public AutoencoderWeights(int PNumLayers, RBMLayer[] PLayers, IWeightInitializer PWInitializer)
{
numweightsets = PNumLayers - 1;
weights = new RBMWeightSet[numweightsets];
for (int i = 0; i < numweightsets; i++)
{
weights[i] = new RBMWeightSet(PLayers[i].Count, PLayers[i + 1].Count, PWInitializer);
}
}
internal void Load(TextReader PFile)
{
numweightsets = int.Parse(PFile.ReadLine());
weights = new RBMWeightSet[numweightsets];
for (int i = 0; i < numweightsets; i++)
{
weights[i] = RBMWeightSet.Load(PFile);
}
}
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;
}
private void InitWeights(IWeightInitializer PWeightInit)
{
weights = new RBMWeightSet(numvisibles, numhiddens, PWeightInit);
for (int i = 0; i < numvisibles; i++)
{
for (int j = 0; j < numhiddens; j++)
{
weights.SetWeight(i, j, Utility.NextGaussian(0, 0.1));
}
}
}
public object Clone()
{
RBMWeightSet newweights = new RBMWeightSet(presize, postsize, new ZeroWeightInitializer());
for (int i = 0; i < presize; i++)
{
for (int j = 0; j < postsize; j++)
{
newweights.SetWeight(i, j, weights[i][j]);
}
}
return(newweights);
}
internal static RBMWeightSet Load(TextReader PFile)
{
RBMWeightSet retval = new RBMWeightSet();
retval.presize = int.Parse(PFile.ReadLine());
retval.weights = new double[retval.presize][];
retval.weightchanges = new double[retval.presize][];
for (int i = 0; i < retval.presize; i++)
{
retval.weights[i] = Utility.LoadArray(PFile);
}
retval.postsize = retval.weights[0].GetLength(0);
for (int i = 0; i < retval.presize; i++)
{
retval.weightchanges[i] = new double[retval.postsize];
}
return(retval);
}
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]);
}
}
