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Python Pytorch logspace() method

  • Last Updated : 22 Apr, 2020

PyTorch is an open-source machine learning library developed by Facebook. It is used for deep neural network and natural language processing purposes.

The function torch.logspace() returns a one-dimensional tensor of steps points logarithmically spaced with base base between  {\text{base}}^{\text{start}}   and {\text{ base}}^{\text{end}} .

The output tensor is 1-D of size steps.

Syntax: torch.logspace(start, end, steps=100, base=10, out=None)

start: the starting value for the set of point.
end: the ending value for the set of points
steps: number of points to sample between start and end. Default: 100.
base: base of the logarithm function. Default: 10.0
out(Tensor, optional): the output tensor

Return type: A tensor

Code #1:

# Importing the PyTorch library
import torch
# Applying the logspace function and
# storing the resulting tensor in 't'
a = torch.logspace(3, 10, 5)
print("a = ", a)
b = torch.logspace(start =-10, end = 10, steps = 5)
print("b = ", b)


a =  tensor([1.0000e+03, 5.6234e+04, 3.1623e+06, 1.7783e+08, 1.0000e+10])
b =  tensor([1.0000e-10, 1.0000e-05, 1.0000e+00, 1.0000e+05, 1.0000e+10])


Code #2: Visualization

# Importing the PyTorch library
import torch
# Importing the NumPy library
import numpy as np
# Importing the matplotlib.pylot function
import matplotlib.pyplot as plt
# Applying the logspace function to get a tensor of size 15 with values from -5 to 5 using base 2
a = torch.logspace(-5, 5, 15, 2)
# Plotting
plt.plot(a.numpy(), np.zeros(a.numpy().shape), color = 'red', marker = "o"


tensor([3.1250e-02, 5.1271e-02, 8.4119e-02, 1.3801e-01, 2.2643e-01, 3.7150e-01,
        6.0951e-01, 1.0000e+00, 1.6407e+00, 2.6918e+00, 4.4164e+00, 7.2458e+00,
        1.1888e+01, 1.9504e+01, 3.2000e+01])
[torch.FloatTensor of size 15]

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