Python/neural_network/activation_functions/leaky_rectified_linear_unit.py

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"""
Leaky Rectified Linear Unit (Leaky ReLU)
Use Case: Leaky ReLU addresses the problem of the vanishing gradient.
For more detailed information, you can refer to the following link:
https://en.wikipedia.org/wiki/Rectifier_(neural_networks)#Leaky_ReLU
"""
import numpy as np
def leaky_rectified_linear_unit(vector: np.ndarray, alpha: float) -> np.ndarray:
"""
Implements the LeakyReLU activation function.
Parameters:
vector (np.ndarray): The input array for LeakyReLU activation.
alpha (float): The slope for negative values.
Returns:
np.ndarray: The input array after applying the LeakyReLU activation.
Formula: f(x) = x if x > 0 else f(x) = alpha * x
Examples:
>>> leaky_rectified_linear_unit(vector=np.array([2.3,0.6,-2,-3.8]), alpha=0.3)
array([ 2.3 , 0.6 , -0.6 , -1.14])
>>> leaky_rectified_linear_unit(np.array([-9.2, -0.3, 0.45, -4.56]), alpha=0.067)
array([-0.6164 , -0.0201 , 0.45 , -0.30552])
"""
return np.where(vector > 0, vector, alpha * vector)
if __name__ == "__main__":
import doctest
doctest.testmod()