
,complex
orbool
) isinput
(Required). *torch
must use a scalar withoutinput=
. - The 2nd argument(
tensor
ofint
,float
orcomplex
orscalar
ofint
,float
,complex
orbool
) withtorch
or the 1st argument(tensor
ofint
,float
orcomplex
orscalar
ofint
,float
,complex
orbool
) isexponent
(Required). - The combination of a scalar(
input
) and a scalar(exponent
) cannot be used.
import torch
tensor1 = torch.tensor(-3.)
tensor2 = torch.tensor([-4., -3., -2., -1., 0., 1., 2., 3.])
torch.float_power(input=tensor1, exponent=tensor2)
tensor1.float_power(exponent=tensor2)
# tensor([1.2346e-02, -3.7037e-02, 1.1111e-01, -3.3333e-01,
# 1.0000e+00, -3.0000e+00, 9.0000e+00, -2.7000e+01],
# dtype=torch.float64)
torch.float_power(input=tensor1, exponent=-3.)
# tensor(-0.0370, dtype=torch.float64)
torch.float_power(-3., exponent=tensor2)
# tensor([1.2346e-02, -3.7037e-02, 1.1111e-01, -3.3333e-01,
# 1.0000e+00, -3.0000e+00, 9.0000e+00, -2.7000e+01],
# dtype=torch.float64)
tensor1 = torch.tensor([-3., 1., -2., 3., 5., -5., 0., -4.])
tensor2 = torch.tensor([-4., -3., -2., -1., 0., 1., 2., 3.])
torch.float_power(input=tensor1, exponent=tensor2)
# tensor([1.2346e-02, 1.0000e+00, 2.5000e-01, 3.3333e-01,
# 1.0000e+00, -5.0000e+00, 0.0000e+00, -6.4000e+01],
# dtype=torch.float64)
torch.float_power(input=tensor1, exponent=-3.)
# tensor([-0.0370, 1.0000, -0.1250, 0.0370,
# 0.0080, -0.0080, inf, -0.0156], dtype=torch.float64)
torch.float_power(-3., exponent=tensor2)
# tensor([1.2346e-02, -3.7037e-02, 1.1111e-01, -3.3333e-01,
# 1.0000e+00, -3.0000e+00, 9.0000e+00, -2.7000e+01],
# dtype=torch.float64)
tensor1 = torch.tensor([[-3., 1., -2., 3.], [5., -5., 0., -4.]])
tensor2 = torch.tensor([0., 1., 2., 3.])
torch.float_power(input=tensor1, exponent=tensor2)
# tensor([[1., 1., 4., 27.], [1., -5., 0., -64.]],
# dtype=torch.float64)
torch.float_power(input=tensor1, exponent=-3.)
# tensor([[-0.0370, 1.0000, -0.1250, 0.0370],
# [0.0080, -0.0080, inf, -0.0156]],
# dtype=torch.float64)
torch.float_power(-3., exponent=tensor2)
# tensor([1., -3., 9., -27.], dtype=torch.float64)
tensor1 = torch.tensor([[[-3., 1.], [-2., 3.]],
[[5., -5.], [0., -4.]]])
tensor2 = torch.tensor([2., 3.])
torch.float_power(input=tensor1, exponent=tensor2)
# tensor([[[9., 1.], [4., 27.]],
# [[25., -125.], [0., -64.]]], dtype=torch.float64)
torch.float_power(input=tensor1, exponent=-3.)
# tensor([[[-0.0370, 1.0000], [-0.1250, 0.0370]],
# [[0.0080, -0.0080], [inf, -0.0156]]],
# dtype=torch.float64)
torch.float_power(-3., exponent=tensor2)
# tensor([9., -27.], dtype=torch.float64)
tensor1 = torch.tensor([[[-3, 1], [-2, 3]],
[[5, -5], [0, -4]]])
tensor2 = torch.tensor([2, 3])
torch.float_power(input=tensor1, exponent=tensor2)
# tensor([[[9., 1.], [4., 27.]],
# [[25., -125.], [0., -64.]]], dtype=torch.float64)
torch.float_power(input=tensor1, exponent=-3)
# tensor([[[-0.0370, 1.0000], [-0.1250, 0.0370]],
# [[0.0080, -0.0080], [inf, -0.0156]]],
# dtype=torch.float64)
torch.float_power(-3, exponent=tensor2)
# tensor([9., -27.], dtype=torch.float64)
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.float_power(input=tensor1, exponent=tensor2)
# tensor([[[9.0000-2.2044e-15j, 1.0000+0.0000e+00j],
# [4.0000-9.7972e-16j, 27.0000+0.0000e+00j]],
# [[25.0000+0.0000e+00j, -125.0000+4.5924e-14j],
# [0.0000-0.0000e+00j, -64.0000+2.3513e-14j]]],
# dtype=torch.complex128)
torch.float_power(input=tensor1, exponent=-3.+0.j)
# tensor([[[-0.0370-1.3607e-17j, 1.0000+0.0000e+00j],
# [-0.1250-4.5924e-17j, 0.0370+0.0000e+00j]],
# [[0.0080+0.0000e+00j, -0.0080-2.9392e-18j],
# [inf+nanj, -0.0156-5.7405e-18j]]],
# dtype=torch.complex128)
torch.float_power(-3.+0.j, exponent=tensor2)
# tensor([9.0000-2.2044e-15j, -27.0000+9.9196e-15j],
# dtype=torch.complex128)
my_tensor = torch.tensor([[[False, True], [False, True]],
[[False, True], [False, True]]])
torch.float_power(input=my_tensor, exponent=False)
# tensor([[[1., 1.], [1., 1.]],
# [[1., 1.], [1., 1.]]], dtype=torch.float64)
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