OpenNN::ConjugateGradient Class Reference

#include <conjugate_gradient.h>

Inheritance diagram for OpenNN::ConjugateGradient:

OpenNN::TrainingAlgorithm

List of all members.

Classes

struct  ConjugateGradientResults

Public Types

enum  TrainingDirectionMethod { PR, FR }

Public Member Functions

 ConjugateGradient (void)
 ConjugateGradient (PerformanceFunctional *)
 ConjugateGradient (TiXmlElement *)
virtual ~ConjugateGradient (void)
const TrainingRateAlgorithmget_training_rate_algorithm (void) const
TrainingRateAlgorithmget_training_rate_algorithm_pointer (void)
const TrainingDirectionMethodget_training_direction_method (void) const
std::string write_training_direction_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 unsigned int & get_maximum_generalization_evaluation_decreases (void) const
const double & get_gradient_norm_goal (void) const
const unsigned int & get_maximum_epochs_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_evaluation_history (void) const
const bool & get_reserve_generalization_evaluation_history (void) const
const bool & get_reserve_gradient_history (void) const
const bool & get_reserve_gradient_norm_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
const unsigned int & get_display_period (void) const
void set_default (void)
void set_training_direction_method (const TrainingDirectionMethod &)
void set_training_direction_method (const std::string &)
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_performance_goal (const double &)
void set_minimum_performance_increase (const double &)
void set_maximum_generalization_evaluation_decreases (const unsigned int &)
void set_gradient_norm_goal (const double &)
void set_maximum_epochs_number (const unsigned int &)
void set_maximum_time (const double &)
void set_reserve_parameters_history (const bool &)
void set_reserve_parameters_norm_history (const bool &)
void set_reserve_evaluation_history (const bool &)
void set_reserve_generalization_evaluation_history (const bool &)
void set_reserve_gradient_history (const bool &)
void set_reserve_gradient_norm_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 unsigned int &)
double calculate_PR_parameter (const Vector< double > &, const Vector< double > &) const
double calculate_FR_parameter (const Vector< double > &, const Vector< double > &) const
Vector< double > calculate_PR_training_direction (const Vector< double > &, const Vector< double > &, const Vector< double > &) const
Vector< double > calculate_FR_training_direction (const Vector< double > &, const Vector< double > &, const Vector< double > &) const
Vector< double > calculate_training_direction (const Vector< double > &, const Vector< double > &, const Vector< double > &) const
Vector< double > calculate_gradient_descent_training_direction (const Vector< double > &) const
ConjugateGradientResultsperform_training (void)
std::string write_training_algorithm_type (void) const
TiXmlElement * to_XML (void) const
void from_XML (TiXmlElement *)


Detailed Description

This concrete class represents a conjugate gradient training algorithm for a performance functional of a neural network.

Definition at line 35 of file conjugate_gradient.h.


Member Enumeration Documentation

Enumeration of the available training operators for obtaining the training direction.

Definition at line 44 of file conjugate_gradient.h.


Constructor & Destructor Documentation

OpenNN::ConjugateGradient::ConjugateGradient ( void   )  [explicit]

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

Definition at line 46 of file conjugate_gradient.cpp.

OpenNN::ConjugateGradient::ConjugateGradient ( PerformanceFunctional new_performance_functional_pointer  )  [explicit]

General constructor. It creates a conjugate gradient training algorithm associated to a performance functional object. It also initializes the rest of class members to their default values.

Parameters:
new_performance_functional_pointer Pointer to a performance functional object.

Definition at line 59 of file conjugate_gradient.cpp.

OpenNN::ConjugateGradient::ConjugateGradient ( TiXmlElement *  conjugate_gradient_element  )  [explicit]

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

Parameters:
conjugate_gradient_element Tiny XML element with the members of a conjugate gradient object.

Definition at line 75 of file conjugate_gradient.cpp.

OpenNN::ConjugateGradient::~ConjugateGradient ( void   )  [virtual]

Destructor.

Definition at line 88 of file conjugate_gradient.cpp.


Member Function Documentation

const TrainingRateAlgorithm & OpenNN::ConjugateGradient::get_training_rate_algorithm ( void   )  const

This method returns a constant reference to the training rate algorithm object inside the conjugate gradient method object.

Definition at line 99 of file conjugate_gradient.cpp.

TrainingRateAlgorithm * OpenNN::ConjugateGradient::get_training_rate_algorithm_pointer ( void   ) 

This method returns a pointer to the training rate algorithm object inside the conjugate gradient method object.

Definition at line 109 of file conjugate_gradient.cpp.

const ConjugateGradient::TrainingDirectionMethod & OpenNN::ConjugateGradient::get_training_direction_method ( void   )  const

This method returns the conjugate gradient training direction method used for training.

