OpenNN::QuasiNewtonMethod Class Reference

#include <quasi_newton_method.h>

Inheritance diagram for OpenNN::QuasiNewtonMethod:

Classes
struct	QuasiNewtonMethodResults

Public Types
enum	InverseHessianApproximationMethod { DFP, BFGS }

Public Member Functions
	QuasiNewtonMethod (void)
	QuasiNewtonMethod (PerformanceFunctional *)
	QuasiNewtonMethod (const tinyxml2::XMLDocument &)
virtual	~QuasiNewtonMethod (void)
const TrainingRateAlgorithm &	get_training_rate_algorithm (void) const
TrainingRateAlgorithm *	get_training_rate_algorithm_pointer (void)
const InverseHessianApproximationMethod &	get_inverse_Hessian_approximation_method (void) const
std::string	write_inverse_Hessian_approximation_method (void) const
const double &	get_warning_parameters_norm (void) const
const double &	get_warning_gradient_norm (void) const
const double &	get_warning_training_rate (void) const
const double &	get_error_parameters_norm (void) const
const double &	get_error_gradient_norm (void) const
const double &	get_error_training_rate (void) const
const double &	get_minimum_parameters_increment_norm (void) const
const double &	get_minimum_performance_increase (void) const
const double &	get_performance_goal (void) const
const double &	get_gradient_norm_goal (void) const
const size_t &	get_maximum_generalization_performance_decreases (void) const
const size_t &	get_maximum_iterations_number (void) const
const double &	get_maximum_time (void) const
const bool &	get_reserve_parameters_history (void) const
const bool &	get_reserve_parameters_norm_history (void) const
const bool &	get_reserve_performance_history (void) const
const bool &	get_reserve_gradient_history (void) const
const bool &	get_reserve_gradient_norm_history (void) const
const bool &	get_reserve_inverse_Hessian_history (void) const
const bool &	get_reserve_generalization_performance_history (void) const
const bool &	get_reserve_training_direction_history (void) const
const bool &	get_reserve_training_rate_history (void) const
const bool &	get_reserve_elapsed_time_history (void) const
void	set_performance_functional_pointer (PerformanceFunctional *)
void	set_inverse_Hessian_approximation_method (const InverseHessianApproximationMethod &)
void	set_inverse_Hessian_approximation_method (const std::string &)
void	set_default (void)
void	set_warning_parameters_norm (const double &)
void	set_warning_gradient_norm (const double &)
void	set_warning_training_rate (const double &)
void	set_error_parameters_norm (const double &)
void	set_error_gradient_norm (const double &)
void	set_error_training_rate (const double &)
void	set_minimum_parameters_increment_norm (const double &)
void	set_minimum_performance_increase (const double &)
void	set_performance_goal (const double &)
void	set_gradient_norm_goal (const double &)
void	set_maximum_generalization_performance_decreases (const size_t &)
void	set_maximum_iterations_number (const size_t &)
void	set_maximum_time (const double &)
void	set_reserve_parameters_history (const bool &)
void	set_reserve_parameters_norm_history (const bool &)
void	set_reserve_performance_history (const bool &)
void	set_reserve_gradient_history (const bool &)
void	set_reserve_gradient_norm_history (const bool &)
void	set_reserve_inverse_Hessian_history (const bool &)
void	set_reserve_generalization_performance_history (const bool &)
void	set_reserve_training_direction_history (const bool &)
void	set_reserve_training_rate_history (const bool &)
void	set_reserve_elapsed_time_history (const bool &)
void	set_display_period (const size_t &)
Vector< double >	calculate_gradient_descent_training_direction (const Vector< double > &) const
Matrix< double >	calculate_DFP_inverse_Hessian (const Vector< double > &, const Vector< double > &, const Vector< double > &, const Vector< double > &, const Matrix< double > &) const
Matrix< double >	calculate_BFGS_inverse_Hessian (const Vector< double > &, const Vector< double > &, const Vector< double > &, const Vector< double > &, const Matrix< double > &) const
Matrix< double >	calculate_inverse_Hessian_approximation (const Vector< double > &, const Vector< double > &, const Vector< double > &, const Vector< double > &, const Matrix< double > &) const
Vector< double >	calculate_training_direction (const Vector< double > &, const Matrix< double > &) const
QuasiNewtonMethodResults *	perform_training (void)
void	set_reserve_all_training_history (const bool &)
std::string	write_training_algorithm_type (void) const
tinyxml2::XMLDocument *	to_XML (void) const
std::string	to_string (void) const
Matrix< std::string >	to_string_matrix (void) const
void	from_XML (const tinyxml2::XMLDocument &)
Public Member Functions inherited from OpenNN::TrainingAlgorithm
	TrainingAlgorithm (void)
	TrainingAlgorithm (PerformanceFunctional *)
	TrainingAlgorithm (const tinyxml2::XMLDocument &)
virtual	~TrainingAlgorithm (void)
virtual TrainingAlgorithm &	operator= (const TrainingAlgorithm &)
virtual bool	operator== (const TrainingAlgorithm &) const
PerformanceFunctional *	get_performance_functional_pointer (void) const
bool	has_performance_functional (void) const
const bool &	get_display (void) const
const size_t &	get_display_period (void) const
const size_t &	get_save_period (void) const
const std::string &	get_neural_network_file_name (void) const
void	set (void)
void	set (PerformanceFunctional *)
void	set_display (const bool &)
void	set_display_period (const size_t &)
void	set_save_period (const size_t &)
void	set_neural_network_file_name (const std::string &)
virtual void	check (void) const
void	print (void) const
void	save (const std::string &) const
void	load (const std::string &)
virtual void	initialize_random (void)

