Python/neural_network/activation_functions/rectified_linear_unit.py

"""
This script demonstrates the implementation of the ReLU function.

It's a kind of activation function defined as the positive part of its argument in the
context of neural network.
The function takes a vector of K real numbers as input and then argmax(x, 0).
After through ReLU, the element of the vector always 0 or real number.

Script inspired from its corresponding Wikipedia article
https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
"""
from __future__ import annotations

import numpy as np


def relu(vector: list[float]):
    """
    Implements the relu function

    Parameters:
        vector (np.array,list,tuple): A  numpy array of shape (1,n)
        consisting of real values or a similar list,tuple


    Returns:
        relu_vec (np.array): The input numpy array, after applying
        relu.

    >>> vec = np.array([-1, 0, 5])
    >>> relu(vec)
    array([0, 0, 5])
    """

    # compare two arrays and then return element-wise maxima.
    return np.maximum(0, vector)


if __name__ == "__main__":
    print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]