brainstorm/handlers/base_handler.py
def el_deriv(self, x, y, dy, dx):
        """Backpropagate derivatives through the exponential linear function.

        f'(x) = 1 if x > 0 else f(x) + 1

        Note that we chose to fix alpha to 1

        Args:
            x (array_type): Inputs to the exponential linear function.
                            This argument is not used and is present only to
                            conform with other activation functions.
            y (array_type): Outputs of the exponential linear function.
            dy (array_type): Derivatives with respect to the outputs.
            dx (array_type): Array in which the derivatives with respect to
                             the inputs are placed.
        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.
        """
Backpropagate derivatives through the exponential linear function.

        f'(x) = 1 if x > 0 else f(x) + 1

        Note that we chose to fix alpha to 1

        Args:
            x (array_type): Inputs to the exponential linear function.
                            This argument is not used and is present only to
                            conform with other activation functions.
            y (array_type): Outputs of the exponential linear function.
            dy (array_type): Derivatives with respect to the outputs.
            dx (array_type): Array in which the derivatives with respect to
                             the inputs are placed.
        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.
        
Backpropagate derivatives through the exponential linear function.

Note that we chose to fix alpha to 1
Backpropagate derivatives through the exponential linear function . Note that we chose to fix alpha to 1
def el_deriv(self, x, y, dy, dx):
def el_deriv ( self , x , y , dy , dx ) :
Backpropagate derivatives through the exponential linear function.
Backpropagate derivatives through the exponential linear function .
"""Backpropagate derivatives through the exponential linear function.

        f'(x) = 1 if x > 0 else f(x) + 1

        Note that we chose to fix alpha to 1

        Args:
            x (array_type): Inputs to the exponential linear function.
                            This argument is not used and is present only to
                            conform with other activation functions.
            y (array_type): Outputs of the exponential linear function.
            dy (array_type): Derivatives with respect to the outputs.
            dx (array_type): Array in which the derivatives with respect to
                             the inputs are placed.
        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
Inputs to the exponential linear function.
This argument is not used and is present only to
conform with other activation functions.
Inputs to the exponential linear function . This argument is not used and is present only to conform with other activation functions .
y
Outputs of the exponential linear function.
Outputs of the exponential linear function .
dy
Derivatives with respect to the outputs.
Derivatives with respect to the outputs .
dx
Array in which the derivatives with respect to
the inputs are placed.
Array in which the derivatives with respect to the inputs are placed .