This class offers methods for the usage of the Resilient-Backpropagation-algorithm with weight-backtracking. More...

#include <shark/Algorithms/GradientDescent/Rprop.h>

Inheritance diagram for shark::RpropPlus:

Public Member Functions
SHARK_EXPORT_SYMBOL	RpropPlus ()

std::string	name () const
	From INameable: return the class name. More...

SHARK_EXPORT_SYMBOL void	init (ObjectiveFunctionType const &objectiveFunction, SearchPointType const &startingPoint)
	initializes the optimizer using a predefined starting point More...

SHARK_EXPORT_SYMBOL void	init (ObjectiveFunctionType const &objectiveFunction, SearchPointType const &startingPoint, double initDelta)

SHARK_EXPORT_SYMBOL void	step (ObjectiveFunctionType const &objectiveFunction)
	Carry out one step of the optimizer for the supplied objective function. More...

SHARK_EXPORT_SYMBOL void	read (InArchive &archive)
	Read the component from the supplied archive. More...

SHARK_EXPORT_SYMBOL void	write (OutArchive &archive) const
	Write the component to the supplied archive. More...

Public Member Functions inherited from shark::RpropMinus
SHARK_EXPORT_SYMBOL	RpropMinus ()

void	setEtaMinus (double etaMinus)
	set decrease factor More...

void	setEtaPlus (double etaPlus)
	set increase factor More...

void	setMaxDelta (double d)
	set upper limit on update More...

void	setMinDelta (double d)
	set lower limit on update More...

double	maxDelta () const
	return the maximal step size component More...

RealVector const &	derivative () const
	Returns the derivative at the current point. Can be used for stopping criteria. More...

Public Member Functions inherited from shark::AbstractSingleObjectiveOptimizer< RealVector >
std::size_t	numInitPoints () const
	By default most single objective optimizers only require a single point. More...

virtual void	init (ObjectiveFunctionType const &function, std::vector< SearchPointType > const &initPoints)
	Initialize the optimizer for the supplied objective function using a set of initialisation points. More...

virtual const SolutionType &	solution () const
	returns the current solution of the optimizer More...

Public Member Functions inherited from shark::AbstractOptimizer< RealVector, double, SingleObjectiveResultSet< RealVector > >
const Features &	features () const

virtual void	updateFeatures ()

bool	requiresValue () const

bool	requiresFirstDerivative () const

bool	requiresSecondDerivative () const

bool	canSolveConstrained () const

bool	requiresClosestFeasible () const

virtual	~AbstractOptimizer ()

virtual void	init (ObjectiveFunctionType const &function)
	Initialize the optimizer for the supplied objective function. More...

Public Member Functions inherited from shark::INameable
virtual	~INameable ()

Public Member Functions inherited from shark::ISerializable
virtual	~ISerializable ()
	Virtual d'tor. More...

void	load (InArchive &archive, unsigned int version)
	Versioned loading of components, calls read(...). More...

void	save (OutArchive &archive, unsigned int version) const
	Versioned storing of components, calls write(...). More...

	BOOST_SERIALIZATION_SPLIT_MEMBER ()

Protected Attributes
RealVector	m_deltaw
	The final update values for all weights. More...

Protected Attributes inherited from shark::RpropMinus
ObjectiveFunctionType::FirstOrderDerivative	m_derivative

double	m_increaseFactor
	The increase factor \( \eta^+ \), set to 1.2 by default. More...

double	m_decreaseFactor
	The decrease factor \( \eta^- \), set to 0.5 by default. More...

double	m_maxDelta
	The upper limit of the increments \( \Delta w_i^{(t)} \), set to 1e100 by default. More...

double	m_minDelta
	The lower limit of the increments \( \Delta w_i^{(t)} \), set to 0.0 by default. More...

size_t	m_parameterSize

RealVector	m_oldDerivative
	The last error gradient. More...

RealVector	m_delta
	The absolute update values (increment) for all weights. More...

Protected Attributes inherited from shark::AbstractSingleObjectiveOptimizer< RealVector >
SolutionType	m_best
	Current solution of the optimizer. More...

Protected Attributes inherited from shark::AbstractOptimizer< RealVector, double, SingleObjectiveResultSet< RealVector > >
Features	m_features

Additional Inherited Members
Public Types inherited from shark::AbstractSingleObjectiveOptimizer< RealVector >
typedef base_type::SearchPointType	SearchPointType

typedef base_type::SolutionType	SolutionType

typedef base_type::ResultType	ResultType

typedef base_type::ObjectiveFunctionType	ObjectiveFunctionType

Public Types inherited from shark::AbstractOptimizer< RealVector, double, SingleObjectiveResultSet< RealVector > >
enum	Feature
	Models features that the optimizer requires from the objective function. More...

typedef RealVector	SearchPointType

typedef double	ResultType

typedef SingleObjectiveResultSet< RealVector >	SolutionType

typedef AbstractObjectiveFunction< RealVector, ResultType >	ObjectiveFunctionType

typedef TypedFlags< Feature >	Features

typedef TypedFeatureNotAvailableException< Feature >	FeatureNotAvailableException

Protected Member Functions inherited from shark::AbstractOptimizer< RealVector, double, SingleObjectiveResultSet< RealVector > >
void	checkFeatures (ObjectiveFunctionType const &objectiveFunction)
	Convenience function that checks whether the features of the supplied objective function match with the required features of the optimizer. More...

Detailed Description

This class offers methods for the usage of the Resilient-Backpropagation-algorithm with weight-backtracking.

