square(), pow() and float_power() in PyTorch

Super Kai (Kazuya Ito) - May 9 - - Dev Community

square() can square the zero or more elements of a 0D or more D tensor, 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 with torch or using a tensor is input(Required-Type:tensor of int, float, complex or bool).
  • There is out argument with torch(Optional-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]]])
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pow() can get the 0D or more D tensor of the zero or more powers from the zero or more elements of two of 0D or more D tensors or a 0D or more D tensor and a scalar as shown below:

*Memos:

  • pow() can be used with torch or a tensor.
  • The 1st argument with torch(tensor or scalar of int, float or complex) or using a tensor(tensor of int, float or complex) is input(Required). *torch must use a scalar without input=.
  • The 2nd argument with torch or the 1st argument with a tensor is exponent(Required-Type:tensor or scalar of int, float or complex). *A negative scalar cannot be used.
  • There is out argument with torch(Optional-Type:tensor): *Memos:
    • out= must be used.
    • My post explains out argument.
  • The combination of a scalar(input) and a scalar(exponent) cannot be used.
  • The combination of a tensor(input(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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float_power() can get the 0D or more D tensor of the zero or more powers of float or complex from the zero or more elements of two of 0D or more D tensors or a 0D or more D tensor and a scalar as shown below:

*Memos:

  • float_power() can be used with torch or a tensor.
  • The 1st argument with torch(tensor or scalar of int, float, complex or bool) or using a tensor(tensor of int, float, complex or bool) is input(Required). *torch must use a scalar without input=.
  • The 2nd argument with torch or the 1st argument with a tensor is exponent(Required-Type:tensor or scalar of int, float, complex or bool).
  • There is out argument with torch(Optional-Type:tensor): *Memos:
    • out= must be used.
    • My post explains out argument.
  • 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(-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)

torch.float_power(input=tensor1, exponent=-3.)
# tensor(-0.0370, 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(-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)

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)

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(-3., exponent=tensor2)
# tensor([1., -3., 9., -27.], 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)

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(-3., exponent=tensor2)
# tensor([9., -27.], 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)

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(-3, exponent=tensor2)
# tensor([9., -27.], 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)

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(-3.+0.j, exponent=tensor2)
# tensor([9.0000-2.2044e-15j, -27.0000+9.9196e-15j],
#        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)

tensor1 = torch.tensor([[[True, False], [True, False]],
                         [[False, True], [False, True]]])
tensor2 = torch.tensor([True, False])

torch.float_power(input=tensor1, exponent=tensor2)
# tensor([[[1., 1.], [1., 1.]],
#         [[0., 1.], [0., 1.]]], dtype=torch.float64)

torch.float_power(True, exponent=tensor2)
# tensor([1., 1.], dtype=torch.float64)

torch.float_power(input=tensor1, exponent=True)
# tensor([[[1., 0.], [1., 0.]],
#         [[0., 1.], [0., 1.]]], dtype=torch.float64)
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