Definition at line 119 of file conjugate_gradient.cpp.

std::string OpenNN::ConjugateGradient::write_training_direction_method ( void   )  const

This method returns a string with the name of the training direction.

Definition at line 129 of file conjugate_gradient.cpp.

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

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

Definition at line 164 of file conjugate_gradient.cpp.

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

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

Definition at line 174 of file conjugate_gradient.cpp.

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

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

Definition at line 184 of file conjugate_gradient.cpp.

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

This method 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 194 of file conjugate_gradient.cpp.

const double & OpenNN::ConjugateGradient::get_error_gradient_norm ( void   )  const

This method 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 205 of file conjugate_gradient.cpp.

const double & OpenNN::ConjugateGradient::get_error_training_rate ( void   )  const

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

Definition at line 216 of file conjugate_gradient.cpp.

const double & OpenNN::ConjugateGradient::get_minimum_parameters_increment_norm ( void   )  const

This method returns the minimum norm of the parameter increment vector used as a stopping criteria when training.

Definition at line 226 of file conjugate_gradient.cpp.

const double & OpenNN::ConjugateGradient::get_minimum_performance_increase ( void   )  const

This method returns the minimum performance improvement during training.

Definition at line 236 of file conjugate_gradient.cpp.

const double & OpenNN::ConjugateGradient::get_performance_goal ( void   )  const

This method returns the goal value for the performance. This is used as a stopping criterium when training a multilayer perceptron

Definition at line 247 of file conjugate_gradient.cpp.

const unsigned int & OpenNN::ConjugateGradient::get_maximum_generalization_evaluation_decreases ( void   )  const

This method returns the maximum number of generalization failures during the training process.

Definition at line 268 of file conjugate_gradient.cpp.

const double & OpenNN::ConjugateGradient::get_gradient_norm_goal ( void   )  const

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

Definition at line 258 of file conjugate_gradient.cpp.

const unsigned int & OpenNN::ConjugateGradient::get_maximum_epochs_number ( void   )  const

This method returns the maximum number of epochs for training.

Definition at line 278 of file conjugate_gradient.cpp.

const double & OpenNN::ConjugateGradient::get_maximum_time ( void   )  const

This method returns the maximum training time.

Definition at line 288 of file conjugate_gradient.cpp.

const bool & OpenNN::ConjugateGradient::get_reserve_parameters_history ( void   )  const

This method returns true if the parameters history matrix is to be reserved, and false otherwise.

Definition at line 298 of file conjugate_gradient.cpp.

const bool & OpenNN::ConjugateGradient::get_reserve_parameters_norm_history ( void   )  const

This method returns true if the parameters norm history vector is to be reserved, and false otherwise.

Definition at line 308 of file conjugate_gradient.cpp.

const bool & OpenNN::ConjugateGradient::get_reserve_evaluation_history ( void   )  const

This method returns true if the evaluation history vector is to be reserved, and false otherwise.

Definition at line 318 of file conjugate_gradient.cpp.

const bool & OpenNN::ConjugateGradient::get_reserve_generalization_evaluation_history ( void   )  const

This method returns true if the Generalization evaluation history vector is to be reserved, and false otherwise.

Definition at line 379 of file conjugate_gradient.cpp.

const bool & OpenNN::ConjugateGradient::get_reserve_gradient_history ( void   )  const

This method returns true if the gradient history vector of vectors is to be reserved, and false otherwise.

Definition at line 328 of file conjugate_gradient.cpp.

const bool & OpenNN::ConjugateGradient::get_reserve_gradient_norm_history ( void   )  const

This method returns true if the gradient norm history vector is to be reserved, and false otherwise.

Definition at line 338 of file conjugate_gradient.cpp.

const bool & OpenNN::ConjugateGradient::get_reserve_training_direction_history ( void   )  const

This method returns true if the training direction history matrix is to be reserved, and false otherwise.

Definition at line 349 of file conjugate_gradient.cpp.

const bool & OpenNN::ConjugateGradient::get_reserve_training_rate_history ( void   )  const

This method returns true if the training rate history vector is to be reserved, and false otherwise.

Definition at line 359 of file conjugate_gradient.cpp.

const bool & OpenNN::ConjugateGradient::get_reserve_elapsed_time_history ( void   )  const

This method returns true if the elapsed time history vector is to be reserved, and false otherwise.

Definition at line 369 of file conjugate_gradient.cpp.

const unsigned int & OpenNN::ConjugateGradient::get_display_period ( void   )  const

This method returns the number of epochs between the training showing progress.

Definition at line 389 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_default ( void   )  [virtual]

This method sets the default values into a conjugate gradient object. Training operators:

  • Training direction method = Polak-Ribiere;
  • Training rate method = Brent;
Training parameters:
  • First training rate: 1.0.
  • Bracketing factor: 2.0.
  • Training rate tolerance: 1.0e-3.
Stopping criteria:
  • Performance goal: -1.0e99.
  • Gradient norm goal: 0.0.
  • Maximum training time: 1.0e6.
  • Maximum number of epochs: 100.
User stuff:
  • Warning training rate: 1.0e6.
  • Error training rate: 1.0e12.
  • Display: true.
  • Display period: 25.
Reserve:
  • Reserve training direction history: false.
  • Reserve training direction norm history: false.
  • Reserve training rate history: false.

Reimplemented from OpenNN::TrainingAlgorithm.

Definition at line 520 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_training_direction_method ( const TrainingDirectionMethod new_training_direction_method  ) 

This method sets a new training direction method to be used for training.

Parameters:
new_training_direction_method Conjugate gradient training direction method.

Definition at line 403 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_training_direction_method ( const std::string &  new_training_direction_method_name  ) 

This method sets a new conjugate gradient training direction from a string representation. Possible values are:

  • "PR"
  • "FR"
Parameters:
new_training_direction_method_name String with the name of the training direction method.

Definition at line 419 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_warning_parameters_norm ( const double &  new_warning_parameters_norm  ) 

This method 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 574 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_warning_gradient_norm ( const double &  new_warning_gradient_norm  ) 

This method 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 605 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_warning_training_rate ( const double &  new_warning_training_rate  ) 

This method 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 636 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_error_parameters_norm ( const double &  new_error_parameters_norm  ) 

This method 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 665 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_error_gradient_norm ( const double &  new_error_gradient_norm  ) 

This method 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 696 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_error_training_rate ( const double &  new_error_training_rate  ) 

This method 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 727 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_minimum_parameters_increment_norm ( const double &  new_minimum_parameters_increment_norm  ) 

This method sets a new value for the minimum parameters increment norm stopping criterium.

Parameters:
new_minimum_parameters_increment_norm Value of norm of parameters increment norm used to stop training.

Definition at line 757 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_performance_goal ( const double &  new_performance_goal  ) 

This method sets a new goal value for the performance. This is used as a stopping criterium when training a multilayer perceptron

Parameters:
new_performance_goal Goal value for the performance.

Definition at line 818 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_minimum_performance_increase ( const double &  new_minimum_performance_increase  ) 

This method sets a new minimum performance improvement during training.

Parameters:
new_minimum_performance_increase Minimum improvement in the performance between two epochs.

Definition at line 787 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_maximum_generalization_evaluation_decreases ( const unsigned int &  new_maximum_generalization_evaluation_decreases  ) 

This method sets a new maximum number of generalization failures.

Parameters:
new_maximum_generalization_evaluation_decreases Maximum number of epochs in which the generalization evalutation decreases.

Definition at line 860 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_gradient_norm_goal ( const double &  new_gradient_norm_goal  ) 

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

Parameters:
new_gradient_norm_goal Goal value for the norm of the objective function gradient.

Definition at line 830 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_maximum_epochs_number ( const unsigned int &  new_maximum_epochs_number  ) 

This method sets a maximum number of epochs for training.

Parameters:
new_maximum_epochs_number Maximum number of epochs for training.

Definition at line 890 of file conjugate_gradient.cpp.

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

This method sets a new maximum training time.

Parameters:
new_maximum_time Maximum training time.

Definition at line 920 of file conjugate_gradient.cpp.

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

This method 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 950 of file conjugate_gradient.cpp.

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

This method 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 961 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_reserve_evaluation_history ( const bool &  new_reserve_evaluation_history  ) 

This method makes the evaluation history vector to be reseved or not in memory.

Parameters:
new_reserve_evaluation_history True if the evaluation history vector is to be reserved, false otherwise.

Definition at line 972 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_reserve_generalization_evaluation_history ( const bool &  new_reserve_generalization_evaluation_history  ) 

This method makes the Generalization evaluation history to be reserved or not in memory. This is a vector.

Parameters:
new_reserve_generalization_evaluation_history True if the Generalization evaluation history is to be reserved, false otherwise.

Definition at line 1043 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_reserve_gradient_history ( const bool &  new_reserve_gradient_history  ) 

This method 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 983 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_reserve_gradient_norm_history ( const bool &  new_reserve_gradient_norm_history  ) 

This method 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 995 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_reserve_training_direction_history ( const bool &  new_reserve_training_direction_history  ) 

This method 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 1007 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_reserve_training_rate_history ( const bool &  new_reserve_training_rate_history  ) 

This method 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 1019 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_reserve_elapsed_time_history ( const bool &  new_reserve_elapsed_time_history  ) 

This method makes the elapsed time over the epochs 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 1031 of file conjugate_gradient.cpp.

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

This method makes the training history of all variables to reseved or not in memory when training.

  • Parameters.
  • Parameters norm.
  • Evaluation.
  • Gradient.
  • Gradient norm.
  • Generalization performance.
  • Training direction.
  • Training direction norm.
  • Training rate.

Parameters:
new_reserve_all_training_history True if all training history variables are to be reserved, false otherwise.

Definition at line 460 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::set_display_period ( const unsigned int &  new_display_period  ) 

This method sets a new number of epochs between the training showing progress.

Parameters:
new_display_period Number of epochs between the training showing progress.

Definition at line 1055 of file conjugate_gradient.cpp.

double OpenNN::ConjugateGradient::calculate_PR_parameter ( const Vector< double > &  old_gradient,
const Vector< double > &  gradient 
) const

This method returns the Polak-Ribiere parameter used to calculate the training direction.

Parameters:
old_gradient Previous objective function gradient.
gradient Current objective function gradient.

Definition at line 1122 of file conjugate_gradient.cpp.

double OpenNN::ConjugateGradient::calculate_FR_parameter ( const Vector< double > &  old_gradient,
const Vector< double > &  gradient 
) const

This method returns the Fletcher-Reeves parameter used to calculate the training direction.

Parameters:
old_gradient Previous objective function gradient.
gradient,: Current objective function gradient.

Definition at line 1086 of file conjugate_gradient.cpp.

Vector< double > OpenNN::ConjugateGradient::calculate_PR_training_direction ( const Vector< double > &  old_gradient,
const Vector< double > &  gradient,
const Vector< double > &  old_training_direction 
) const

This method returns the training direction using the Polak-Ribiere update.

Parameters:
old_gradient Previous objective function gradient.
gradient Current objective function gradient.
old_training_direction Previous training direction vector.

Definition at line 1160 of file conjugate_gradient.cpp.

Vector< double > OpenNN::ConjugateGradient::calculate_FR_training_direction ( const Vector< double > &  old_gradient,
const Vector< double > &  gradient,
const Vector< double > &  old_training_direction 
) const

This method returns the training direction using the Fletcher-Reeves update.

Parameters:
old_gradient Previous objective function gradient.
gradient Current objective function gradient.
old_training_direction Previous training direction vector.

Definition at line 1183 of file conjugate_gradient.cpp.

Vector< double > OpenNN::ConjugateGradient::calculate_training_direction ( const Vector< double > &  old_gradient,
const Vector< double > &  gradient,
const Vector< double > &  old_training_direction 
) const

This method returns the conjugate gradient training direction, which has been previously normalized.

Parameters:
old_gradient Gradient vector in the previous epoch.
gradient Current gradient vector.
old_training_direction Training direction in the previous epoch.

Definition at line 1206 of file conjugate_gradient.cpp.

Vector< double > OpenNN::ConjugateGradient::calculate_gradient_descent_training_direction ( const Vector< double > &  gradient  )  const

This method returns the gradient descent training direction, which is the negative of the normalized gradient.

Parameters:
gradient Gradient vector.

Definition at line 1242 of file conjugate_gradient.cpp.

ConjugateGradient::ConjugateGradientResults * OpenNN::ConjugateGradient::perform_training ( void   )  [virtual]

This method trains a neural network with an associated performance functional according to the conjugate gradient algorithm. Training occurs according to the training operators, training parameters and stopping criteria.

Implements OpenNN::TrainingAlgorithm.

Definition at line 1367 of file conjugate_gradient.cpp.

std::string OpenNN::ConjugateGradient::write_training_algorithm_type ( void   )  const [virtual]

This method writes a string with the type of training algoritm.

Reimplemented from OpenNN::TrainingAlgorithm.

Definition at line 1773 of file conjugate_gradient.cpp.

TiXmlElement * OpenNN::ConjugateGradient::to_XML ( void   )  const [virtual]

This method serializes the conjugate gradient object into a XML element of the TinyXML library. See the OpenNN manual for more information about the format of this element.

Reimplemented from OpenNN::TrainingAlgorithm.

Definition at line 1784 of file conjugate_gradient.cpp.

void OpenNN::ConjugateGradient::from_XML ( TiXmlElement *  conjugate_gradient_element  )  [virtual]

This method deserializes the conjugate gradient object from a XML element of the TinyXML library. See the OpenNN manual for more information about the format of this element.

Parameters:
conjugate_gradient_element Pointer to a TinyXML element containing the member data.

Reimplemented from OpenNN::TrainingAlgorithm.

Definition at line 2106 of file conjugate_gradient.cpp.


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

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