Detailed Description

Optimizes the parameters of a differentiable function using an objective function.

#include <GOptimizer.h>

Inheritance diagram for GClasses::GNeuralNetOptimizer:

Public Member Functions
	GNeuralNetOptimizer (GNeuralNet &model, GRand &rand, GObjective *objective=NULL)

virtual	~GNeuralNetOptimizer ()

size_t	batchesPerEpoch () const

size_t	batchSize () const

virtual void	computeGradient (const GVec &feat, const GVec &lab)=0
	Evaluate feat and lab, and update the model's gradient. More...

GContextNeuralNet &	context ()
	Returns the default context for training the model. (Note: It is allocated lazily. This should not be called before layers are added to the model. For multi-threaded optimization, a separate context should be allocated for each thread.) More...

virtual void	descendGradient (double learningRate)=0
	Step the model's parameters in the direction of the calculated gradient scaled by learningRate. More...

size_t	epochs () const

double	improvementThresh () const

double	learningRate () const

GNeuralNet &	model ()

GObjective *	objective ()

void	optimize (const GMatrix &features, const GMatrix &labels)

virtual void	optimizeBatch (const GMatrix &features, const GMatrix &labels, size_t start, size_t batchSize)
	Update and apply the gradient for a single batch in order. More...

void	optimizeBatch (const GMatrix &features, const GMatrix &labels, size_t start)

virtual void	optimizeBatch (const GMatrix &features, const GMatrix &labels, GRandomIndexIterator &ii, size_t batchSize)
	Update and apply the gradient for a single batch in randomized order. More...

void	optimizeBatch (const GMatrix &features, const GMatrix &labels, GRandomIndexIterator &ii)

virtual void	optimizeIncremental (const GVec &feat, const GVec &lab)
	Update and apply the gradient for a single training sample (on-line). More...

void	optimizeWithValidation (const GMatrix &features, const GMatrix &labels, const GMatrix &validationFeat, const GMatrix &validationLab)

void	optimizeWithValidation (const GMatrix &features, const GMatrix &labels, double validationPortion=0.35)

virtual void	prepareForOptimizing ()=0
	Prepare for optimization (i.e. allocate delta vectors). More...

GRand &	rand ()

void	resetState ()
	Flushes the memory in any recurrent units in the network. This method should be called when beginning a new training sequence with neural networks that contain any recurrent blocks. More...

void	setBatchesPerEpoch (size_t b)

void	setBatchSize (size_t b)

void	setEpochs (size_t e)

void	setImprovementThresh (double m)

void	setLearningRate (double l)

void	setObjective (GObjective *objective)

void	setWindowSize (size_t w)

double	sumLoss (const GMatrix &features, const GMatrix &labels)

size_t	windowSize () const

Protected Attributes
size_t	m_batchesPerEpoch

size_t	m_batchSize

size_t	m_epochs

double	m_learningRate

double	m_minImprovement

GNeuralNet &	m_model

GObjective *	m_objective

GContextNeuralNet *	m_pContext

GRand &	m_rand

size_t	m_windowSize

Constructor & Destructor Documentation

GClasses::GNeuralNetOptimizer::GNeuralNetOptimizer	(	GNeuralNet &	model,
		GRand &	rand,
		GObjective *	objective = `NULL`
	)

virtual GClasses::GNeuralNetOptimizer::~GNeuralNetOptimizer ( )

virtual

Member Function Documentation

size_t GClasses::GNeuralNetOptimizer::batchesPerEpoch ( ) const

inline

size_t GClasses::GNeuralNetOptimizer::batchSize ( ) const

inline

virtual void GClasses::GNeuralNetOptimizer::computeGradient	(	const GVec &	feat,
		const GVec &	lab
	)

pure virtual

Evaluate feat and lab, and update the model's gradient.

Implemented in GClasses::GRMSPropOptimizer, GClasses::GAdamOptimizer, and GClasses::GSGDOptimizer.

GContextNeuralNet& GClasses::GNeuralNetOptimizer::context ( )

Returns the default context for training the model. (Note: It is allocated lazily. This should not be called before layers are added to the model. For multi-threaded optimization, a separate context should be allocated for each thread.)

virtual void GClasses::GNeuralNetOptimizer::descendGradient ( double learningRate )

pure virtual

Step the model's parameters in the direction of the calculated gradient scaled by learningRate.

