conjugate_gradient.h

//   OpenNN: Open Neural Networks Library
//   www.opennn.net
//
//   C O N J U G A T E   G R A D I E N T   C L A S S   H E A D E R
//
//   Artificial Intelligence Techniques SL
//   artelnics@artelnics.com
 
#ifndef CONJUGATEGRADIENT_H
#define CONJUGATEGRADIENT_H
 
// System inlcludes
 
#include <string>
#include <sstream>
#include <iostream>
#include <fstream>
#include <algorithm>
#include <functional>
#include <climits>
#include <cmath>
#include <ctime>
 
// OpenNN includes
 
#include "config.h"
#include "loss_index.h"
#include "optimization_algorithm.h"
#include "learning_rate_algorithm.h"
 
namespace OpenNN
{
 
struct ConjugateGradientData;
 
 
 
class ConjugateGradient : public OptimizationAlgorithm
{
 
public:
 
   // Enumerations
 
 
   enum class TrainingDirectionMethod{PR, FR};
 
   // DEFAULT CONSTRUCTOR
 
   explicit ConjugateGradient(); 
 
   explicit ConjugateGradient(LossIndex*);   
 
   virtual ~ConjugateGradient();
 
   // Get methods
 
   const LearningRateAlgorithm& get_learning_rate_algorithm() const;
   LearningRateAlgorithm* get_learning_rate_algorithm_pointer();
 
   // Training operators
 
   const TrainingDirectionMethod& get_training_direction_method() const;
   string write_training_direction_method() const;
 
   // Stopping criteria
 
   
 
   const type& get_minimum_loss_decrease() const;
   const type& get_loss_goal() const;
   const Index& get_maximum_selection_failures() const;
 
 
   const Index& get_maximum_epochs_number() const;
   const type& get_maximum_time() const;
 
   // Set methods
 
   void set_default();
 
   void set_loss_index_pointer(LossIndex*);
 
   // Training operators
 
   void set_training_direction_method(const TrainingDirectionMethod&);
   void set_training_direction_method(const string&);
 
   // Stopping criteria
 
   
 
   void set_loss_goal(const type&);
   void set_minimum_loss_decrease(const type&);
   void set_maximum_selection_failures(const Index&);
 
 
   void set_maximum_epochs_number(const Index&);
   void set_maximum_time(const type&);
 
   // Utilities
 
   void set_save_period(const Index&);
 
   // Training direction methods
 
   type calculate_PR_parameter(const Tensor<type, 1>&, const Tensor<type, 1>&) const;
   type calculate_FR_parameter(const Tensor<type, 1>&, const Tensor<type, 1>&) const;
 
   void calculate_PR_training_direction(const Tensor<type, 1>&, const Tensor<type, 1>&, const Tensor<type, 1>&, Tensor<type, 1>&) const;
   void calculate_FR_training_direction(const Tensor<type, 1>&, const Tensor<type, 1>&, const Tensor<type, 1>&, Tensor<type, 1>&) const;
 
   void calculate_gradient_descent_training_direction(const Tensor<type, 1>&, Tensor<type, 1>&) const;
 
   void calculate_conjugate_gradient_training_direction(const Tensor<type, 1>&,
                                                        const Tensor<type, 1>&,
                                                        const Tensor<type, 1>&,
                                                        Tensor<type, 1>&) const;
 
   // Training methods
 
   TrainingResults perform_training();   
 
   string write_optimization_algorithm_type() const;
 
   // Serialization methods
 
   Tensor<string, 2> to_string_matrix() const;
 
   void from_XML(const tinyxml2::XMLDocument&);
 
   void write_XML(tinyxml2::XMLPrinter&) const;
 
   void update_parameters(
           const DataSetBatch& batch,
           NeuralNetworkForwardPropagation& forward_propagation,
           LossIndexBackPropagation& back_propagation,
           ConjugateGradientData& optimization_data);
 
private:
 
   type first_learning_rate = static_cast<type>(0.01);
 
 
   TrainingDirectionMethod training_direction_method = ConjugateGradient::TrainingDirectionMethod::FR;
 
 
   LearningRateAlgorithm learning_rate_algorithm;
 
   // Stopping criteria
 
 
   type minimum_loss_decrease = type(0);
 
 
   type training_loss_goal = type(0);
 
 
   Index maximum_selection_failures;
 
 
   Index maximum_epochs_number;
 
 
   type maximum_time;
};
 
 
struct ConjugateGradientData : public OptimizationAlgorithmData
{
 
    explicit ConjugateGradientData();
 
    explicit ConjugateGradientData(ConjugateGradient*);
 
    virtual ~ConjugateGradientData();
 
    void set(ConjugateGradient*);
 
    void print() const;
 
    ConjugateGradient* conjugate_gradient_pointer = nullptr;
 
    Tensor<type, 1> parameters_increment;
 
    Tensor<type, 1> old_gradient;
 
    Tensor<type, 1> old_training_direction;
 
    Index epoch = 0;
 
    type learning_rate = type(0);
    type old_learning_rate = type(0);
 
    Tensor<type, 0> training_slope;
};
 
}
 
#endif
 
 
// OpenNN: Open Neural Networks Library.
// Copyright(C) 2005-2021 Artificial Intelligence Techniques, SL.
//
// This library is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 2.1 of the License, or any later version.
//
// This library is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
// Lesser General Public License for more details.
 
// You should have received a copy of the GNU Lesser General Public
// License along with this library; if not, write to the Free Software
// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA