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 main()
{
NET Net;
int ct = 0;
InitializeRandoms();
GenerateNetwork(&Net);
RandomWeights(&Net);
InitializeApplication(&Net);
do {
TrainNet(&Net, 10);
if (ct % 10 == 0) {
fprintf(f, "Epoch = %d\n",ct);
EvaluateNet(&Net);
}
ct++;
} while (ct<MAXCT);
fprintf(f, "\n");
fprintf(f, "Finial Epoch:\n");
EvaluateNet(&Net);
FinalizeApplication(&Net);
}
void main()
{
NET Net;
InitializeRandoms();
GenerateNetwork(&Net);
RandomWeights(&Net);
InitializeApplication(&Net);
TrainNet(&Net);
WriteNet(&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");
}
void main()
{
NET Net;
REAL Error;
BOOL Stop;
INT n,m;
InitializeRandoms();
GenerateNetwork(&Net);
RandomWeights(&Net);
InitializeApplication(&Net);
do {
Error = 0;
Stop = TRUE;
for (n=0; n<NUM_DATA; n++) {
SimulateNet(&Net, Input[n], Output[n], FALSE, FALSE);
Error = MAX(Error, Net.Error);
Stop = Stop AND (Net.Error < Net.Epsilon);
}
Error = MAX(Error, Net.Epsilon);
printf("Training %0.0f%% completed ...\n", (Net.Epsilon / Error) * 100);
if (NOT Stop) {
for (m=0; m<10*NUM_DATA; m++) {
n = RandomEqualINT(0, NUM_DATA-1);
SimulateNet(&Net, Input[n], Output[n], TRUE, FALSE);
}
}
} while (NOT Stop);
for (n=0; n<NUM_DATA; n++) {
SimulateNet(&Net, Input[n], Output[n], FALSE, TRUE);
}
FinalizeApplication(&Net);
}
