neural_network.activation_functions.softplus¶

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

Functions¶

softplus(→ numpy.ndarray)

Implements the Softplus activation function.

neural_network.activation_functions.softplus.softplus(vector: numpy.ndarray) → numpy.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])