| public RunEpoch ( double input ) : double | ||
| input | double | Array of input vectors. |
| Результат | double | |
// Worker thread
void SearchSolution( )
{
// set random generators range
Neuron.RandRange = new DoubleRange( 0, Math.Max( pointsPanel.ClientRectangle.Width, pointsPanel.ClientRectangle.Height ) );
// create network
DistanceNetwork network = new DistanceNetwork( 2, networkSize * networkSize );
// create learning algorithm
SOMLearning trainer = new SOMLearning( network, networkSize, networkSize );
// create map
map = new int[networkSize, networkSize, 3];
double fixedLearningRate = learningRate / 10;
double driftingLearningRate = fixedLearningRate * 9;
// iterations
int i = 0;
RefreshDelegate refreshIterationsBox = delegate () {
currentIterationBox.Text = i.ToString( );
};
RefreshDelegate enableControls = delegate () {
EnableControls( true );
};
// loop
while ( !needToStop )
{
trainer.LearningRate = driftingLearningRate * ( iterations - i ) / iterations + fixedLearningRate;
trainer.LearningRadius = (double) learningRadius * ( iterations - i ) / iterations;
// run training epoch
trainer.RunEpoch( trainingSet );
// update map
UpdateMap( network );
// increase current iteration
i++;
// set current iteration's info
this.Invoke(refreshIterationsBox);
// stop ?
if ( i >= iterations )
break;
}
// enable settings controls
this.Invoke(enableControls);
}
/// <summary>
/// Enseña patrones a la red en un hilo secundario.
/// </summary>
protected override void teachingWorker_DoWork(object sender, DoWorkEventArgs e)
{
// Extrae los kanjis
var allKanjis = (List<Kanji>)e.Argument;
// Genera una lista de patrones, una por cada kanji
var allPatterns = new List<Pattern>(allKanjis.Count);
foreach (var kanji in allKanjis)
{
//Dependiendo del modo obj de aprendizaje genera el patron
string imageHash = string.Empty;
Pattern pattern = null;
switch (Method)
{
case GenerationMethod.Normal:
case GenerationMethod.Heightmap:
generatePattern_Normal(kanji.sourceImage, out pattern, out imageHash);
break;
case GenerationMethod.Hashing:
generatePattern_Hashing(kanji.sourceImage, out pattern, out imageHash);
break;
}
// Lo agrega a la lista de patrones a aprender
allPatterns.Add(pattern);
}
// Crea un entrenador
var sqrtOutputSize = (int)Math.Sqrt(OutputSize);
SOMLearning trainer = new SOMLearning(somNN, sqrtOutputSize, sqrtOutputSize);
// Crea el set de datos a entrenar
var trainingSet = generateInputSet(allPatterns);
// Iteraciones de aprendizaje
for (int i = 0; i < LearningIterations; i++)
{
// Establece los valores de aprendizaje y radio para la corrida actual
var completedRatio = (float)i / (LearningIterations - 1);
trainer.LearningRate = completedRatio * LearningEndingRate + (1 - completedRatio) * LearningInitialRate;
trainer.LearningRadius = completedRatio * LearningEndingRadius + (1 - completedRatio) * LearningInitialRadius;
// Ejecuta la corrida
trainer.RunEpoch(trainingSet);
// Informa progreso
((BackgroundWorker)sender).ReportProgress((int)(completedRatio * 100 * .99f));
// Revisa la solicitud de cancelar
if (needToStop)
{
e.Cancel = true;
break;
}
}
// Genera la lista de clases para cada patron aprendido
var classes = new List<int>(allPatterns.Count);
foreach (var pattern in allPatterns) classes.Add(classifyPattern(pattern));
if (classes.GroupBy(i => i).Where(g => g.Count() > 1).ToList().Count > 0)
throw new Exception("No se pudo distinguir la diferencia de clase entre dos kanjis.");
// Para cada patron busca la neurona representante
for (int i = 0; i < allKanjis.Count; i++)
{
// Guarda el kanji en el diccionario
learnedKanjis.Add(classes[i].ToString(), allKanjis[i]);
}
}
void KohonenTeach()
{
Neuron.RandRange = new Range(0.0f, 1.0f);
kohonenNetwork = new DistanceNetwork(liczba_wejsc, liczba_neuronow_pion * liczba_neuronow_poziom);
kohonenNetwork.Randomize();
SOMLearning teacher = new SOMLearning(kohonenNetwork);
double driftingLearningRate = this.wps_zmiany_wsp;
double fixedLearningRate = this.pocz_wart_wsp_nauki;
double learningRadius = this.pocz_rozmiar_sasiedz;
double driftingLearningRadius = this.wsp_zmian_rozm_sasiedz;
int iteration = 1;
while (!needToStop)
{
teacher.LearningRate = driftingLearningRate * (numberOfCycles - iteration) / numberOfCycles + fixedLearningRate;
teacher.LearningRadius = (double)learningRadius * (numberOfCycles - iteration) / numberOfCycles;
teacher.RunEpoch(trainingKData);
SetIterationsCount(iteration++);
if (iteration > numberOfCycles) break;
}
teachingDone = true;
needToStop = false;
UpdateLabels();
}