brainstorm/handlers/base_handler.py
def el(self, x, y):
        """
        Compute exponential linear activation function.

        f(x) = x if x > 0 else exp(x) - 1

        Note that we chose to fix alpha to 1

        Args:
            x (array_type): Input Array.
            y (array_type): Output Array

        Returns:
            None

        References:
            Clevert, D. A., Unterthiner, T., & Hochreiter, S. (2015).
            Fast and Accurate Deep Network Learning by Exponential Linear Units.
            arXiv preprint arXiv:1511.07289.
        """

        Compute exponential linear activation function.

        f(x) = x if x > 0 else exp(x) - 1

        Note that we chose to fix alpha to 1

        Args:
            x (array_type): Input Array.
            y (array_type): Output Array

        Returns:
            None

        References:
            Clevert, D. A., Unterthiner, T., & Hochreiter, S. (2015).
            Fast and Accurate Deep Network Learning by Exponential Linear Units.
            arXiv preprint arXiv:1511.07289.
        
Compute exponential linear activation function.

Note that we chose to fix alpha to 1
Compute exponential linear activation function . Note that we chose to fix alpha to 1
def el(self, x, y):
def el ( self , x , y ) :
Compute exponential linear activation function.
Compute exponential linear activation function .
"""
        Compute exponential linear activation function.

        f(x) = x if x > 0 else exp(x) - 1

        Note that we chose to fix alpha to 1

        Args:
            x (array_type): Input Array.
            y (array_type): Output Array

        Returns:
            None

        References:
            Clevert, D. A., Unterthiner, T., & Hochreiter, S. (2015).
            Fast and Accurate Deep Network Learning by Exponential Linear Units.
            arXiv preprint arXiv:1511.07289.
        """
None
self
x
None
y
None