Started loader-class for 8x8 pixel digit training data
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@ -54,7 +54,7 @@ void Layer::connectTo(const Layer & nextLayer)
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void Layer::updateInputWeights(Layer & prevLayer)
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{
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static const double trainingRate = 0.3;
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static const double trainingRate = 0.2;
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for (size_t targetLayerIndex = 0; targetLayerIndex < sizeWithoutBiasNeuron(); ++targetLayerIndex)
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{
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@ -18,14 +18,16 @@ SOURCES += main.cpp\
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../../Net.cpp \
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../../Neuron.cpp \
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netlearner.cpp \
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errorplotter.cpp
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errorplotter.cpp \
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trainingdataloader.cpp
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HEADERS += neuroui.h \
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../../Layer.h \
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../../Net.h \
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../../Neuron.h \
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netlearner.h \
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errorplotter.h
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errorplotter.h \
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trainingdataloader.h
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FORMS += neuroui.ui
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@ -1,5 +1,6 @@
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#include "netlearner.h"
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#include "../../Net.h"
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#include "trainingdataloader.h"
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#include <QElapsedTimer>
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@ -9,6 +10,9 @@ void NetLearner::run()
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{
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QElapsedTimer timer;
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TrainingDataLoader dataLoader;
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dataLoader.addSamples("../NeuroUI/training data/mnist_train0.jpg", 0);
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Net myNet;
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try
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{
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@ -26,7 +30,7 @@ void NetLearner::run()
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timer.start();
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size_t numIterations = 1000000;
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size_t numIterations = 2000000;
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for (size_t iteration = 0; iteration < numIterations; ++iteration)
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{
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std::vector<double> inputValues =
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29
gui/NeuroUI/trainingdataloader.cpp
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29
gui/NeuroUI/trainingdataloader.cpp
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@ -0,0 +1,29 @@
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#include "trainingdataloader.h"
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#include <QImage>
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#include <QColor>
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TrainingDataLoader::TrainingDataLoader()
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{
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}
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void TrainingDataLoader::addSamples(const QString &sourceFile, TrainingDataLoader::SampleId sampleId)
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{
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QImage sourceImage;
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sourceImage.load(sourceFile);
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Sample sample;
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sample.first = sampleId;
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for (unsigned int y = 0; y < 8; ++y)
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{
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for (unsigned int x = 0; x < 8; ++x)
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{
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sample.second[x + y * 8] = qGray(sourceImage.pixel(x, y)) / 255.0;
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}
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}
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m_samples.push_back(sample);
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}
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26
gui/NeuroUI/trainingdataloader.h
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26
gui/NeuroUI/trainingdataloader.h
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@ -0,0 +1,26 @@
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#ifndef TRAININGDATALOADER_H
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#define TRAININGDATALOADER_H
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#include <utility>
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#include <list>
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#include <string>
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#include <QString>
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class TrainingDataLoader
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{
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public:
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using SampleData = double[64];
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using SampleId = unsigned int;
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using Sample = std::pair<SampleId, SampleData>;
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private:
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std::list<Sample> m_samples;
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public:
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TrainingDataLoader();
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void addSamples(const QString &sourceFile, SampleId sampleId);
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};
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#endif // TRAININGDATALOADER_H
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