70 lines
1.9 KiB
C++
70 lines
1.9 KiB
C++
#include "netlearner.h"
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#include "../../Net.h"
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void NetLearner::run()
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{
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try
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{
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Net myNet({2, 3, 1});
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size_t batchSize = 5000;
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size_t batchIndex = 0;
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double batchMaxError = 0.0;
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double batchMeanError = 0.0;
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size_t numIterations = 100000;
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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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{
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std::rand() / (double)RAND_MAX,
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std::rand() / (double)RAND_MAX
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};
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std::vector<double> targetValues =
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{
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(inputValues[0] + inputValues[1]) / 2.0
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};
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myNet.feedForward(inputValues);
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std::vector<double> outputValues = myNet.getOutput();
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double error = outputValues[0] - targetValues[0];
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batchMeanError += error;
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batchMaxError = std::max<double>(batchMaxError, error);
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if (batchIndex++ == batchSize)
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{
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QString logString;
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logString.append("Batch error (");
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logString.append(QString::number(batchSize));
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logString.append(" iterations, max/mean): ");
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logString.append(QString::number(std::abs(batchMaxError)));
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logString.append(" / ");
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logString.append(QString::number(std::abs(batchMeanError / batchSize)));
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emit logMessage(logString);
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batchIndex = 0;
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batchMaxError = 0.0;
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batchMeanError = 0.0;
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}
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myNet.backProp(targetValues);
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emit progress((double)iteration / (double)numIterations);
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}
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myNet.save("mynet.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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}
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