1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
|
/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile: itkIterativeSupervisedTrainingFunction.txx,v $
Language: C++
Date: $Date: 2007-08-23 20:02:20 $
Version: $Revision: 1.4 $
Copyright (c) Insight Software Consortium. All rights reserved.
See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#ifndef __itkIterativeSupervisedTrainingFunction_txx
#define __itkIterativeSupervisedTrainingFunction_txx
#include "itkIterativeSupervisedTrainingFunction.h"
#include <fstream>
namespace itk
{
namespace Statistics
{
template<class TSample, class TTargetVector, class ScalarType>
IterativeSupervisedTrainingFunction<TSample,TTargetVector,ScalarType>
::IterativeSupervisedTrainingFunction()
{
this->m_LearningRate = 0.5;
m_Threshold = 0;
m_Stop = false;
}
template<class TSample, class TTargetVector, class ScalarType>
void IterativeSupervisedTrainingFunction<TSample,TTargetVector,ScalarType>
::SetNumOfIterations(long i)
{
this->SetIterations(i);
this->Modified();
}
template<class TSample, class TTargetVector, class ScalarType>
void IterativeSupervisedTrainingFunction<TSample,TTargetVector,ScalarType>
::Train(typename IterativeSupervisedTrainingFunction<TSample, TTargetVector, ScalarType>::NetworkType* Net,
TSample* samples, TTargetVector* targets)
{
this->SetTrainingSamples(samples);
this->SetTargetValues(targets);
InternalVectorType outputvector;
InternalVectorType errorvector;
outputvector.SetSize(targets->GetMeasurementVectorSize());
errorvector.SetSize(targets->GetMeasurementVectorSize());
//typename Superclass::OutputVectorType outputvector;
typename Superclass::VectorType inputvector;
typename Superclass::OutputVectorType targetvector;
//typename Superclass::OutputVectorType errorvector;
#ifdef __OLD_CODE__
std::ofstream outfile;
outfile.open("output.txt");
#endif
const long num_iterations = this->GetIterations();
m_Stop = false;
long i = 0;
while (!m_Stop)
{
int temp = rand() % (this->m_InputSamples.size());
inputvector = this->m_InputSamples[temp];
targetvector = this->m_Targets[temp];
outputvector = Net->GenerateOutput(inputvector);
for(unsigned int k=0; k<targetvector.Size(); k++)
{
errorvector[k] = targetvector[k] - outputvector[k];
}
#ifdef __OLD_CODE__
outfile <<errorvector[0] << std::endl;
#endif
Net->BackwardPropagate(this->m_PerformanceFunction->EvaluateDerivative(errorvector));
Net->UpdateWeights(this->m_LearningRate);
i++;
if (i > num_iterations)
{
m_Stop = true;
}
}
#ifdef __OLD_CODE__
if (this->m_PerformanceFunction->Evaluate(errorvector) < m_Threshold
&& i < num_iterations)
{
std::cout << "Goal Met " << std::endl;
}
else
{
std::cout << "Goal Not Met Max Iterations Reached " << std::endl;
}
#endif
}
/** Print the object */
template<class TSample, class TTargetVector, class ScalarType>
void
IterativeSupervisedTrainingFunction<TSample,TTargetVector,ScalarType>
::PrintSelf( std::ostream& os, Indent indent ) const
{
os << indent << "IterativeSupervisedTrainingFunction(" << this << ")" << std::endl;
os << indent << "m_Threshold = " << m_Threshold << std::endl;
os << indent << "m_Stop = " << m_Stop << std::endl;
Superclass::PrintSelf( os, indent );
}
} // end namespace Statistics
} // end namespace itk
#endif
|
