Update scaled_exponential_linear_unit.py

neural_network/activation_functions/scaled_exponential_linear_unit.py

@@ -16,26 +16,26 @@
 
 
 def scaled_exponential_linear_unit(
-    vector: np.ndarray, alpha: float = 1.6732, _lambda: float = 1.0507
+    vector: np.ndarray, alpha: float = 1.6732, lambda_: float = 1.0507
 ) -> np.ndarray:
     """
     Applies the Scaled Exponential Linear Unit function to each element of the vector.
     Parameters :
         vector : np.ndarray
         alpha : float (default = 1.6732)
-        _lambda : float (default = 1.0507)
+        lambda_ : float (default = 1.0507)
 
     Returns : np.ndarray
-    Formula : f(x) = _lambda * x if x > 0
-                     _lambda * alpha * (e**x - 1) if x <= 0
+    Formula : f(x) = lambda_ * x if x > 0
+                     lambda_ * alpha * (e**x - 1) if x <= 0
     Examples :
     >>> scaled_exponential_linear_unit(vector=np.array([1.3, 3.7, 2.4]))
     array([1.36591, 3.88759, 2.52168])
 
     >>> scaled_exponential_linear_unit(vector=np.array([1.3, 4.7, 8.2]))
     array([1.36591, 4.93829, 8.61574])
     """
-    return _lambda * np.where(vector > 0, vector, alpha * (np.exp(vector) - 1))
+    return lambda_ * np.where(vector > 0, vector, alpha * (np.exp(vector) - 1))
 
 
 if __name__ == "__main__":