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How to Apply Rectified Linear Unit Function Element-Wise in PyTorch?

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  • Last Updated : 02 Jun, 2022
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In this article, we are going to see How to Apply Rectified Linear Unit Function Element-Wise in PyTorch in Python. We can Rectify Linear Unit Function Element-Wise by using torch.nn.ReLU() method.

torch.nn.ReLU() method

In PyTorch, torch.nn.ReLu() method replaces all the negative values with 0 and all the non-negative left unchanged. The values of the tensor must be real only. we can also do this operation in-place by using inplace=True as a Parameter. before moving further let’s see the syntax of the given method.

Syntax: torch.nn.ReLU(inplace=False)

Parameters:

  • inplace: This parameter is use when we want to do this operation in-place. Default value of inplace is False.

Example 1:

The following program is to understand how to compute the Rectified Linear Unit Function Element-Wise.

Python




# Import the required library
import torch
import torch.nn as nn
  
# define a tensor
input = torch.tensor([[-1., 0., 2., 0.],
                      [3., 4., -5., 0.],
                      [6., -9., -10., 11.],
                      [0., 13., 14., -15.]])
  
print(" Original Tensor: ", input)
  
# Apply Rectified Linear Unit Function 
# Element-Wise
Rel = torch.nn.ReLU()
Output = Rel(input)
  
# display result
print(" Output Tensor: ", Output)

Output:

 

Example 2:

The following program is to understand how to Apply Rectified Linear Unit Function with inplace=True.

Python




# Import the required library
import torch
import torch.nn as nn
  
# define a tensor
input = torch.tensor([[-2., 3., -6., 2.],
                      [3., -6., 5., 0.],
                      [6., -3., 0., -11.],
                      [13., -13., 14., 15.]])
  
print(" Original Tensor: ", input)
  
# Apply Rectified Linear Unit Function 
# Element-Wise Do this operation 
# in-place
Rel = torch.nn.ReLU(inplace=True)
Output = Rel(input)
  
# display result
print(" Output Tensor: ", Output)

Output:

 


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