TrainingError.h
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1 /*!
2  *
3  *
4  * \brief Stopping Criterion which stops, when the trainign error seems to converge
5  *
6  *
7  *
8  * \author O. Krause
9  * \date 2010
10  *
11  *
12  * \par Copyright 1995-2015 Shark Development Team
13  *
14  * <BR><HR>
15  * This file is part of Shark.
16  * <http://image.diku.dk/shark/>
17  *
18  * Shark is free software: you can redistribute it and/or modify
19  * it under the terms of the GNU Lesser General Public License as published
20  * by the Free Software Foundation, either version 3 of the License, or
21  * (at your option) any later version.
22  *
23  * Shark is distributed in the hope that it will be useful,
24  * but WITHOUT ANY WARRANTY; without even the implied warranty of
25  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
26  * GNU Lesser General Public License for more details.
27  *
28  * You should have received a copy of the GNU Lesser General Public License
29  * along with Shark. If not, see <http://www.gnu.org/licenses/>.
30  *
31  */
32 
33 #ifndef SHARK_TRAINERS_STOPPINGCRITERA_TRAININGERROR_H
34 #define SHARK_TRAINERS_STOPPINGCRITERA_TRAININGERROR_H
35 
37 #include <shark/Core/ResultSets.h>
38 #include <queue>
39 #include <numeric>
40 namespace shark{
41 
42 /// \brief This stopping criterion tracks the improvement of the error function of the training error over an interval of iterations.
43 ///
44 /// If at one point, the difference between the error values of the beginning and the end of the interval are smaller
45 /// than a certain value, this stopping criterion assumes convergence and stops.
46 /// Of course, this may be misleading, when the algorithm temporarily gets stuck at a saddle point of the error surface.
47 /// The functions assumes that the algorithm is minimizing. For details, see:
48 ///
49 /// Lutz Prechelt. Early Stopping - but when? In Genevieve B. Orr and
50 /// Klaus-Robert Müller: Neural Networks: Tricks of the Trade, volume
51 /// 1524 of LNCS, Springer, 1997.
52 ///
53 template<class PointType = RealVector>
54 class TrainingError: public AbstractStoppingCriterion< SingleObjectiveResultSet<PointType> >{
55 public:
56  /// constructs the TrainingError generalization loss
57  /// @param intervalSize size of the interval over which the progress is monitored
58  /// @param minDifference minimum difference between start and end of the interval allowed before training stops
59  TrainingError(size_t intervalSize, double minDifference){
60  m_minDifference = minDifference;
61  m_intervalSize = intervalSize;
62  reset();
63  }
64  /// returns true if training should stop
66 
67  m_interval.pop();
68  m_interval.push(set.value);
69  return (m_interval.front()-set.value) >= 0
70  && (m_interval.front()-set.value) < m_minDifference;
71  }
72  /// resets the internal state
73  void reset(){
74  m_interval = std::queue<double>();
75  for(size_t i = 0; i != m_intervalSize;++i) {
77  }
78  }
79 protected:
80  /// monitored training interval
81  std::queue<double> m_interval;
82  /// minmum difference allowed
84  /// size of the interval
86 };
87 }
88 
89 
90 #endif