TheAlgorithms-Python

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