74 lines
1.4 KiB
C++
74 lines
1.4 KiB
C++
#include <iostream>
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#include <exception>
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#include <algorithm>
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#include <assert.h>
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#include "Net.h"
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const double pi = std::acos(-1);
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int main()
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{
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try
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{
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std::cout << "Neuro running" << std::endl;
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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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std::cout << "Batch error (" << batchSize << " iterations, max/mean): ";
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std::cout << std::abs(batchMaxError);
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std::cout << " / ";
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std::cout << std::abs(batchMeanError / batchSize);
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std::cout << std::endl;
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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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}
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myNet.save("mynet.nnet");
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Net copyNet("mynet.nnet");
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}
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catch (std::exception &ex)
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{
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std::cerr << "Error: " << ex.what() << std::endl;
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return 1;
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}
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return 0;
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} |