Private Attributes
TrainingRateAlgorithm	training_rate_algorithm
InverseHessianApproximationMethod	inverse_Hessian_approximation_method
double	warning_parameters_norm
double	warning_gradient_norm
double	warning_training_rate
double	error_parameters_norm
double	error_gradient_norm
double	error_training_rate
double	minimum_parameters_increment_norm
double	minimum_performance_increase
double	performance_goal
double	gradient_norm_goal
size_t	maximum_generalization_performance_decreases
size_t	maximum_iterations_number
double	maximum_time
bool	reserve_parameters_history
bool	reserve_parameters_norm_history
bool	reserve_performance_history
bool	reserve_gradient_history
bool	reserve_gradient_norm_history
bool	reserve_inverse_Hessian_history
bool	reserve_training_direction_history
bool	reserve_training_rate_history
bool	reserve_elapsed_time_history
bool	reserve_generalization_performance_history

Additional Inherited Members
Protected Attributes inherited from OpenNN::TrainingAlgorithm
PerformanceFunctional *	performance_functional_pointer
size_t	display_period
size_t	save_period
std::string	neural_network_file_name
bool	display

Detailed Description

This concrete class represents a quasi-Newton training algorithm for a performance functional of a neural network.

Definition at line 47 of file quasi_newton_method.h.

Constructor & Destructor Documentation

OpenNN::QuasiNewtonMethod::QuasiNewtonMethod ( void  )

explicit

Default constructor. It creates a quasi-Newton method training algorithm not associated to any performance functional. It also initializes the class members to their default values.

Definition at line 29 of file quasi_newton_method.cpp.

OpenNN::QuasiNewtonMethod::QuasiNewtonMethod ( PerformanceFunctional *  new_performance_functional_pointer )

explicit

Performance functional constructor. It creates a quasi-Newton method training algorithm associated to a performance functional. It also initializes the class members to their default values.

Parameters

new_performance_functional_pointer

Pointer to a performance functional object.

Definition at line 43 of file quasi_newton_method.cpp.

OpenNN::QuasiNewtonMethod::QuasiNewtonMethod ( const tinyxml2::XMLDocument &  document )

explicit

XML constructor. It creates a quasi-Newton method training algorithm not associated to any performance functional. It also initializes the class members to their default values.

Definition at line 58 of file quasi_newton_method.cpp.

OpenNN::QuasiNewtonMethod::~QuasiNewtonMethod ( void  )

virtual

Destructor. It does not delete any object.

Definition at line 70 of file quasi_newton_method.cpp.

Member Function Documentation

Matrix< double > OpenNN::QuasiNewtonMethod::calculate_BFGS_inverse_Hessian	(	const Vector< double > &	old_parameters,
		const Vector< double > &	parameters,
		const Vector< double > &	old_gradient,
		const Vector< double > &	gradient,
		const Matrix< double > &	old_inverse_Hessian
	)		const

Returns an approximation of the inverse Hessian matrix according to the Broyden-Fletcher-Goldfarb-Shanno (BGFS) algorithm.

