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neural_network/activation_functions/scaled_exponential_linear_unit.py

@@ -2,8 +2,8 @@
 Implements the Scaled Exponential Linear Unit or SELU function.
 
 The function takes a vector of K real numbers and two real numbers alpha(default = 1.6732) and lambda (default = 1.0507)
-as input and then applies the SELU function to each element of the vector. SELU is a self-normalizing activation function. 
-It is a variant of the ELU. The main advantage of SELU is that we can be sure that the output will always be standardized 
+as input and then applies the SELU function to each element of the vector. SELU is a self-normalizing activation function.
+It is a variant of the ELU. The main advantage of SELU is that we can be sure that the output will always be standardized
 due to its self-normalizing behavior. That means there is no need to include Batch-Normalization layers.
 
 References :
@@ -12,7 +12,10 @@
 
 import numpy as np
 
-def scaled_exponential_linear_unit(vector: np.ndarray, alpha : float = 1.6732, _lambda : float = 1.0507) -> np.ndarray:
+
+def scaled_exponential_linear_unit(
+    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
@@ -30,6 +33,8 @@ def scaled_exponential_linear_unit(vector: np.ndarray, alpha : float = 1.6732, _
     """
     return _lambda * np.where(vector > 0, vector, alpha * (np.exp(vector) - 1))
 
+
 if __name__ == "__main__":
     import doctest
-    doctest.testmod()
+
+    doctest.testmod()