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
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
|
/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkQuickPropLearningRule.txx
Language: C++
Date: $Date$
Version: $Revision$
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 __itkQuickPropLearningRule_txx
#define __itkQuickPropLearningRule_txx
#include "itkQuickPropLearningRule.h"
namespace itk
{
namespace Statistics
{
template<class LayerType, class TTargetVector>
QuickPropLearningRule <LayerType,TTargetVector>
::QuickPropLearningRule()
{
m_Momentum = 0.9; //Default
m_Max_Growth_Factor = 1.75;
m_Decay = -0.0001;
m_SplitEpsilon = 1;
m_Epsilon = 0.55;
m_Threshold = 0.0;
m_SigmoidPrimeOffset = 0;
m_SplitEpsilon = 0;
}
template<class LayerType, class TTargetVector>
void
QuickPropLearningRule<LayerType,TTargetVector>
::Learn(LayerType* layer, ValueType itkNotUsed(lr))
{
typename LayerType::WeightSetType::Pointer inputweightset;
inputweightset = layer->GetInputWeightSet();
//For Quickprop
typename LayerType::ValuePointer DWvalues_m_1 = inputweightset->GetPrevDWValues();
typename LayerType::ValuePointer Delvalues_m_1 = inputweightset->GetPrevDeltaValues();
typename LayerType::ValuePointer Delvalues = inputweightset->GetTotalDeltaValues();
typename LayerType::ValuePointer weightvalues = inputweightset->GetWeightValues();
unsigned int input_cols = inputweightset->GetNumberOfInputNodes();
unsigned int input_rows = inputweightset->GetNumberOfOutputNodes();
vnl_matrix<ValueType> DW_m_1(input_rows, input_cols);
DW_m_1.fill(0);
vnl_matrix<ValueType> Del_m_1(input_rows, input_cols);
Del_m_1.fill(0);
DW_m_1.copy_in(DWvalues_m_1);
Del_m_1.copy_in(Delvalues_m_1);
vnl_matrix<ValueType> DW_temp(inputweightset->GetNumberOfOutputNodes(),
inputweightset->GetNumberOfInputNodes());
vnl_matrix<ValueType> weights(inputweightset->GetNumberOfOutputNodes(),
inputweightset->GetNumberOfInputNodes());
DW_temp.copy_in(Delvalues);
weights.copy_in(weightvalues);
vnl_matrix<ValueType> temp(inputweightset->GetNumberOfOutputNodes(),
inputweightset->GetNumberOfInputNodes());
temp.fill(0);
//get bias
vnl_vector<ValueType> delb;
delb.set_size(inputweightset->GetNumberOfOutputNodes());
delb.fill(0);
vnl_vector<ValueType> delb_m_1;
delb_m_1.set_size(inputweightset->GetNumberOfOutputNodes());
delb_m_1.fill(0);
vnl_vector<ValueType> DB_m_1;
DB_m_1.set_size(inputweightset->GetNumberOfOutputNodes());
DB_m_1.fill(0);
vnl_vector<ValueType> DB;
DB.set_size(inputweightset->GetNumberOfOutputNodes());
DB.fill(0);
typename LayerType::ValuePointer deltaBValues = inputweightset->GetTotalDeltaBValues();
delb.copy_in(deltaBValues);
typename LayerType::ValuePointer prevDeltaBValues = inputweightset->GetPrevDeltaBValues();
delb_m_1.copy_in(prevDeltaBValues);
typename LayerType::ValuePointer prevDBValues = inputweightset->GetPrevDBValues();
DB_m_1.copy_in(prevDBValues);
DW_temp.set_column(input_cols-1,delb);
Del_m_1.set_column(input_cols-1,delb_m_1);
DW_m_1.set_column(input_cols-1,DB_m_1);
ValueType step_val;
float shrink_factor =(float)m_Max_Growth_Factor/(1.0+ m_Max_Growth_Factor);
for(unsigned int i=0; i<input_rows; i++)
{
for(unsigned int j=0; j<input_cols; j++)
{
step_val=0;
DW_temp(i,j) += m_Decay*weights(i,j);
if(DW_m_1(i,j)>m_Threshold)
{
if(DW_temp(i,j)>0.0)
{
step_val += (m_Epsilon *DW_temp(i,j));
}
if(DW_temp(i,j) >(shrink_factor*Del_m_1(i,j)))
{
step_val += (m_Max_Growth_Factor*DW_m_1(i,j));
}
else
{
step_val += ((DW_temp(i,j)/(Del_m_1(i,j)-DW_temp(i,j)))*DW_m_1(i,j));
}
}
else if(DW_m_1(i,j)< -m_Threshold)
{
if(DW_temp(i,j)<0.0)
{
step_val += (m_Epsilon *DW_temp(i,j));
}
if(DW_temp(i,j) <(shrink_factor *Del_m_1(i,j)))
{
step_val += (m_Max_Growth_Factor *DW_m_1(i,j));
}
else
{
step_val += ((DW_temp(i,j)/(Del_m_1(i,j)-DW_temp(i,j)))*DW_m_1(i,j));
}
}
else
{
step_val += (m_Epsilon*DW_temp(i,j))+(m_Momentum *DW_m_1(i,j));
}
temp(i,j)=step_val;
}// inner for
}//outer for
DB=temp.get_column(input_cols-1);
inputweightset->SetDBValues(DB.data_block());
inputweightset->SetDWValues(temp.data_block());
}
template<class LayerType, class TTargetVector>
void
QuickPropLearningRule<LayerType,TTargetVector>
::Learn(LayerType* itkNotUsed(layer), TTargetVector itkNotUsed(errors),ValueType itkNotUsed(lr))
{
}
/** Print the object */
template<class LayerType, class TTargetVector>
void
QuickPropLearningRule<LayerType,TTargetVector>
::PrintSelf( std::ostream& os, Indent indent ) const
{
os << indent << "QuickPropLearningRule(" << this << ")" << std::endl;
os << indent << "m_Momentum = " << m_Momentum << std::endl;
os << indent << "m_Max_Growth_Factor = " << m_Max_Growth_Factor << std::endl;
os << indent << "m_Decay = " << m_Decay << std::endl;
os << indent << "m_Threshold = " << m_Threshold << std::endl;
os << indent << "m_Epsilon = " << m_Epsilon << std::endl;
os << indent << "m_SigmoidPrimeOffset = " << m_SigmoidPrimeOffset << std::endl;
os << indent << "m_SplitEpsilon = " << m_SplitEpsilon << std::endl;
Superclass::PrintSelf( os, indent );
}
} // end namespace Statistics
} // end namespace itk
#endif
|
