Implemented Swish Function

maths/swish.py

@@ -0,0 +1,53 @@
+"""
+This script demonstrates the implementation of the Swish function.
+
+The function takes a vector x of K real numbers as input and then
+returns x * sigmoid(x).
+Swish is a smooth, non-monotonic function defined as
+f(x) = x ·sigmoid(x).
+Extensive experiments shows that Swish consistently
+matches or outperforms ReLU on deep networks applied to a variety
+of challenging domains such as image classification and
+machine translation.
+
+Script inspired from its corresponding research paper
+https://arxiv.org/abs/1710.05941
+"""
+
+import numpy as np
+
+
+def sigmoid(vector: np.array) -> np.array:
+    """
+    Swish function can be implemented easily with the help of
+    sigmoid function
+    """
+    return 1 / (1 + np.exp(-vector))
+
+
+def swish(vector: np.array) -> np.array:
+    """
+    Implements the swish function
+
+    Parameters:
+        vector (np.array): A  numpy array consisting of real
+        values.
+
+    Returns:
+        vector (np.array): The input numpy array, after applying
+        swish.
+
+    Examples:
+    >>> swish(np.array([-1.0, 1.0, 2.0]))
+    array([-0.26894142,  0.73105858,  1.76159416])
+
+    >>> swish(np.array([-2]))
+    array([-0.23840584])
+    """
+    return vector * sigmoid(vector)
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()