Python/neural_network/activation_functions/softplus.py

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"""
Softplus Activation Function
Use Case: The Softplus function is a smooth approximation of the ReLU function.
For more detailed information, you can refer to the following link:
https://en.wikipedia.org/wiki/Rectifier_(neural_networks)#Softplus
"""
import numpy as np
def softplus(vector: np.ndarray) -> np.ndarray:
"""
Implements the Softplus activation function.
Parameters:
vector (np.ndarray): The input array for the Softplus activation.
Returns:
np.ndarray: The input array after applying the Softplus activation.
Formula: f(x) = ln(1 + e^x)
Examples:
>>> softplus(np.array([2.3, 0.6, -2, -3.8]))
array([2.39554546, 1.03748795, 0.12692801, 0.02212422])
>>> softplus(np.array([-9.2, -0.3, 0.45, -4.56]))
array([1.01034298e-04, 5.54355244e-01, 9.43248946e-01, 1.04077103e-02])
"""
return np.log(1 + np.exp(vector))
if __name__ == "__main__":
import doctest
doctest.testmod()