void main()
{
NET Net;
BOOL Stop;
REAL MinTestError;
InitializeRandoms();
GenerateNetwork(&Net);
RandomWeights(&Net);
InitializeApplication(&Net);
Stop = FALSE;
MinTestError = MAX_REAL;
do {
TrainNet(&Net, 10);
TestNet(&Net);
if (TestError < MinTestError) {
fprintf(f, " - saving Weights ...");
MinTestError = TestError;
SaveWeights(&Net);
}
else if (TestError > 1.2 * MinTestError) {
fprintf(f, " - stopping Training and restoring Weights ...");
Stop = TRUE;
RestoreWeights(&Net);
}
} while (NOT Stop);
TestNet(&Net);
EvaluateNet(&Net);
FinalizeApplication(&Net);
}
void MultiLayerPerceptron::Run(const char* fname, const int& maxiter)
{
int countTrain = 0;
int countLines = 0;
bool Stop = false;
bool firstIter = true;
double dMinTestError = 0.0;
InitializeRandoms();
RandomWeights();
do {
countLines += Train(fname);
Test(fname);
countTrain++;
if(firstIter)
{
dMinTestError = dAvgTestError;
firstIter = false;
}
printf( "%i \t TestError: %f", countTrain, dAvgTestError);
if ( dAvgTestError < dMinTestError)
{
printf(" -> saving weights\n");
dMinTestError = dAvgTestError;
SaveWeights();
}
else if (dAvgTestError > 1.2 * dMinTestError)
{
printf(" -> stopping training and restoring weights\n");
Stop = true;
RestoreWeights();
}
else
{
printf(" -> ok\n");
}
} while ( (!Stop) && (countTrain<maxiter) );
printf("bye\n");
}
