Detailed Description

Trains a neural network by ADAM. See Diederik P. Kingma and Jimmy Lei Ba, "Adam: A Method for Stochastic Optimization", 2015.

#include <GOptimizer.h>

Inheritance diagram for GClasses::GAdamOptimizer:

Public Member Functions
	GAdamOptimizer (GNeuralNet &model, GRand &rand, GObjective *error=NULL)

double	beta1 () const

double	beta2 () const

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

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

double	epsilon () const

virtual void	prepareForOptimizing () override
	Prepare for optimization (i.e. allocate buffers). More...

void	setBeta1 (double b)

void	setBeta2 (double b)

void	setEpsilon (double e)

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

virtual	~GNeuralNetOptimizer ()

size_t	batchesPerEpoch () const

size_t	batchSize () const

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...

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)

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

Additional Inherited Members
Protected Attributes inherited from GClasses::GNeuralNetOptimizer
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::GAdamOptimizer::GAdamOptimizer	(	GNeuralNet &	model,
		GRand &	rand,
		GObjective *	error = `NULL`
	)

Member Function Documentation

double GClasses::GAdamOptimizer::beta1 ( ) const

inline

double GClasses::GAdamOptimizer::beta2 ( ) const

inline

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

overridevirtual

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

Implements GClasses::GNeuralNetOptimizer.

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

overridevirtual

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

Implements GClasses::GNeuralNetOptimizer.

double GClasses::GAdamOptimizer::epsilon ( ) const

inline

virtual void GClasses::GAdamOptimizer::prepareForOptimizing ( )

overridevirtual

Prepare for optimization (i.e. allocate buffers).

Implements GClasses::GNeuralNetOptimizer.

void GClasses::GAdamOptimizer::setBeta1 ( double b )

inline

void GClasses::GAdamOptimizer::setBeta2 ( double b )

inline

void GClasses::GAdamOptimizer::setEpsilon ( double e )

inline

Detailed Description

Public Member Functions

Additional Inherited Members

Constructor & Destructor Documentation

Member Function Documentation