mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-12-27 05:30:16 +00:00
f6b12420ce
* Added Leaky ReLU activation function * Added Leaky ReLU activation function * Added Leaky ReLU activation function * Formatting and spelling fixes done
40 lines
1.1 KiB
Python
40 lines
1.1 KiB
Python
"""
|
|
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()
|