In this article, we are going to cover how to compute the Heaviside step function for each element in input in PyTorch using Python. We can compute this with the help of torch.heaviside() method.
torch.heaviside() method
The torch.heaviside() method is used to compute the Heaviside step function for each element. This method accepts input and values as parameters. The parameters type should be tensor only. If the input < 0 then it return 0. whereas, if input > 0 then this method 1 respectively. If the input=0 then this method returns a value the same as the values (one of the parameters). Below is the syntax of the given method:
Syntax: torch.heaviside(input, value)
Parameters:
- input (Tensor): This is our input tensor.
- value (Tensor): This value is a tensor and it’s where input is 0.
Return: This method returns the computed heaviside step function.
Example 1
In this example, we compute the Heaviside step function for each element in the given 1D tensor.
Python3
# Import the required libraries import torch   # define two tensors input_tens = torch.tensor([0.3, -1.2, 0, 2.0, 0.9]) values_tens = torch.tensor([0.2])   # display above defined tensors print(" The Input Tensor: ", input_tens) print(" The Values Tensor: ", values_tens)   # compute heaviside step function for each # element hea = torch.heaviside(input_tens, values_tens)   # Display Output print(" computed Heaviside step function for each element: \n", hea) |
Output:
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Example 2
In the following example, we compute the Heaviside step function for each element in the given 2D tensor.
Python3
# Import the required libraries import torch   # define a 2D tensor for input input_tens = input = torch.tensor([[-2.9, 0.0, -1.6, 2.5],                                    [0.0, -1.2, 0.0, 0.0],                                    [-2.3, 0.0, 1.8, -1.3],                                    [0.0, 2.2, -1.3, 0.0]])   # define a tensor for values values_tens = torch.tensor([0.2, 0.3, 0.4, 0.5])   # display above defined tensors print("\n\n The Input Tensor: \n", input_tens) print("\n The Values Tensor: \n", values_tens)   # compute heaviside step function for each # element hea = torch.heaviside(input_tens, values_tens)   # Display Output print("\n computed Heaviside step function for each element: \n", hea) |
Output:
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