added tanh function to activation functions

neural_network/activation_functions/tanh.py

@@ -0,0 +1,44 @@
+"""
+Hyperbolic Tangent Function (Tanh)
+reference : https://paperswithcode.com/method/tanh-activation#:~:text=Tanh%20Activation%20is%20an%20activation,for%20multi%2Dlayer%20neural%20networks.
+
+The hyperbolic tangent function, commonly denoted as "tanh," is a mathematical
+function that maps real numbers to the range (-1, 1). It's defined as:
+
+tanh(x) = (e^x - e^(-x)) / (e^x + e^(-x))
+
+Where:
+- e is the base of the natural logarithm (approximately 2.71828).
+- x is the input value to the function.
+
+The tanh function's curve is "S" shaped and centered at the origin (0,0). It
+behaves as follows:
+- As x approaches negative infinity, tanh(x) approaches -1.
+- As x approaches positive infinity, tanh(x) approaches 1.
+- At x = 0, tanh(x) equals 0.
+
+In machine learning, tanh is often used as an activation function in neural
+networks, introducing non-linearity and squishing neuron outputs to a range
+between -1 and 1.
+
+"""
+import numpy as np
+
+
+def tanh(vector: np.ndarray) -> np.ndarray:
+    """_summary_
+    Args:
+        vector (np.ndarray): numpy array
+    Returns:
+        np.ndarray: numpy array after applying tanh
+    Examples :
+    >>> tanh(vector=np.array([-2, -1, 0,  1,  2]))
+    array([-0.96402758, -0.76159416,  0.        ,  0.76159416,  0.96402758])
+    """
+    return (np.exp(vector) - np.exp(-vector)) / (np.exp(vector) + np.exp(-vector))
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()