weighted_squared_error.h

//   OpenNN: Open Neural Networks Library
//   www.opennn.net
//
//   W E I G H T 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 WEIGHTEDSQUAREDERROR_H
#define WEIGHTEDSQUAREDERROR_H
 
// System includes
 
#include <string>
#include <sstream>
#include <iostream>
#include <fstream>
#include <limits>
#include <math.h>
 
// OpenNN includes
 
#include "config.h"
#include "loss_index.h"
#include "data_set.h"
 
namespace OpenNN
{
 
 
 
class WeightedSquaredError : public LossIndex
{
 
public:
 
   // Constructors
 
   explicit WeightedSquaredError();
 
   explicit WeightedSquaredError(NeuralNetwork*, DataSet*); 
 
   // Destructor
 
   virtual ~WeightedSquaredError(); 
 
   // Get methods
 
   type get_positives_weight() const;
   type get_negatives_weight() const;
 
   type get_normalizaton_coefficient() const;
 
   // Set methods
 
   void set_default();
 
   void set_positives_weight(const type&);
   void set_negatives_weight(const type&);
 
   void set_weights(const type&, const type&);
 
   void set_weights();
 
   void set_normalization_coefficient();
 
   void set_data_set_pointer(DataSet*);
 
   type weighted_sum_squared_error(const Tensor<type, 2>& x, const Tensor<type, 2>& y) const;
 
   string get_error_type() const;
 
   string get_error_type_text() 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_squared_errors_lm(const DataSetBatch&,
                                    const NeuralNetworkForwardPropagation&,
                                    LossIndexBackPropagationLM&) const;
 
   void calculate_error_lm(const DataSetBatch&,
                           const NeuralNetworkForwardPropagation&,
                           LossIndexBackPropagationLM&) const;
 
   void calculate_error_gradient_lm(const DataSetBatch&,
                              LossIndexBackPropagationLM&) const;
 
   void calculate_error_hessian_lm(const DataSetBatch&,
                                           LossIndexBackPropagationLM&) const;
 
   // Serialization methods
 
   void from_XML(const tinyxml2::XMLDocument&);
 
   void write_XML(tinyxml2::XMLPrinter&) const;   
 
private:
 
 
   type positives_weight = type(NAN);
 
 
   type negatives_weight = type(NAN);
 
 
   type normalization_coefficient;
 
#ifdef OPENNN_CUDA
    #include "../../opennn-cuda/opennn-cuda/weighted_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