int main()
{
int nInput = 2;
int nHide = 2;
int nOutput = 1;
BpNet bpNet(nInput, nHide, nOutput);
int n = 4;
double **ppInput, **ppOutput;
ppInput = new double *[n];
ppOutput = new double *[n];
for (int i = 0; i < n; i++)
{
ppInput[i] = new double[2];
ppOutput[i] = new double[1];
}
/*ppInput[0][0] = 0.8; ppInput[0][1] = 0.5; ppInput[0][2] = 0;
ppInput[1][0] = 0.9; ppInput[1][1] = 0.7; ppInput[1][2] = 0.3;
ppInput[2][0] = 1; ppInput[2][1] = 0.8; ppInput[2][2] = 0.5;
ppInput[3][0] = 0; ppInput[3][1] = 0.2; ppInput[3][2] = 0.3;
ppInput[4][0] = 0.2; ppInput[4][1] = 0.1; ppInput[4][2] = 1.3;
ppInput[5][0] = 0.2; ppInput[5][1] = 0.7; ppInput[5][2] = 0.8;
ppOutput[0][0] = 0; ppOutput[0][1] = 1;
ppOutput[1][0] = 0; ppOutput[1][1] = 1;
ppOutput[2][0] = 0; ppOutput[2][1] = 1;
ppOutput[3][0] = 1; ppOutput[3][1] = 0;
ppOutput[4][0] = 1; ppOutput[4][1] = 0;
ppOutput[5][0] = 1; ppOutput[5][1] = 0;
*/
ppInput[0][0] = 0; ppInput[0][1] = 0;
ppInput[1][0] = 0; ppInput[1][1] = 1;
ppInput[2][0] = 1; ppInput[2][1] = 0;
ppInput[3][0] = 1; ppInput[3][1] = 1;
ppOutput[0][0] = 0;
ppOutput[1][0] = 1;
ppOutput[2][0] = 1;
ppOutput[3][0] = 0;
bpNet.Train(n, ppInput, ppOutput);
double pTest[1];
double pRs[1];
while (1)
{
printf("test:\n");
scanf("%lf%lf", &pTest[0], &pTest[1]);
bpNet.Classify(pTest, pRs);
printf("%lf\n", pRs[0]);
}
return 0;
}
bool trial(const backpropSettings& bSettings, const boost::array< boost::array<float, inputWidth>, numTrials >& cTrials, const boost::array< boost::array<float, outputWidth>, numTrials >& cResults)
{
size_t midWidth = (inputWidth + outputWidth);
while ((midWidth & 15) != 0) { ++midWidth; };
boost::array<size_t, 3> layers = {{inputWidth , midWidth , outputWidth}};
std::cout << "dimensions= " << inputWidth << "," << midWidth << "," << outputWidth << "\nnumTrials= " << cTrials.size() << "\n";
//boost::array<size_t, 2> layers = {{16 , 2}};
neuralNet cNet(layers.begin(), layers.end());
backpropNet bpNet(cNet, bSettings);
bpNet.randomizeWeights();
double _MSE = 1.0;
double MSE = 0.0;
double numTrials = 0.0;
boost::timer cTimer;
for (size_t iEpoch = 0; iEpoch < 3000; ++iEpoch)
{
// determine an order that we intend to visit the trials:
std::vector<size_t> order(cTrials.size());
for (size_t iTurn = 0; iTurn != order.size(); ++iTurn) { order[iTurn] = iTurn; }
std::random_shuffle(order.begin(), order.end());
// jitter:
if ((iEpoch % 1000) == 0)
{
bpNet.jitterNetwork();
}
// perform trials:
for (size_t iNTrial = 0; iNTrial != order.size(); ++iNTrial)
{
size_t iTrial = order[iNTrial];
bpNet.getNeuralNet().feedForward(cTrials[iTrial].begin());
MSE += bpNet.backPropagate(cResults[iTrial].begin());
assert(MSE == MSE);
numTrials += 1.0;
bpNet.updateWeights();
}
// debug:
if ((iEpoch+1) % 100 == 0)
{
//rankedNet.updateWeights();
_MSE = MSE/numTrials;
std::cout << std::setw(4) << iEpoch << " " << _MSE << "\n";
MSE = 0.0;
numTrials = 0.0;
}
}
std::cout << "test " << ((_MSE<0.05)?"passed":"FAILED") << "! Elapsed time = " << cTimer.elapsed() << "\n";
return _MSE<0.05;
};
