Neuro/gui/NeuroUI/netlearner.cpp

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#include "netlearner.h"
#include "../../Net.h"
void NetLearner::run()
{
try
{
Net myNet({2, 3, 1});
size_t batchSize = 5000;
size_t batchIndex = 0;
double batchMaxError = 0.0;
double batchMeanError = 0.0;
size_t numIterations = 1000000;
for (size_t iteration = 0; iteration < numIterations; ++iteration)
{
std::vector<double> inputValues =
{
std::rand() / (double)RAND_MAX,
std::rand() / (double)RAND_MAX
};
std::vector<double> targetValues =
{
(inputValues[0] + inputValues[1]) / 2.0
};
myNet.feedForward(inputValues);
std::vector<double> outputValues = myNet.getOutput();
double error = outputValues[0] - targetValues[0];
batchMeanError += error;
batchMaxError = std::max<double>(batchMaxError, error);
if (batchIndex++ == batchSize)
{
QString logString;
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);
batchIndex = 0;
batchMaxError = 0.0;
batchMeanError = 0.0;
}
myNet.backProp(targetValues);
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emit progress((double)iteration / (double)numIterations);
}
}
catch (std::exception &ex)
{
QString logString("Error: ");
logString.append(ex.what());
emit logMessage(logString);
}
}