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

The PReLU activation function, defined by (where alpha is trainable) More...

#include <parametric_relu.hpp>

Public Member Functions
	PReLU (const double userAlpha=0.03)
	Create the PReLU object using the specified parameters. More...
void	Reset ()
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...
template<typename eT >
void	Gradient (const arma::Mat< eT > &input, const arma::Mat< eT > &error, arma::Mat< eT > &gradient)
	Calculate the gradient using the output delta and the input activation. More...
OutputDataType const &	Parameters () const
	Get the parameters.
OutputDataType &	Parameters ()
	Modify the parameters.
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.
OutputDataType const &	Gradient () const
	Get the gradient.
OutputDataType &	Gradient ()
	Modify the gradient.
double const &	Alpha () const
	Get the non zero gradient.
double &	Alpha ()
	Modify the non zero gradient.
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::PReLU< InputDataType, OutputDataType >

The PReLU activation function, defined by (where alpha is trainable)

\begin{eqnarray*} f(x) &=& \max(x, alpha*x) \\ f'(x) &=& \left\{ \begin{array}{lr} 1 & : x > 0 \\ alpha & : 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

PReLU()

template<typename InputDataType , typename OutputDataType >

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

(

const double

userAlpha = 0.03

)

Create the PReLU object using the specified parameters.

The non zero gradient can be adjusted by specifying tha parameter alpha in the range 0 to 1. Default (alpha = 0.03). This parameter is trainable.

Parameters

userAlpha

Non zero gradient

Member Function Documentation

Backward()

template<typename InputDataType , typename OutputDataType >

template<typename DataType >

void mlpack::ann::PReLU< 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::PReLU< 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.

Gradient()

template<typename InputDataType , typename OutputDataType >

template<typename eT >

void mlpack::ann::PReLU< InputDataType, OutputDataType >::Gradient	(	const arma::Mat< eT > &	input,
		const arma::Mat< eT > &	error,
		arma::Mat< eT > &	gradient
	)

Calculate the gradient using the output delta and the input activation.

Parameters

input	The input parameter used for calculating the gradient.
error	The calculated error.
gradient	The calculated gradient.

Reset()

template<typename InputDataType , typename OutputDataType >

void mlpack::ann::PReLU< InputDataType, OutputDataType >::Reset

(

)

Set value of alpha to the one given by user.

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

• src/mlpack/methods/ann/layer/parametric_relu.hpp
• src/mlpack/methods/ann/layer/parametric_relu_impl.hpp