#include <random_search.h>

Inheritance diagram for OpenNN::RandomSearch:

Classes
struct	RandomSearchResults

Public Member Functions
	RandomSearch (void)

	RandomSearch (PerformanceFunctional *)

	RandomSearch (const tinyxml2::XMLDocument &)

virtual	~RandomSearch (void)

const double &	get_training_rate_reduction_factor (void) const

const size_t &	get_training_rate_reduction_period (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 double &	get_warning_parameters_norm (void) const

const double &	get_warning_training_rate (void) const

const double &	get_error_parameters_norm (void) const

const double &	get_error_training_rate (void) const

const double &	get_performance_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_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_default (void)

void	set_first_training_rate (const double &)

void	set_training_rate_reduction_factor (const double &)

void	set_training_rate_reduction_period (const size_t &)

void	set_reserve_parameters_history (const bool &)

void	set_reserve_parameters_norm_history (const bool &)

void	set_reserve_performance_history (const bool &)

void	set_warning_parameters_norm (const double &)

void	set_warning_training_rate (const double &)

void	set_error_parameters_norm (const double &)

void	set_error_training_rate (const double &)

void	set_performance_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_generalization_performance_history (const bool &)

void	set_reserve_training_direction_history (const bool &)

void	set_reserve_training_direction_norm_history (const bool &)

void	set_reserve_training_rate_history (const bool &)

void	set_reserve_elapsed_time_history (const bool &)

virtual void	set_reserve_all_training_history (const bool &)

void	set_display_period (const size_t &)

Vector< double >	calculate_training_direction (void) const

RandomSearchResults *	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 *)

virtual void	set_performance_functional_pointer (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
double	first_training_rate

double	training_rate_reduction_factor

size_t	training_rate_reduction_period

double	warning_parameters_norm

double	warning_training_rate

double	error_parameters_norm

double	error_training_rate

double	performance_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_generalization_performance_history

bool	reserve_training_direction_history

bool	reserve_training_direction_norm_history

bool	reserve_training_rate_history

bool	reserve_elapsed_time_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 random search training algorithm for a performance functional of a neural network.

Definition at line 45 of file random_search.h.

Constructor & Destructor Documentation

OpenNN::RandomSearch::RandomSearch ( void )

explicit

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

Definition at line 27 of file random_search.cpp.

OpenNN::RandomSearch::RandomSearch ( PerformanceFunctional * new_performance_functional_pointer )

explicit

Performance functional constructor. It creates a random search training algorithm 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 random_search.cpp.

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

explicit

XML constructor. It creates a random search training algorithm not associated to any performance functional object. It also loads the rest of class members from a XML document.

Parameters

document TinyXML document containing the members of a random search object.

Definition at line 55 of file random_search.cpp.

OpenNN::RandomSearch::~RandomSearch ( void )

virtual

Destructor. It does not delete any object.

Definition at line 66 of file random_search.cpp.

Member Function Documentation

void OpenNN::RandomSearch::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 1559 of file random_search.cpp.

const double & OpenNN::RandomSearch::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 97 of file random_search.cpp.

const double & OpenNN::RandomSearch::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 108 of file random_search.cpp.

const double & OpenNN::RandomSearch::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 119 of file random_search.cpp.

RandomSearch::RandomSearchResults * OpenNN::RandomSearch::perform_training ( void )

virtual

Trains a neural network with an associated performance functional according to the random search training algorithm. Training occurs according to the training parameters.

Implements OpenNN::TrainingAlgorithm.

Definition at line 898 of file random_search.cpp.

void OpenNN::RandomSearch::set_default ( void )

virtual

Sets all the random search object members to their default values:

Training rate reduction factor: 0.9
Training rate reduction period: 10
Warning parameters norm: 1.0e6
Error parameters norm: 1.0e9
Performance goal: -1.0e99
Maximum time: 1.0e6
Maximum iterations number: 100
Reserve potential parameters history: False
Reserve potential parameters norm history: False
Reserve performance history: False.
Display: True
Display period: 10

Reimplemented from OpenNN::TrainingAlgorithm.

Definition at line 263 of file random_search.cpp.

void OpenNN::RandomSearch::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 650 of file random_search.cpp.

void OpenNN::RandomSearch::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 462 of file random_search.cpp.

void OpenNN::RandomSearch::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 493 of file random_search.cpp.

void OpenNN::RandomSearch::set_first_training_rate ( const double & new_first_training_rate )

Sets the initial training rate in the random search. The training rate is the step given in some training direction.

Parameters

new_first_training_rate Firs training rate value.

Definition at line 309 of file random_search.cpp.

void OpenNN::RandomSearch::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 535 of file random_search.cpp.

void OpenNN::RandomSearch::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 548 of file random_search.cpp.

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

Sets a new maximum training time.

Parameters

new_maximum_time Maximum training time.

Definition at line 559 of file random_search.cpp.

void OpenNN::RandomSearch::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 524 of file random_search.cpp.

void OpenNN::RandomSearch::set_reserve_all_training_history ( const bool & new_reserve_all_training_history )

virtual

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

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 378 of file random_search.cpp.

void OpenNN::RandomSearch::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 626 of file random_search.cpp.

void OpenNN::RandomSearch::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 638 of file random_search.cpp.

void OpenNN::RandomSearch::set_reserve_parameters_history ( const bool & new_reserve_parameters_history )

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

Parameters

new_reserve_parameters_history True if the potential parameters history is to be reserved, false otherwise.

Definition at line 342 of file random_search.cpp.

void OpenNN::RandomSearch::set_reserve_parameters_norm_history ( const bool & new_reserve_parameters_norm_history )

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

Parameters

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

Definition at line 354 of file random_search.cpp.

void OpenNN::RandomSearch::set_reserve_performance_history ( const bool & new_reserve_performance_history )

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

Parameters

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

Definition at line 366 of file random_search.cpp.

void OpenNN::RandomSearch::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 590 of file random_search.cpp.

void OpenNN::RandomSearch::set_reserve_training_direction_norm_history ( const bool & new_reserve_training_direction_norm_history )

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

Parameters

new_reserve_training_direction_norm_history True if the history of the norm of the training direction is to be reserved, false otherwise.

Definition at line 602 of file random_search.cpp.

void OpenNN::RandomSearch::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 614 of file random_search.cpp.

void OpenNN::RandomSearch::set_training_rate_reduction_factor ( const double & new_training_rate_reduction_factor )

Sets a new value for the reduction factor of the training rate.

Parameters

new_training_rate_reduction_factor Reduction factor value.

Definition at line 320 of file random_search.cpp.

void OpenNN::RandomSearch::set_training_rate_reduction_period ( const size_t & new_training_rate_reduction_period )

Sets a new period value for the reduction of the training rate. This is measured in training iterations.

Parameters

new_training_rate_reduction_period Reduction period for the training rate.

Definition at line 331 of file random_search.cpp.

void OpenNN::RandomSearch::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 401 of file random_search.cpp.

void OpenNN::RandomSearch::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 433 of file random_search.cpp.

Matrix< std::string > OpenNN::RandomSearch::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 1174 of file random_search.cpp.

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

virtual

Prints to the screen the training parameters, the stopping criteria and other user stuff concerning the random search object.

Reimplemented from OpenNN::TrainingAlgorithm.

Definition at line 1286 of file random_search.cpp.

Member Data Documentation

size_t OpenNN::RandomSearch::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 322 of file random_search.h.

double OpenNN::RandomSearch::training_rate_reduction_factor

private

Factor which reduces the training rate. It must be greater than zero and less than one.

Definition at line 290 of file random_search.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