OpenNN::GradientDescent Class Reference

#include <gradient_descent.h>

Inheritance diagram for OpenNN::GradientDescent:

Classes
struct	GradientDescentResults

Public Member Functions
	GradientDescent (void)
	GradientDescent (PerformanceFunctional *)
	GradientDescent (const tinyxml2::XMLDocument &)
virtual	~GradientDescent (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_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_training_rate_algorithm (const TrainingRateAlgorithm &)
void	set_default (void)
void	set_reserve_all_training_history (const bool &)
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_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_training_direction (const Vector< double > &) const
GradientDescentResults *	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_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 gradient descent training algorithm for a performance functional of a neural network.

Definition at line 43 of file gradient_descent.h.

Constructor & Destructor Documentation

OpenNN::GradientDescent::GradientDescent ( void  )

explicit

Default constructor. It creates a gradient descent 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 gradient_descent.cpp.

OpenNN::GradientDescent::GradientDescent ( PerformanceFunctional *  new_performance_functional_pointer )

explicit

Performance functional constructor. It creates a gradient descent 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 41 of file gradient_descent.cpp.

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

explicit

XML constructor. It creates a gradient descent training algorithm not associated to any performance functional object. It also loads the class members from a XML document.

Parameters

document

TinyXML document with the members of a gradient descent object.

Definition at line 57 of file gradient_descent.cpp.

Member Function Documentation

Vector< double > OpenNN::GradientDescent::calculate_training_direction

(

const Vector< double > &

gradient

)

const

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

Parameters

gradient

Performance function gradient.

Definition at line 915 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 2068 of file gradient_descent.cpp.

const double & OpenNN::GradientDescent::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 gradient_descent.cpp.

const double & OpenNN::GradientDescent::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 gradient_descent.cpp.

const double & OpenNN::GradientDescent::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 gradient_descent.cpp.

const double & OpenNN::GradientDescent::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 gradient_descent.cpp.

const double & OpenNN::GradientDescent::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 gradient_descent.cpp.

const double & OpenNN::GradientDescent::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 gradient_descent.cpp.

const double & OpenNN::GradientDescent::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 gradient_descent.cpp.

const double & OpenNN::GradientDescent::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 gradient_descent.cpp.

GradientDescent::GradientDescentResults * OpenNN::GradientDescent::perform_training ( void  )

virtual

Trains a neural network with an associated performance functional, according to the gradient descent method. Training occurs according to the training parameters and stopping criteria. It returns a results structure with the history and the final values of the reserved variables.

Implements OpenNN::TrainingAlgorithm.

Definition at line 1219 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 885 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 564 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 533 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 595 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 698 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 728 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 739 of file gradient_descent.cpp.

void OpenNN::GradientDescent::set_maximum_time

(

const double &

new_maximum_time

)

Sets a new maximum training time.

Parameters

new_maximum_time

Maximum training time.

Definition at line 750 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 625 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 655 of file gradient_descent.cpp.

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

virtual

Sets a pointer to a performance functional object to be associated to the gradient descent 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 341 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 686 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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.
• Parameters norm.
• Performance.
• Gradient.
• Gradient norm.
• Generalization performance.
• Training direction.
• Training direction norm.
• Training rate.

Parameters

new_reserve_all_training_history

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

Definition at line 412 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 861 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 873 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 813 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 825 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 780 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 791 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 802 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 837 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 849 of file gradient_descent.cpp.

void OpenNN::GradientDescent::set_training_rate_algorithm

(

const TrainingRateAlgorithm &

new_training_rate_algorithm

)

Sets a new training rate algorithm object into the gradient descent object.

Parameters

new_training_rate_algorithm

Object of the class TrainingRateAlgorithm

Definition at line 329 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 473 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 442 of file gradient_descent.cpp.

void OpenNN::GradientDescent::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 504 of file gradient_descent.cpp.

Matrix< std::string > OpenNN::GradientDescent::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 1573 of file gradient_descent.cpp.

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

virtual

Serializes the training parameters, the stopping criteria and other user stuff concerning the gradient descent object.

Reimplemented from OpenNN::TrainingAlgorithm.

Definition at line 1738 of file gradient_descent.cpp.

Member Data Documentation

size_t OpenNN::GradientDescent::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 352 of file gradient_descent.h.

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

• gradient_descent.h
• gradient_descent.cpp