The Rprop algorithm is an improvement of the algorithms with adaptive learning rates (as the Adaptive Backpropagation algorithm by Silva and Ameida, please see AdpBP.h for a description of the working of such an algorithm), that uses increments for the update of the weights, that are independent from the absolute partial derivatives. This makes sense, because large flat regions in the search space (plateaus) lead to small absolute partial derivatives and so the increments are chosen small, but the increments should be large to skip the plateau. In contrast, the absolute partial derivatives are very large at the "slopes" of very "narrow canyons", which leads to large increments that will skip the minimum lying at the bottom of the canyon, but it would make more sense to chose small increments to hit the minimum.
So, the Rprop algorithm only uses the signs of the partial derivatives and not the absolute values to adapt the parameters.
Instead of individual learning rates, it uses the parameter \(\Delta_i^{(t)}\) for weight \(w_i,\ i = 1, \dots, n\) in iteration \(t\), where the parameter will be adapted before the change of the weights:

\( \Delta_i^{(t)} = \Bigg\{ \begin{array}{ll} min( \eta^+ \cdot \Delta_i^{(t-1)}, \Delta_{max} ), & \mbox{if\ } \frac{\partial E^{(t-1)}}{\partial w_i} \cdot \frac{\partial E^{(t)}}{\partial w_i} > 0 \\ max( \eta^- \cdot \Delta_i^{(t-1)}, \Delta_{min} ), & \mbox{if\ } \frac{\partial E^{(t-1)}}{\partial w_i} \cdot \frac{\partial E^{(t)}}{\partial w_i} < 0 \\ \Delta_i^{(t-1)}, & \mbox{otherwise} \end{array} \)

The parameters \(\eta^+ > 1\) and \(0 < \eta^- < 1\) control the speed of the adaptation. To stabilize the increments, they are restricted to the interval \([\Delta_{min}, \Delta_{max}]\).
After the adaptation of the \(\Delta_i\) the update for the weights will be calculated as

\( \Delta w_i^{(t)} := - \mbox{sign} \left( \frac{\partial E^{(t)}}{\partial w_i}\right) \cdot \Delta_i^{(t)} \)

Furthermore, weight-backtracking will take place to increase the stability of the method, i.e. if \(\frac{\partial E^{(t-1)}}{\partial w_i} \cdot \frac{\partial E^{(t)}}{\partial w_i} < 0\) then \(\Delta w_i^{(t)} := - \Delta w_i^{(t-1)}; \frac{\partial E^{(t)}}{\partial w_i} := 0\), where the assignment of zero to the partial derivative of the error leads to a freezing of the increment in the next iteration.

For further information about the algorithm, please refer to:

Martin Riedmiller and Heinrich Braun,
"A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm".
In "Proceedings of the IEEE International Conference on Neural Networks", pp. 586-591,
Published by IEEE Press in 1993

Author: C. Igel

Date: 1999

Changes:: none

Status:: stable

Definition at line 261 of file Rprop.h.

Constructor & Destructor Documentation

◆ RpropPlus()

SHARK_EXPORT_SYMBOL shark::RpropPlus::RpropPlus ( )

Member Function Documentation

◆ init() [1/2]

SHARK_EXPORT_SYMBOL void shark::RpropPlus::init	(	ObjectiveFunctionType const &	function,
		SearchPointType const &	startingPoint
	)

virtual

initializes the optimizer using a predefined starting point

Reimplemented from shark::RpropMinus.

Reimplemented in shark::IRpropPlusFull, and shark::IRpropPlus.

◆ init() [2/2]

SHARK_EXPORT_SYMBOL void shark::RpropPlus::init	(	ObjectiveFunctionType const &	objectiveFunction,
		SearchPointType const &	startingPoint,
		double	initDelta
	)

virtual

Reimplemented from shark::RpropMinus.

Reimplemented in shark::IRpropPlusFull, and shark::IRpropPlus.

◆ name()

std::string shark::RpropPlus::name ( ) const

inlinevirtual

From INameable: return the class name.

Reimplemented from shark::RpropMinus.

Reimplemented in shark::IRpropPlusFull, and shark::IRpropPlus.

Definition at line 267 of file Rprop.h.

References shark::RpropMinus::init(), shark::RpropMinus::read(), SHARK_EXPORT_SYMBOL, shark::RpropMinus::step(), and shark::RpropMinus::write().

◆ read()

SHARK_EXPORT_SYMBOL void shark::RpropPlus::read ( InArchive & archive )

virtual

Read the component from the supplied archive.

Parameters

[in,out] archive The archive to read from.

Reimplemented from shark::RpropMinus.

Reimplemented in shark::IRpropPlusFull, and shark::IRpropPlus.

◆ step()

SHARK_EXPORT_SYMBOL void shark::RpropPlus::step ( ObjectiveFunctionType const & function )

virtual

Carry out one step of the optimizer for the supplied objective function.

Parameters

[in] function The objective function to initialize for.

Reimplemented from shark::RpropMinus.

Reimplemented in shark::IRpropPlusFull, and shark::IRpropPlus.

◆ write()

SHARK_EXPORT_SYMBOL void shark::RpropPlus::write ( OutArchive & archive ) const

virtual

Write the component to the supplied archive.

Parameters

[in,out] archive The archive to write to.

Reimplemented from shark::RpropMinus.

Reimplemented in shark::IRpropPlusFull, and shark::IRpropPlus.

Member Data Documentation

◆ m_deltaw

RealVector shark::RpropPlus::m_deltaw

protected

The final update values for all weights.

Definition at line 280 of file Rprop.h.

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

include/shark/Algorithms/GradientDescent/Rprop.h

Public Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ RpropPlus()

Member Function Documentation

◆ init() [1/2]

◆ init() [2/2]

◆ name()

◆ read()

◆ step()

◆ write()

Member Data Documentation

◆ m_deltaw