Implemented in GClasses::GRMSPropOptimizer, GClasses::GAdamOptimizer, and GClasses::GSGDOptimizer.

size_t GClasses::GNeuralNetOptimizer::epochs ( ) const

inline

double GClasses::GNeuralNetOptimizer::improvementThresh ( ) const

inline

double GClasses::GNeuralNetOptimizer::learningRate ( ) const

inline

GNeuralNet& GClasses::GNeuralNetOptimizer::model ( )

inline

GObjective* GClasses::GNeuralNetOptimizer::objective ( )

inline

void GClasses::GNeuralNetOptimizer::optimize	(	const GMatrix &	features,
		const GMatrix &	labels
	)

virtual void GClasses::GNeuralNetOptimizer::optimizeBatch	(	const GMatrix &	features,
		const GMatrix &	labels,
		size_t	start,
		size_t	batchSize
	)

virtual

Update and apply the gradient for a single batch in order.

void GClasses::GNeuralNetOptimizer::optimizeBatch	(	const GMatrix &	features,
		const GMatrix &	labels,
		size_t	start
	)

virtual void GClasses::GNeuralNetOptimizer::optimizeBatch	(	const GMatrix &	features,
		const GMatrix &	labels,
		GRandomIndexIterator &	ii,
		size_t	batchSize
	)

virtual

Update and apply the gradient for a single batch in randomized order.

void GClasses::GNeuralNetOptimizer::optimizeBatch	(	const GMatrix &	features,
		const GMatrix &	labels,
		GRandomIndexIterator &	ii
	)

virtual void GClasses::GNeuralNetOptimizer::optimizeIncremental	(	const GVec &	feat,
		const GVec &	lab
	)

virtual

Update and apply the gradient for a single training sample (on-line).

void GClasses::GNeuralNetOptimizer::optimizeWithValidation	(	const GMatrix &	features,
		const GMatrix &	labels,
		const GMatrix &	validationFeat,
		const GMatrix &	validationLab
	)

void GClasses::GNeuralNetOptimizer::optimizeWithValidation	(	const GMatrix &	features,
		const GMatrix &	labels,
		double	validationPortion = `0.35`
	)

virtual void GClasses::GNeuralNetOptimizer::prepareForOptimizing ( )

pure virtual

Prepare for optimization (i.e. allocate delta vectors).

Implemented in GClasses::GRMSPropOptimizer, GClasses::GAdamOptimizer, and GClasses::GSGDOptimizer.

GRand& GClasses::GNeuralNetOptimizer::rand ( )

inline

void GClasses::GNeuralNetOptimizer::resetState ( )

Flushes the memory in any recurrent units in the network. This method should be called when beginning a new training sequence with neural networks that contain any recurrent blocks.

void GClasses::GNeuralNetOptimizer::setBatchesPerEpoch ( size_t b )

inline

void GClasses::GNeuralNetOptimizer::setBatchSize ( size_t b )

inline

void GClasses::GNeuralNetOptimizer::setEpochs ( size_t e )

inline

void GClasses::GNeuralNetOptimizer::setImprovementThresh ( double m )

inline

void GClasses::GNeuralNetOptimizer::setLearningRate ( double l )

inline

void GClasses::GNeuralNetOptimizer::setObjective ( GObjective * objective )

inline

void GClasses::GNeuralNetOptimizer::setWindowSize ( size_t w )

inline

double GClasses::GNeuralNetOptimizer::sumLoss	(	const GMatrix &	features,
		const GMatrix &	labels
	)

size_t GClasses::GNeuralNetOptimizer::windowSize ( ) const

inline

Member Data Documentation

size_t GClasses::GNeuralNetOptimizer::m_batchesPerEpoch

protected

size_t GClasses::GNeuralNetOptimizer::m_batchSize

protected

size_t GClasses::GNeuralNetOptimizer::m_epochs

protected

double GClasses::GNeuralNetOptimizer::m_learningRate

protected

double GClasses::GNeuralNetOptimizer::m_minImprovement

protected

GNeuralNet& GClasses::GNeuralNetOptimizer::m_model

protected

GObjective* GClasses::GNeuralNetOptimizer::m_objective

protected

GContextNeuralNet* GClasses::GNeuralNetOptimizer::m_pContext

protected

GRand& GClasses::GNeuralNetOptimizer::m_rand

protected

size_t GClasses::GNeuralNetOptimizer::m_windowSize

protected

Detailed Description

Public Member Functions

Protected Attributes

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation