normalized_squared_error.h

//   OpenNN: Open Neural Networks Library
//   www.opennn.net
//
//   N O R M A L I Z E D   S Q U A R E D   E R R O R   C L A S S   H E A D E R
//
//   Artificial Intelligence Techniques SL
//   artelnics@artelnics.com
 
#ifndef NORMALIZEDSQUAREDERROR_H
#define NORMALIZEDSQUAREDERROR_H
 
// System includes
 
#include <cmath>
#include <iostream>
#include <fstream>
#include <limits>
#include <string>
#include <sstream>
 
// OpenNN includes
 
#include "config.h"
#include "loss_index.h"
#include "data_set.h"
 
namespace OpenNN
{
 
 
 
class NormalizedSquaredError : public LossIndex
{
 
public:
 
    // Constructors
 
   explicit NormalizedSquaredError(NeuralNetwork*, DataSet*);
 
   explicit NormalizedSquaredError();   
 
    // Destructor
 
   virtual ~NormalizedSquaredError();
 
   // Get methods
 
    type get_normalization_coefficient() const;
    type get_selection_normalization_coefficient() const;
 
   // Set methods
 
    void set_normalization_coefficient();
    void set_normalization_coefficient(const type&);
 
    void set_time_series_normalization_coefficient();
 
    void set_selection_normalization_coefficient();
    void set_selection_normalization_coefficient(const type&);
 
    void set_default();
 
    void set_data_set_pointer(DataSet* new_data_set_pointer);
 
   // Normalization coefficients 
 
   type calculate_normalization_coefficient(const Tensor<type, 2>&, const Tensor<type, 1>&) const;
 
   type calculate_time_series_normalization_coefficient(const Tensor<type, 2>&, const Tensor<type, 2>&) const;
 
   // Back propagation
     
   void calculate_error(const DataSetBatch&,
                        const NeuralNetworkForwardPropagation&,
                        LossIndexBackPropagation&) const;
 
   void calculate_output_delta(const DataSetBatch&,
                               NeuralNetworkForwardPropagation&,
                               LossIndexBackPropagation&) const;
 
    // Back propagation LM
 
   void calculate_error_lm(const DataSetBatch&,
                           const NeuralNetworkForwardPropagation&,
                           LossIndexBackPropagationLM&) const;
 
   void calculate_output_delta_lm(const DataSetBatch&,
                               NeuralNetworkForwardPropagation&,
                               LossIndexBackPropagationLM&) const;
 
   void calculate_error_gradient_lm(const DataSetBatch&,
                              LossIndexBackPropagationLM&) const;
 
   void calculate_error_hessian_lm(const DataSetBatch&,
                                        LossIndexBackPropagationLM&) const;
 
   // Serialization methods
 
   string get_error_type() const;
   string get_error_type_text() const;
 
   void from_XML(const tinyxml2::XMLDocument&);
 
   void write_XML(tinyxml2::XMLPrinter&) const;
 
private:
 
 
   type normalization_coefficient = type(NAN);
 
   type selection_normalization_coefficient = type(NAN);
 
#ifdef OPENNN_CUDA
    #include "../../opennn-cuda/opennn-cuda/normalized_squared_error_cuda.h"
#endif
 
};
 
}
 
#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