Added Tanh Activation Function

Author: Mathasuriya-infy

neural_network/activation_functions/tanh.py

@@ -0,0 +1,40 @@
+"""
+This script demonstrates the implementation of the Hyperbolic Tangent (Tanh) function.
+
+The tanh function is a hyperbolic function that maps any real-valued input to a value
+between -1 and 1. It's commonly used as an activation function in neural networks
+and is a scaled version of the sigmoid function.
+
+For more detailed information, you can refer to the following link:
+https://en.wikipedia.org/wiki/Hyperbolic_functions#Hyperbolic_tangent
+"""
+
+import numpy as np
+
+
+def tanh(vector: np.ndarray) -> np.ndarray:
+    """
+    Implements the hyperbolic tangent (tanh) activation function.
+
+    Parameters:
+        vector (np.ndarray): A vector that consists of numeric values
+
+    Returns:
+        np.ndarray: Input vector after applying tanh activation function
+
+    Formula: f(x) = (e^x - e^(-x)) / (e^x + e^(-x)) = (e^(2x) - 1) / (e^(2x) + 1)
+
+    Examples:
+    >>> tanh(np.array([-1.0, 0.0, 1.0, 2.0]))
+    array([-0.76159416,  0.        ,  0.76159416,  0.96402758])
+
+    >>> tanh(np.array([-5.0, -2.5, 2.5, 5.0]))
+    array([-0.9999092, -0.9866143,  0.9866143,  0.9999092])
+    """
+    return np.tanh(vector)
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()