Added Softplus activation function (TheAlgorithms#9944)

Author: kausthub-kannan

neural_network/activation_functions/softplus.py

@@ -0,0 +1,37 @@
+"""
+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()