mlpack::ann::FlattenTSwish< InputDataType, OutputDataType > Class Template Reference

The Flatten T Swish activation function, defined by. More...

#include <flatten_t_swish.hpp>

Public Member Functions
	FlattenTSwish (const double T=-0.20)
	Create the Flatten T Swish object using the specified parameters. More...
template<typename InputType , typename OutputType >
void	Forward (const InputType &input, OutputType &output)
	Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activity forward through f. More...
template<typename DataType >
void	Backward (const DataType &input, const DataType &gy, DataType &g)
	Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backwards through f. More...
OutputDataType const &	OutputParameter () const
	Get the output parameter.
OutputDataType &	OutputParameter ()
	Modify the output parameter.
OutputDataType const &	Delta () const
	Get the delta.
OutputDataType &	Delta ()
	Modify the delta.
double const &	T () const
	Get the T parameter.
double &	T ()
	Modify the T parameter.
size_t	WeightSize () const
	Get size of weights.
template<typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the layer.

Detailed Description

template<typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
class mlpack::ann::FlattenTSwish< InputDataType, OutputDataType >

The Flatten T Swish activation function, defined by.

\begin{eqnarray*} f'(x) &=& \left\{ \begin{array}{lr} frac{x}{1+exp(-x)} + T & : x \ge 0 \\ T & : x < 0 \end{array} \right. \\ f'(x) &=& \left\{ \begin{array}{lr} \sigma(x)(1 - f(x)) + f(x) & : x > 0 \\ 0 & : x \le 0 \end{array} \right. \end{eqnarray*}

Template Parameters

InputDataType	Type of the input data (arma::colvec, arma::mat, arma::sp_mat or arma::cube).
OutputDataType	Type of the output data (arma::colvec, arma::mat, arma::sp_mat or arma::cube).

Constructor & Destructor Documentation

FlattenTSwish()

template<typename InputDataType , typename OutputDataType >

mlpack::ann::FlattenTSwish< InputDataType, OutputDataType >::FlattenTSwish

(

const double

T = -0.20

)

Create the Flatten T Swish object using the specified parameters.

The thresholded value T can be adjusted via T paramaters. When the x is < 0, T will be used instead of 0. The default value of T is -0.20 as suggested in the paper.

Parameters

T

Member Function Documentation

Backward()

template<typename InputDataType , typename OutputDataType >

template<typename DataType >

void mlpack::ann::FlattenTSwish< InputDataType, OutputDataType >::Backward	(	const DataType &	input,
		const DataType &	gy,
		DataType &	g
	)

Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backwards through f.

Using the results from the feed forward pass.

Parameters

input	The propagated input activation.
gy	The backpropagated error.
g	The calculated gradient.

Forward()

template<typename InputDataType , typename OutputDataType >

template<typename InputType , typename OutputType >

void mlpack::ann::FlattenTSwish< InputDataType, OutputDataType >::Forward	(	const InputType &	input,
		OutputType &	output
	)

Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activity forward through f.

Parameters

input	Input data used for evaluating the specified function.
output	Resulting output activation.

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The documentation for this class was generated from the following files:

• src/mlpack/methods/ann/layer/flatten_t_swish.hpp
• src/mlpack/methods/ann/layer/flatten_t_swish_impl.hpp