Parameters

old_parameters	A previous set of parameters.
old_gradient	The gradient of the objective function for that previous set of parameters.
old_inverse_Hessian	The Hessian of the objective function for that previous set of parameters.
parameters	Actual set of parameters.
gradient	The gradient of the objective function for the actual set of parameters.

Definition at line 1264 of file quasi_newton_method.cpp.

Matrix< double > OpenNN::QuasiNewtonMethod::calculate_DFP_inverse_Hessian	(	const Vector< double > &	old_parameters,
		const Vector< double > &	parameters,
		const Vector< double > &	old_gradient,
		const Vector< double > &	gradient,
		const Matrix< double > &	old_inverse_Hessian
	)		const

Returns an approximation of the inverse Hessian matrix according to the Davidon-Fletcher-Powel (DFP) algorithm.

Parameters

old_parameters	A previous set of parameters.
old_gradient	The gradient of the objective function for that previous set of parameters.
old_inverse_Hessian	The Hessian of the objective function for that previous set of parameters.
parameters	Actual set of parameters.
gradient	The gradient of the objective function for the actual set of parameters.

Definition at line 1113 of file quasi_newton_method.cpp.

Vector< double > OpenNN::QuasiNewtonMethod::calculate_gradient_descent_training_direction

(

const Vector< double > &

gradient

)

const

Returns the gradient descent training direction, which is the negative of the normalized gradient.

Parameters

gradient

Gradient vector.

Definition at line 1061 of file quasi_newton_method.cpp.

Matrix< double > OpenNN::QuasiNewtonMethod::calculate_inverse_Hessian_approximation	(	const Vector< double > &	old_parameters,
		const Vector< double > &	parameters,
		const Vector< double > &	old_gradient,
		const Vector< double > &	gradient,
		const Matrix< double > &	old_inverse_Hessian
	)		const

Calculates an approximation of the inverse Hessian, accoring to the method used.

Parameters

old_parameters	Another point of the objective function.
parameters	Current point of the objective function
old_gradient	Gradient at the other point.
gradient	Gradient at the current point.
old_inverse_Hessian	Inverse Hessian at the other point of the objective function.

Definition at line 1010 of file quasi_newton_method.cpp.

Vector< double > OpenNN::QuasiNewtonMethod::calculate_training_direction	(	const Vector< double > &	gradient,
		const Matrix< double > &	inverse_Hessian_approximation
	)		const

Returns the quasi-Newton method training direction, which has been previously normalized.

Parameters

gradient	Gradient vector.
inverse_Hessian_approximation	Inverse Hessian approximation matrix.

Definition at line 1050 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::from_XML ( const tinyxml2::XMLDocument &  document )

virtual

Loads a default training algorithm from a XML document.

Parameters

document

TinyXML document containing the performance term members.

Reimplemented from OpenNN::TrainingAlgorithm.

Definition at line 2712 of file quasi_newton_method.cpp.

const double & OpenNN::QuasiNewtonMethod::get_error_gradient_norm

(

void

)

const

Returns the value for the norm of the gradient vector at wich an error message is written to the screen and the program exits.

Definition at line 189 of file quasi_newton_method.cpp.

const double & OpenNN::QuasiNewtonMethod::get_error_training_rate

(

void

)

const

Returns the training rate value at wich the line minimization algorithm is assumed to fail when bracketing a minimum.

Definition at line 200 of file quasi_newton_method.cpp.

const double & OpenNN::QuasiNewtonMethod::get_gradient_norm_goal

(

void

)

const

Returns the goal value for the norm of the objective function gradient. This is used as a stopping criterion when training a multilayer perceptron

Definition at line 242 of file quasi_newton_method.cpp.

const double & OpenNN::QuasiNewtonMethod::get_performance_goal

(

void

)

const

Returns the goal value for the performance. This is used as a stopping criterion when training a multilayer perceptron

Definition at line 231 of file quasi_newton_method.cpp.

QuasiNewtonMethod::QuasiNewtonMethodResults * OpenNN::QuasiNewtonMethod::perform_training ( void  )

virtual

Trains a neural network with an associated performance functional according to the quasi-Newton method. Training occurs according to the training operators, training parameters and stopping criteria.

Implements OpenNN::TrainingAlgorithm.

Definition at line 1707 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_display_period

(

const size_t &

new_display_period

)

Sets a new number of iterations between the training showing progress.

Parameters

new_display_period

Number of iterations between the training showing progress.

