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Load and train handwritten digits

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This commit is contained in:
Michael Mandl 2015-10-27 15:33:54 +01:00
parent 650b4be9fc
commit f9be5ca717
3 changed files with 88 additions and 56 deletions
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82
gui/NeuroUI/netlearner.cpp
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@ -10,70 +10,64 @@ void NetLearner::run()
{
QElapsedTimer timer;
emit logMessage("Loading training data...");
emit progress(0.0);
TrainingDataLoader dataLoader;
dataLoader.addSamples("../NeuroUI/training data/mnist_train0.jpg", 0);
emit progress(0.1);
dataLoader.addSamples("../NeuroUI/training data/mnist_train1.jpg", 1);
emit progress(0.2);
dataLoader.addSamples("../NeuroUI/training data/mnist_train2.jpg", 2);
emit progress(0.3);
dataLoader.addSamples("../NeuroUI/training data/mnist_train3.jpg", 3);
emit progress(0.4);
dataLoader.addSamples("../NeuroUI/training data/mnist_train4.jpg", 4);
emit progress(0.5);
dataLoader.addSamples("../NeuroUI/training data/mnist_train5.jpg", 5);
emit progress(0.6);
dataLoader.addSamples("../NeuroUI/training data/mnist_train6.jpg", 6);
emit progress(0.7);
dataLoader.addSamples("../NeuroUI/training data/mnist_train7.jpg", 7);
emit progress(0.8);
dataLoader.addSamples("../NeuroUI/training data/mnist_train8.jpg", 8);
emit progress(0.9);
dataLoader.addSamples("../NeuroUI/training data/mnist_train9.jpg", 9);
emit progress(1.0);
Net myNet;
try
{
myNet.load("mynet.nnet");
}
catch (...)
{
myNet.initialize({2, 3, 1});
}
emit logMessage("done");
emit progress(0.0);
size_t batchSize = 5000;
size_t batchIndex = 0;
double batchMaxError = 0.0;
double batchMeanError = 0.0;
Net digitClassifier({32*32, 16*16, 32, 1});
timer.start();
size_t numIterations = 2000000;
size_t numIterations = 10000;
for (size_t iteration = 0; iteration < numIterations; ++iteration)
{
std::vector<double> inputValues =
{
std::rand() / (double)RAND_MAX,
std::rand() / (double)RAND_MAX
};
const TrainingDataLoader::Sample &trainingSample = dataLoader.getRandomSample();
std::vector<double> targetValues =
{
(inputValues[0] + inputValues[1]) / 2.0
trainingSample.first / 10.0
};
myNet.feedForward(inputValues);
digitClassifier.feedForward(trainingSample.second);
std::vector<double> outputValues = myNet.getOutput();
std::vector<double> outputValues = digitClassifier.getOutput();
double error = outputValues[0] - targetValues[0];
batchMeanError += error;
batchMaxError = std::max<double>(batchMaxError, error);
QString logString;
if (batchIndex++ == batchSize)
{
QString logString;
logString.append("Error: ");
logString.append(QString::number(std::abs(error)));
logString.append("Batch error (");
logString.append(QString::number(batchSize));
logString.append(" iterations, max/mean): ");
logString.append(QString::number(std::abs(batchMaxError)));
logString.append(" / ");
logString.append(QString::number(std::abs(batchMeanError / batchSize)));
emit logMessage(logString);
emit currentNetError(error);
emit progress((double)iteration / (double)numIterations);
emit logMessage(logString);
emit currentNetError(batchMaxError);
emit progress((double)iteration / (double)numIterations);
batchIndex = 0;
batchMaxError = 0.0;
batchMeanError = 0.0;
}
myNet.backProp(targetValues);
digitClassifier.backProp(targetValues);
}
QString timerLogString;
@ -83,7 +77,7 @@ void NetLearner::run()
emit logMessage(timerLogString);
myNet.save("mynet.nnet");
digitClassifier.save("DigitClassifier.nnet");
}
catch (std::exception &ex)
{