float_power in PyTorch

#python #pytorch #floatpower #power

float_power() can get the 0D or more D tensor of the zero or more powers of float or complex 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:

float_power() can be used with torch or a tensor.
The 1st argument(input) with torch(Type:tensor or scalar of int, float, complex or bool) or using a tensor(Type:tensor of int, float, complex or bool)(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-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.

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)

DEV Community

float_power in PyTorch

Top comments (0)

Read next

Python code that generates a stock price chart for the last n days.

The Integration of AI and Python: Revolutionizing the Developer's World

Multi-Agent System for 🚀 ANY AI/ML Model: 🌐 Web Scraping & 📝 Content Analysis Powered by the 🔗 AI/ML API

Why your FastAPI (or Flask) App performs poorly with high loads