Skip to content
Related Articles

Related Articles

Python Pytorch arange() 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.arange() returns a 1-D tensor of size  \left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil
with values from the interval  [start, end) taken with common difference step beginning from start.

 out_{i+1} = out_i + step

Syntax: torch.arange(start=0, end, step=1, out=None)

start: the starting value for the set of points. Default: 0.
end: the ending value for the set of points
step: the gap between each pair of adjacent points. Default: 1.
out(Tensor, optional): the output tensor

Return type: A tensor

Code #1:

# Importing the PyTorch library
import torch
# Applying the arange function and
# storing the resulting tensor in 't'
a = torch.arange(3)
print("a = ", a)
b = torch.arange(1, 6)
print("b = ", b)
c = torch.arange(1, 5, 0.5)
print("c = ", c)


a =  tensor([0, 1, 2])
b =  tensor([1, 2, 3, 4, 5])
c =  tensor([1.0000, 1.5000, 2.0000, 2.5000, 3.0000, 3.5000, 4.0000, 4.5000])


Note that the non-integer step is subject to floating-point rounding errors when comparing against end; to avoid inconsistency, we advise adding a small epsilon to the end in such cases.

 Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.  

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course

My Personal Notes arrow_drop_up
Recommended Articles
Page :