bool train( const Ptr<TrainData>& trainData, int flags )
{
const int MAX_ITER = 1000;
const double DEFAULT_EPSILON = FLT_EPSILON;
// initialize training data
Mat inputs = trainData->getTrainSamples();
Mat outputs = trainData->getTrainResponses();
Mat sw = trainData->getTrainSampleWeights();
prepare_to_train( inputs, outputs, sw, flags );
// ... and link weights
if( !(flags & UPDATE_WEIGHTS) )
init_weights();
TermCriteria termcrit;
termcrit.type = TermCriteria::COUNT + TermCriteria::EPS;
termcrit.maxCount = std::max((params.termCrit.type & CV_TERMCRIT_ITER ? params.termCrit.maxCount : MAX_ITER), 1);
termcrit.epsilon = std::max((params.termCrit.type & CV_TERMCRIT_EPS ? params.termCrit.epsilon : DEFAULT_EPSILON), DBL_EPSILON);
int iter = params.trainMethod == ANN_MLP::BACKPROP ?
train_backprop( inputs, outputs, sw, termcrit ) :
train_rprop( inputs, outputs, sw, termcrit );
trained = iter > 0;
return trained;
}
int CvANN_MLP::train( const CvMat* _inputs, const CvMat* _outputs,
const CvMat* _sample_weights, const CvMat* _sample_idx,
CvANN_MLP_TrainParams _params, int flags )
{
const int MAX_ITER = 1000;
const double DEFAULT_EPSILON = FLT_EPSILON;
double* sw = 0;
CvVectors x0, u;
int iter = -1;
x0.data.ptr = u.data.ptr = 0;
CV_FUNCNAME( "CvANN_MLP::train" );
__BEGIN__;
int max_iter;
double epsilon;
params = _params;
// initialize training data
CV_CALL( prepare_to_train( _inputs, _outputs, _sample_weights,
_sample_idx, &x0, &u, &sw, flags ));
// ... and link weights
if( !(flags & UPDATE_WEIGHTS) )
init_weights();
max_iter = params.term_crit.type & CV_TERMCRIT_ITER ? params.term_crit.max_iter : MAX_ITER;
max_iter = MIN( max_iter, MAX_ITER );
max_iter = MAX( max_iter, 1 );
epsilon = params.term_crit.type & CV_TERMCRIT_EPS ? params.term_crit.epsilon : DEFAULT_EPSILON;
epsilon = MAX(epsilon, DBL_EPSILON);
params.term_crit.type = CV_TERMCRIT_ITER + CV_TERMCRIT_EPS;
params.term_crit.max_iter = max_iter;
params.term_crit.epsilon = epsilon;
if( params.train_method == CvANN_MLP_TrainParams::BACKPROP )
{
CV_CALL( iter = train_backprop( x0, u, sw ));
}
else
{
CV_CALL( iter = train_rprop( x0, u, sw ));
}
__END__;
cvFree( &x0.data.ptr );
cvFree( &u.data.ptr );
cvFree( &sw );
return iter;
}
