public RBM(RBMLayer PVisibles, RBMLayer PHiddens, RBMLearningRate PLearnRate, IWeightInitializer PWeightInit)
{
numvisibles = PVisibles.Count;
numhiddens = PHiddens.Count;
InitLayers(PVisibles, PHiddens);
InitWeights(PWeightInit);
InitTrainingData();
learnrate = PLearnRate;
}
private void PerformPreTraining(int PPreSynapticLayer)
{
RBMLearningRate sentlearnrate = new RBMLearningRate(learnrate.prelrweights[PPreSynapticLayer]
, learnrate.prelrbiases[PPreSynapticLayer]
, learnrate.premomweights[PPreSynapticLayer]
, learnrate.premombiases[PPreSynapticLayer]);
RBMTrainer.Train(layers[PPreSynapticLayer], layers[PPreSynapticLayer + 1], trainingdata[PPreSynapticLayer]
, sentlearnrate, recognitionweights.GetWeightSet(PPreSynapticLayer));
}
public RBM(RBM PA)
{
visibles = (RBMLayer)PA.visibles.Clone();
hiddens = (RBMLayer)PA.hiddens.Clone();
weights = (RBMWeightSet)PA.weights.Clone();
learnrate = PA.learnrate;
trainingdata = PA.trainingdata;
numvisibles = PA.numvisibles;
numhiddens = PA.numhiddens;
}
public static void Train(RBMLayer PLayerVis, RBMLayer PLayerHid, TrainingData PData
, RBMLearningRate PLearnRate, RBMWeightSet PWeightSet)
{
weightset = PWeightSet;
learnrate = PLearnRate;
for (int i = 0; i < PLayerVis.Count; i++)
{
for (int j = 0; j < PLayerHid.Count; j++)
{
TrainWeight(i, j, CalculateTrainAmount(PData.posvis[i], PData.poshid[j]
, PData.negvis[i], PData.neghid[j]));
}
TrainBias(PLayerVis, i, PData.posvis[i], PData.negvis[i]);
}
for (int j = 0; j < PLayerHid.Count; j++)
{
TrainBias(PLayerHid, j, PData.poshid[j], PData.neghid[j]);
}
}
