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/*=========================================================================
*
* 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
XORTest2(int argc, char* argv[])
{
if (argc < 2)
{
std::cout << "Usage: " << argv[0]
<< " InputTrainingFile(.txt)" << std::endl;
return EXIT_FAILURE;
}
char* dataFileName = argv[1];
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;
SampleType::Pointer sample = SampleType::New();
TargetType::Pointer targets = TargetType::New();
sample->SetMeasurementVectorSize( num_input_nodes);
targets->SetMeasurementVectorSize( num_output_nodes);
std::ifstream infile1;
infile1.open(dataFileName, std::ios::in);
if (infile1.fail())
{
std::cout << argv[0] << " Cannot open file for reading: "
<< dataFileName << std::endl;
return EXIT_FAILURE;
}
infile1 >> mv[0] >> mv[1] >> tv[0];
while (!infile1.eof())
{
std::cout << "Input =" << mv << std::endl;
std::cout << "target =" << tv << std::endl;
sample->PushBack(mv);
targets->PushBack(tv);
infile1 >> mv[0] >> mv[1] >> tv[0];
}
infile1.close();
std::cout << sample->Size() << std::endl;
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(0.001);
trainingfcn->Train(net1, sample, targets);
//Network Simulation
std::cout << sample->Size() << std::endl;
std::cout << "Network Simulation" << std::endl;
TargetVectorType ov;
ov.Fill(0.0);
SampleType::ConstIterator iter1 = sample->Begin();
TargetType::ConstIterator iter2 = targets->Begin();
unsigned int error1 = 0;
unsigned int error2 = 0;
int flag;
std::ofstream outfile;
outfile.open("out1.txt",std::ios::out);
if (outfile.fail())
{
std::cout << argv[0] << " Cannot open file for writing: "
<< "out1.txt" << std::endl;
return EXIT_FAILURE;
}
while (iter1 != sample->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 4 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 ((error1 + error2) > 2)
{
std::cout << "Test failed." << std::endl;
return EXIT_FAILURE;
}
std::cout << "Test passed." << std::endl;
return EXIT_SUCCESS;
}
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