87 lines
2.3 KiB
C++
87 lines
2.3 KiB
C++
#include "netlearner.h"
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#include "../../Net.h"
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#include "mnistloader.h"
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#include <QElapsedTimer>
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#include <QImage>
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void NetLearner::run()
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{
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try
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{
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QElapsedTimer timer;
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emit logMessage("Loading training data...");
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MnistLoader mnistLoader;
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mnistLoader.load("../NeuroUI/MNIST Database/train-images.idx3-ubyte",
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"../NeuroUI/MNIST Database/train-labels.idx1-ubyte");
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emit logMessage("done");
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Net digitClassifier({28*28, 256, 1});
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timer.start();
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size_t numIterations = 100000;
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for (size_t iteration = 0; iteration < numIterations && cancel == false; ++iteration)
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{
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auto trainingSample = mnistLoader.getSample(0);
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emit logMessage(QString("training sample ") + QString::number(trainingSample.label));
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emit sampleImageLoaded(trainingSample.toQImage());
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std::vector<double> targetValues =
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{
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trainingSample.label / 10.0
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};
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std::vector<double> trainingData;
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trainingData.reserve(28*28);
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for (const uint8_t &val : trainingSample.data)
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{
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trainingData.push_back(val / 255.0);
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}
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digitClassifier.feedForward(trainingData);
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std::vector<double> outputValues = digitClassifier.getOutput();
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double error = outputValues[0] - targetValues[0];
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QString logString;
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logString.append("Error: ");
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logString.append(QString::number(std::abs(error)));
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emit logMessage(logString);
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emit currentNetError(error);
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emit progress((double)iteration / (double)numIterations);
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digitClassifier.backProp(targetValues);
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}
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QString timerLogString;
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timerLogString.append("Elapsed time: ");
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timerLogString.append(QString::number(timer.elapsed() / 1000.0));
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timerLogString.append(" seconds");
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emit logMessage(timerLogString);
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digitClassifier.save("DigitClassifier.nnet");
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}
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catch (std::exception &ex)
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{
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QString logString("Error: ");
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logString.append(ex.what());
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emit logMessage(logString);
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}
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cancel = false;
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}
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void NetLearner::cancelLearning()
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{
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cancel = true;
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}
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