TheAlgorithms · saahilmahato · Oct 3, 2025 · Oct 3, 2025 · Oct 3, 2025
diff --git a/neural_network/activation_functions/softsign.py b/neural_network/activation_functions/softsign.py
@@ -0,0 +1,38 @@
+"""
+This script demonstrates the implementation of the Softsign activation function.
+
+Softsign is a smooth activation function defined as:
+
+    f(x) = x / (1 + |x|)
+
+It maps input values into the range (-1, 1), similar to the hyperbolic tangent (tanh)
+function but with a polynomial decay instead of exponential.
+
+More about this function can be found in the article:
+https://www.gabormelli.com/RKB/Softsign_Activation_Function
+"""
+
+import numpy as np
+
+
+def softsign(vector: np.ndarray) -> np.ndarray:
+    """
+    Implements the softsign activation function
+
+    Parameters:
+        vector (ndarray): A vector that consists of numeric values
+
+    Returns:
+        vector (ndarray): Input vector after applying softsign function
+
+    >>> vector = np.array([-5, -1, 0, 1, 5])
+    >>> softsign(vector)
+    array([-0.83333333, -0.5       ,  0.        ,  0.5       ,  0.83333333])
+    """
+    return vector / (1 + np.abs(vector))
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()