ISRLU< InputDataType, OutputDataType > Class Template Reference

The ISRLU activation function, defined by. More...

Public Member Functions
	ISRLU (const double alpha=1.0)
	Create the ISRLU object using the specified parameter. More...
double const &	Alpha () const
	Get the non zero gradient. More...
double &	Alpha ()
	Modify the non zero gradient. 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 &	Delta () const
	Get the delta. More...
OutputDataType &	Delta ()
	Modify the delta. 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...
OutputDataType const &	OutputParameter () const
	Get the output parameter. More...
OutputDataType &	OutputParameter ()
	Modify the output parameter. More...
template < typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the layer. More...
size_t	WeightSize ()
	Get size of weights. More...

Detailed Description

template

<

typename

InputDataType

=

arma::mat

,

typename

OutputDataType

=

arma::mat

>

class mlpack::ann::ISRLU< InputDataType, OutputDataType >

The ISRLU activation function, defined by.

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).

Definition at line 60 of file isrlu.hpp.

Constructor & Destructor Documentation

ISRLU()

ISRLU

(

const double

alpha = 1.0

)

Create the ISRLU object using the specified parameter.

Parameters

alpha

Scale parameter controls the value to which an ISRLU saturates for negative inputs.

Member Function Documentation

Alpha() [1/2]

double const& Alpha ( ) const

inline

Get the non zero gradient.

Definition at line 104 of file isrlu.hpp.

Alpha() [2/2]

double& Alpha ( )

inline

Modify the non zero gradient.

Definition at line 106 of file isrlu.hpp.

Backward()

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.

Using the results from the feed forward pass.

Parameters

input	The propagated input activation f(x).
gy	The backpropagated error.
g	The calculated gradient.

Delta() [1/2]

OutputDataType const& Delta ( ) const

inline

Get the delta.

Definition at line 99 of file isrlu.hpp.

Delta() [2/2]

OutputDataType& Delta ( )

inline

Modify the delta.

Definition at line 101 of file isrlu.hpp.

Forward()

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.

Parameters

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

OutputParameter() [1/2]

OutputDataType const& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 94 of file isrlu.hpp.

OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 96 of file isrlu.hpp.

serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

Serialize the layer.

Referenced by ISRLU< InputDataType, OutputDataType >::WeightSize().

WeightSize()

size_t WeightSize ( )

inline

Get size of weights.

Definition at line 109 of file isrlu.hpp.

References ISRLU< InputDataType, OutputDataType >::serialize().

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

• /home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/ann/layer/isrlu.hpp