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Python Pytorch arrange() method

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  • Last Updated : 05 Aug, 2021
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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.arrange() 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.arrange(start=0, end, step=1, out=None)
Parameters
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: 

Python3




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


Output: 

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.
 

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