#include <newton_method.h>

Inheritance diagram for OpenNN::NewtonMethod:

Classes
struct	NewtonMethodResults

Public Member Functions
	NewtonMethod (void)

	NewtonMethod (PerformanceFunctional *)

	NewtonMethod (const tinyxml2::XMLDocument &)

virtual	~NewtonMethod (void)

const TrainingRateAlgorithm &	get_training_rate_algorithm (void) const

TrainingRateAlgorithm *	get_training_rate_algorithm_pointer (void)

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_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_reserve_all_training_history (const bool &)

void	set_display_period (const size_t &)

Vector< double >	calculate_gradient_descent_training_direction (const Vector< double > &) const

Vector< double >	calculate_training_direction (const Vector< double > &, const Matrix< double > &) const

NewtonMethodResults *	perform_training (void)

std::string	write_training_algorithm_type (void) const

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

tinyxml2::XMLDocument *	to_XML (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

virtual std::string	to_string (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

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 the Newton method training algorithm for a performance functional of a neural network.

Definition at line 45 of file newton_method.h.

Constructor & Destructor Documentation

OpenNN::NewtonMethod::NewtonMethod ( void )

explicit

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

Definition at line 27 of file newton_method.cpp.

OpenNN::NewtonMethod::NewtonMethod ( PerformanceFunctional * new_performance_functional_pointer )

explicit

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

Parameters

new_performance_functional_pointer Pointer to a performance functional object.

Definition at line 41 of file newton_method.cpp.

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

explicit

XML Constructor. Creates a Newton method object, and loads its members from a XML document.

Parameters

document Pointer to a TinyXML document containing the Newton method data.

Definition at line 56 of file newton_method.cpp.

Member Function Documentation

Vector< double > OpenNN::NewtonMethod::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 886 of file newton_method.cpp.

Vector< double > OpenNN::NewtonMethod::calculate_training_direction	(	const Vector< double > &	gradient,
		const Matrix< double > &	inverse_Hessian
	)		const

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

Parameters

gradient	Gradient vector.
inverse_Hessian	Inverse Hessian matrix.

Definition at line 931 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 2134 of file newton_method.cpp.

const double & OpenNN::NewtonMethod::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 145 of file newton_method.cpp.

const double & OpenNN::NewtonMethod::get_error_parameters_norm ( void ) const

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

Definition at line 134 of file newton_method.cpp.

const double & OpenNN::NewtonMethod::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 156 of file newton_method.cpp.

const double & OpenNN::NewtonMethod::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 198 of file newton_method.cpp.

const double & OpenNN::NewtonMethod::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 187 of file newton_method.cpp.

const double & OpenNN::NewtonMethod::get_warning_gradient_norm ( void ) const

Returns the minimum value for the norm of the gradient vector at wich a warning message is written to the screen.

Definition at line 112 of file newton_method.cpp.

const double & OpenNN::NewtonMethod::get_warning_parameters_norm ( void ) const

Returns the minimum value for the norm of the parameters vector at wich a warning message is written to the screen.

Definition at line 101 of file newton_method.cpp.

const double & OpenNN::NewtonMethod::get_warning_training_rate ( void ) const

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

Definition at line 123 of file newton_method.cpp.

NewtonMethod::NewtonMethodResults * OpenNN::NewtonMethod::perform_training ( void )

virtual

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

Todo:

Implements OpenNN::TrainingAlgorithm.

Definition at line 1212 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 857 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 524 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 493 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 555 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 658 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 688 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 699 of file newton_method.cpp.

void OpenNN::NewtonMethod::set_maximum_time ( const double & new_maximum_time )

Sets a new maximum training time.

Parameters

new_maximum_time Maximum training time.

Definition at line 710 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 585 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 615 of file newton_method.cpp.

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

virtual

Sets a pointer to a performance functional object to be associated to the 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 340 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 646 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 833 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 845 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 773 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 785 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 797 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 740 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 751 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 762 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 809 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 821 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 433 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 402 of file newton_method.cpp.

void OpenNN::NewtonMethod::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 464 of file newton_method.cpp.

Matrix< std::string > OpenNN::NewtonMethod::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 1601 of file newton_method.cpp.

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

virtual

Prints to the screen the training parameters, the stopping criteria and other user stuff concerning the Newton's method object: Stopping criteria:

Performance goal.
Gradient norm goal.
Maximum training time.
Maximum number of iterations.

User stuff:

Display.
Display period.

Reimplemented from OpenNN::TrainingAlgorithm.

Definition at line 1778 of file newton_method.cpp.

Member Data Documentation

size_t OpenNN::NewtonMethod::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 353 of file newton_method.h.

TrainingRateAlgorithm OpenNN::NewtonMethod::training_rate_algorithm

private

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

Definition at line 305 of file newton_method.h.

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

Classes

Public Member Functions

Private Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation