The ELU activation is added (TheAlgorithms#8699)

* tanh function been added

* tanh function been added

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* tanh function is added

* tanh function is added

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* tanh function added

* tanh function added

* tanh function is added

* Apply suggestions from code review

* ELU activation function is added

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* elu activation is added

* ELU activation is added

* Update maths/elu_activation.py

Co-authored-by: Christian Clauss <[email protected]>

* Exponential_linear_unit activation is added

* Exponential_linear_unit activation is added

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Christian Clauss <[email protected]>

Author: 3 people

Diff for: neural_network/activation_functions/exponential_linear_unit.py

@@ -0,0 +1,40 @@
+"""
+Implements the Exponential Linear Unit or ELU function.
+
+The function takes a vector of K real numbers and a real number alpha as
+input and then applies the ELU function to each element of the vector.
+
+Script inspired from its corresponding Wikipedia article
+https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
+"""
+
+import numpy as np
+
+
+def exponential_linear_unit(vector: np.ndarray, alpha: float) -> np.ndarray:
+    """
+         Implements the ELU activation function.
+         Parameters:
+             vector: the array containing input of elu activation
+             alpha: hyper-parameter
+         return:
+         elu (np.array): The input numpy array after applying elu.
+
+         Mathematically, f(x) = x, x>0 else (alpha * (e^x -1)), x<=0, alpha >=0
+
+    Examples:
+    >>> exponential_linear_unit(vector=np.array([2.3,0.6,-2,-3.8]), alpha=0.3)
+    array([ 2.3       ,  0.6       , -0.25939942, -0.29328877])
+
+    >>> exponential_linear_unit(vector=np.array([-9.2,-0.3,0.45,-4.56]), alpha=0.067)
+    array([-0.06699323, -0.01736518,  0.45      , -0.06629904])
+
+
+    """
+    return np.where(vector > 0, vector, (alpha * (np.exp(vector) - 1)))
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()