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
|
/*=========================================================================
*
* Copyright Insight Software Consortium
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
#include "itkOneHiddenLayerBackPropagationNeuralNetwork.h"
#include "itkIterativeSupervisedTrainingFunction.h"
#include "itkListSample.h"
#include <fstream>
#define ROUND(x) (floor(x+0.5))
int
NNetClassifierTest3(int argc, char* argv[])
{
if (argc < 3)
{
std::cout << "Usage: " << argv[0]
<< " InputTrainingFile(.txt) InputTestFile(.txt)" << std::endl;
return EXIT_FAILURE;
}
int num_train=800;
int num_test=200;
char* trainFileName =argv[1];
char* testFileName = argv[2];
const int num_input_nodes = 2;
const int num_hidden_nodes = 2;
const int num_output_nodes = 1;
typedef itk::Vector<double, num_input_nodes> MeasurementVectorType;
typedef itk::Vector<double, num_output_nodes> TargetVectorType;
typedef itk::Statistics::ListSample<MeasurementVectorType> SampleType;
typedef itk::Statistics::ListSample<TargetVectorType> TargetType;
typedef itk::Statistics::IterativeSupervisedTrainingFunction<SampleType, TargetType, double>
TrainingFcnType;
MeasurementVectorType mv;
TargetVectorType tv;
TargetVectorType ov;
ov.Fill(0.0);
SampleType::Pointer trainsample = SampleType::New();
SampleType::Pointer testsample = SampleType::New();
TargetType::Pointer traintargets = TargetType::New();
TargetType::Pointer testtargets = TargetType::New();
trainsample->SetMeasurementVectorSize( num_input_nodes);
traintargets->SetMeasurementVectorSize( num_output_nodes);
testsample->SetMeasurementVectorSize( num_input_nodes);
testtargets->SetMeasurementVectorSize( num_output_nodes);
std::ifstream infile1;
infile1.open(trainFileName, std::ios::in);
if (infile1.fail())
{
std::cout << argv[0] << " Cannot open training file for reading: "
<< trainFileName << std::endl;
return EXIT_FAILURE;
}
for (int a = 0; a < num_train; a++)
{
for (int i = 0; i < num_input_nodes; i++)
{
infile1 >> mv[i];
}
infile1 >> tv[0];
trainsample->PushBack(mv);
traintargets->PushBack(tv);
std::cout << "Input =" << mv << std::endl;
std::cout << "target =" << tv << std::endl;
}
infile1.close();
std::ifstream infile2;
infile2.open(testFileName, std::ios::in);
if (infile2.fail())
{
std::cout << argv[0] << " Cannot open test file for reading: "
<< testFileName << std::endl;
return EXIT_FAILURE;
}
for (int a = 0; a < num_test; a++)
{
for (int i = 0; i < num_input_nodes; i++)
{
infile2 >> mv[i];
}
infile2 >> tv[0];
testsample->PushBack(mv);
testtargets->PushBack(tv);
std::cout << "Input =" << mv << std::endl;
std::cout << "target =" << tv << std::endl;
}
infile2.close();
typedef itk::Statistics::OneHiddenLayerBackPropagationNeuralNetwork<MeasurementVectorType, TargetVectorType> OneHiddenLayerBackPropagationNeuralNetworkType;
OneHiddenLayerBackPropagationNeuralNetworkType::Pointer net1 = OneHiddenLayerBackPropagationNeuralNetworkType::New();
net1->SetNumOfInputNodes(num_input_nodes);
net1->SetNumOfFirstHiddenNodes(num_hidden_nodes);
net1->SetNumOfOutputNodes(num_output_nodes);
net1->Initialize();
net1->InitializeWeights();
net1->SetLearningRate(0.01);
TrainingFcnType::Pointer trainingfcn = TrainingFcnType::New();
trainingfcn->SetIterations(20000);
trainingfcn->SetThreshold(0.0001);
trainingfcn->Train(net1, trainsample, traintargets);
//Network Simulation
std::cout << testsample->Size() << std::endl;
std::cout << "Network Simulation" << std::endl;
SampleType::ConstIterator iter1 = testsample->Begin();
TargetType::ConstIterator iter2 = testtargets->Begin();
unsigned int error1 = 0;
unsigned int error2 = 0;
int flag;
std::ofstream outfile;
outfile.open("out1.txt",std::ios::out);
while (iter1 != testsample->End())
{
mv = iter1.GetMeasurementVector();
tv = iter2.GetMeasurementVector();
ov.SetVnlVector(net1->GenerateOutput(mv));
flag = 0;
if (std::fabs(tv[0]-ov[0])>0.2)
{
flag = 1;
}
if (flag == 1 && ROUND(tv[0]) == 1)
{
++error1;
}
else if (flag == 1 && ROUND(tv[0]) == -1)
{
++error2;
}
outfile<<mv[0]<<" "<<mv[1]<<" "<<tv[0]<<" "<<ov[0]<<std::endl;
std::cout << "Network Input = " << mv << std::endl;
std::cout << "Network Output = " << ov << std::endl;
std::cout << "Target = " << tv << std::endl;
++iter1;
++iter2;
}
std::cout << "Among "<<num_test<<" measurement vectors, " << error1 + error2
<< " vectors are misclassified." << std::endl;
std::cout<<"Network Weights and Biases after Training= "<<std::endl;
std::cout << net1 << std::endl;
if (double(error1 / 10) > 2 || double(error2 / 10) > 2)
{
std::cout << "Test failed." << std::endl;
return EXIT_FAILURE;
}
std::cout << "Test passed." << std::endl;
return EXIT_SUCCESS;
}
|