Definition at line 975 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_error_gradient_norm

(

const double &

new_error_gradient_norm

)

Sets a new value for the gradient vector norm at which an error message is written to the screen and the program exits.

Parameters

new_error_gradient_norm

Error norm of gradient vector value.

Definition at line 638 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_error_parameters_norm

(

const double &

new_error_parameters_norm

)

Sets a new value for the parameters vector norm at which an error message is written to the screen and the program exits.

Parameters

new_error_parameters_norm

Error norm of parameters vector value.

Definition at line 607 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_error_training_rate

(

const double &

new_error_training_rate

)

Sets a new training rate value at wich a the line minimization algorithm is assumed to fail when bracketing a minimum.

Parameters

new_error_training_rate

Error training rate value.

Definition at line 669 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_gradient_norm_goal

(

const double &

new_gradient_norm_goal

)

Sets a new the goal value for the norm of the objective function gradient. This is used as a stopping criterion when training a multilayer perceptron

Parameters

new_gradient_norm_goal

Goal value for the norm of the objective function gradient.

Definition at line 772 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_inverse_Hessian_approximation_method

(

const InverseHessianApproximationMethod &

new_inverse_Hessian_approximation_method

)

Sets a new inverse Hessian approximatation method value.

Parameters

new_inverse_Hessian_approximation_method

Inverse Hessian approximation method value.

Definition at line 399 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_inverse_Hessian_approximation_method

(

const std::string &

new_inverse_Hessian_approximation_method_name

)

Sets a new method for approximating the inverse of the Hessian matrix from a string containing the name. Possible values are:

• "DFP"
• "BFGS"

Parameters

new_inverse_Hessian_approximation_method_name

Name of inverse Hessian approximation method.

Definition at line 416 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_maximum_generalization_performance_decreases

(

const size_t &

new_maximum_generalization_performance_decreases

)

Sets a new maximum number of generalization failures.

Parameters

new_maximum_generalization_performance_decreases

Maximum number of iterations in which the generalization evalutation decreases.

Definition at line 802 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_maximum_iterations_number

(

const size_t &

new_maximum_iterations_number

)

Sets a maximum number of iterations for training.

Parameters

new_maximum_iterations_number

Maximum number of iterations for training.

Definition at line 815 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_maximum_time

(

const double &

new_maximum_time

)

Sets a new maximum training time.

Parameters

new_maximum_time

Maximum training time.

Definition at line 828 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_minimum_parameters_increment_norm

(

const double &

new_minimum_parameters_increment_norm

)

Sets a new value for the minimum parameters increment norm stopping criterion.

Parameters

new_minimum_parameters_increment_norm

Value of norm of parameters increment norm used to stop training.

Definition at line 699 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_minimum_performance_increase

(

const double &

new_minimum_performance_increase

)

Sets a new minimum performance improvement during training.

Parameters

new_minimum_performance_increase

Minimum improvement in the performance between two iterations.

Definition at line 729 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_performance_functional_pointer ( PerformanceFunctional *  new_performance_functional_pointer )

virtual

Sets a pointer to a performance functional object to be associated to the quasi-Newton method object. It also sets that performance functional to the training rate algorithm.

Parameters

new_performance_functional_pointer

Pointer to a performance functional object.

Reimplemented from OpenNN::TrainingAlgorithm.

Definition at line 386 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_performance_goal

(

const double &

new_performance_goal

)

Sets a new goal value for the performance. This is used as a stopping criterion when training a multilayer perceptron

Parameters

new_performance_goal

Goal value for the performance.

Definition at line 760 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_reserve_all_training_history

(

const bool &

new_reserve_all_training_history

)

Makes the training history of all variables to reseved or not in memory.

Parameters

new_reserve_all_training_history

True if the training history of all variables is to be reserved, false otherwise.

Definition at line 445 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_reserve_elapsed_time_history

(

const bool &

new_reserve_elapsed_time_history

)

Makes the elapsed time over the iterations to be reseved or not in memory. This is a vector.

Parameters

new_reserve_elapsed_time_history

True if the elapsed time history vector is to be reserved, false otherwise.

Definition at line 951 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_reserve_generalization_performance_history

(

const bool &

new_reserve_generalization_performance_history

)

Makes the Generalization performance history to be reserved or not in memory. This is a vector.

Parameters

new_reserve_generalization_performance_history

True if the Generalization performance history is to be reserved, false otherwise.

