neural_network.activation_functions.squareplus¶

Squareplus Activation Function

Use Case: Squareplus designed to enhance positive values and suppress negative values. For more detailed information, you can refer to the following link: https://en.wikipedia.org/wiki/Rectifier_(neural_networks)#Squareplus

Functions¶

squareplus(→ numpy.ndarray)

Implements the SquarePlus activation function.

neural_network.activation_functions.squareplus.squareplus(vector: numpy.ndarray, beta: float) → numpy.ndarray¶

Implements the SquarePlus activation function.

Parameters:: vector (np.ndarray): The input array for the SquarePlus activation. beta (float): size of the curved region
Returns:: np.ndarray: The input array after applying the SquarePlus activation.

Formula: f(x) = ( x + sqrt(x^2 + b) ) / 2

Examples: >>> squareplus(np.array([2.3, 0.6, -2, -3.8]), beta=2) array([2.5 , 1.06811457, 0.22474487, 0.12731349])

>>> squareplus(np.array([-9.2, -0.3, 0.45, -4.56]), beta=3)
array([0.0808119 , 0.72891979, 1.11977651, 0.15893419])