Python Pytorch logspace() method Last Updated : 28 Apr, 2025 Summarize Comments Improve Suggest changes Share Like Article Like Report 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) Parameters: 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: Python3 # 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) Output: 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 Python3 # Importing the PyTorch library import torch # Importing the NumPy library import numpy as np # Importing the matplotlib.pyplot 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) print(a) # Plotting plt.plot(a.numpy(), np.zeros(a.numpy().shape), color = 'red', marker = "o") plt.title("torch.linspace") plt.xlabel("X") plt.ylabel("Y") plt.show() Output: 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] Comment More infoAdvertise with us Next Article Python PyTorch log2() method S sanskar27jain Follow Improve Article Tags : Python Python-PyTorch Practice Tags : python Similar Reads Python Pytorch linspace() method 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.linspace() returns a one-dimensional tensor of steps equally spaced points between start and end. The output tensor is 1-D of size 2 min read Python - PyTorch log() method PyTorch torch.log() method gives a new tensor having the natural logarithm of the elements of input tensor. Syntax: torch.log(input, out=None) Arguments input: This is input tensor. out: The output tensor. Return: It returns a Tensor. Let's see this concept with the help of few examples: Example 1: 1 min read Python PyTorch log2() method PyTorch log2() method computes the logarithm to the base 2 of the elements of an input tensor. Â It computes the logarithm values element-wise. Â It takes a tensor as an input and returns a new tensor with computed logarithm values. The elements of the input tensor must be between zero and the positiv 4 min read Python - PyTorch frac() method PyTorch torch.frac() method computes the fractional portion of each element in input. Syntax: torch.frac(input, out=None) Arguments input: This is input tensor. out: The output tensor. Return: It returns a Tensor. Let's see this concept with the help of few examples: Example 1: Python3 # Importing t 1 min read Python - PyTorch floor() method PyTorch torch.floor() method returns a new tensor which is floor of the elements of input, the largest integer less than or equal to each element. Syntax: torch.floor(input, out=None) Arguments input: This is input tensor. out: The output tensor. Return: It returns a Tensor. Let's see this concept w 1 min read Python - PyTorch exp() method PyTorch torch.exp() method returns a new tensor after getting the exponent of the elements of the input tensor. Syntax: torch.exp(input, out=None) Arguments input: This is input tensor. out: The output tensor. Return: It returns a Tensor. Let's see this concept with the help of few examples: Example 1 min read Like