Definition at line 963 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_reserve_gradient_history

(

const bool &

new_reserve_gradient_history

)

Makes the gradient history vector of vectors to be reseved or not in memory.

Parameters

new_reserve_gradient_history

True if the gradient history matrix is to be reserved, false otherwise.

Definition at line 891 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_reserve_gradient_norm_history

(

const bool &

new_reserve_gradient_norm_history

)

Makes the gradient norm history vector to be reseved or not in memory.

Parameters

new_reserve_gradient_norm_history

True if the gradient norm history matrix is to be reserved, false otherwise.

Definition at line 903 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_reserve_inverse_Hessian_history

(

const bool &

new_reserve_inverse_Hessian_history

)

Sets the history of the inverse of the Hessian matrix to be reserved or not in memory. This is a vector of matrices.

Parameters

new_reserve_inverse_Hessian_history

True if the inverse Hessian history is to be reserved, false otherwise.

Definition at line 915 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_reserve_parameters_history

(

const bool &

new_reserve_parameters_history

)

Makes the parameters history vector of vectors to be reseved or not in memory.

Parameters

new_reserve_parameters_history

True if the parameters history vector of vectors is to be reserved, false otherwise.

Definition at line 858 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_reserve_parameters_norm_history

(

const bool &

new_reserve_parameters_norm_history

)

Makes the parameters norm history vector to be reseved or not in memory.

Parameters

new_reserve_parameters_norm_history

True if the parameters norm history vector is to be reserved, false otherwise.

Definition at line 869 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_reserve_performance_history

(

const bool &

new_reserve_performance_history

)

Makes the performance history vector to be reseved or not in memory.

Parameters

new_reserve_performance_history

True if the performance history vector is to be reserved, false otherwise.

Definition at line 880 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_reserve_training_direction_history

(

const bool &

new_reserve_training_direction_history

)

Makes the training direction history vector of vectors to be reseved or not in memory.

Parameters

new_reserve_training_direction_history

True if the training direction history matrix is to be reserved, false otherwise.

Definition at line 927 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_reserve_training_rate_history

(

const bool &

new_reserve_training_rate_history

)

Makes the training rate history vector to be reseved or not in memory.

Parameters

new_reserve_training_rate_history

True if the training rate history vector is to be reserved, false otherwise.

Definition at line 939 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_warning_gradient_norm

(

const double &

new_warning_gradient_norm

)

Sets a new value for the gradient vector norm at which a warning message is written to the screen.

Parameters

new_warning_gradient_norm

Warning norm of gradient vector value.

Definition at line 547 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_warning_parameters_norm

(

const double &

new_warning_parameters_norm

)

Sets a new value for the parameters vector norm at which a warning message is written to the screen.

Parameters

new_warning_parameters_norm

Warning norm of parameters vector value.

Definition at line 516 of file quasi_newton_method.cpp.

void OpenNN::QuasiNewtonMethod::set_warning_training_rate

(

const double &

new_warning_training_rate

)

Sets a new training rate value at wich a warning message is written to the screen during line minimization.

Parameters

new_warning_training_rate

Warning training rate value.

Definition at line 578 of file quasi_newton_method.cpp.

Matrix< std::string > OpenNN::QuasiNewtonMethod::to_string_matrix ( void  ) const

virtual

Returns a default (empty) string matrix containing the members of the training algorithm object.

Reimplemented from OpenNN::TrainingAlgorithm.

Definition at line 2542 of file quasi_newton_method.cpp.

tinyxml2::XMLDocument * OpenNN::QuasiNewtonMethod::to_XML ( void  ) const

virtual

Returns a XML-type string representation of this quasi-Newton method object. It contains the training methods and parameters chosen, as well as the stopping criteria and other user stuff concerning the quasi-Newton method object.

Reimplemented from OpenNN::TrainingAlgorithm.

Definition at line 2149 of file quasi_newton_method.cpp.

Member Data Documentation

size_t OpenNN::QuasiNewtonMethod::maximum_generalization_performance_decreases

private

Maximum number of iterations at which the generalization performance decreases. This is an early stopping method for improving generalization.

Definition at line 399 of file quasi_newton_method.h.

TrainingRateAlgorithm OpenNN::QuasiNewtonMethod::training_rate_algorithm

private

Training rate algorithm object. It is used to calculate the step for the quasi-Newton training direction.

Definition at line 346 of file quasi_newton_method.h.

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

• quasi_newton_method.h
• quasi_newton_method.cpp