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:
