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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)

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:




# 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)

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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