private void UpdateLayer(int PWhichLayer, bool PForward, AutoencoderWeights PWeights)
{
int beginlayer = PWhichLayer - 1;
if (PForward)
{
Utility.WithinBounds("Cannot update this layer!!!", PWhichLayer, 1, numlayers);
}
else
{
Utility.WithinBounds("Cannot update this layer!!!", PWhichLayer, 0, numlayers - 1);
beginlayer = PWhichLayer + 1;
}
RBMLayer thislayer = layers[PWhichLayer];
RBMLayer previouslayer = layers[beginlayer];
double input = 0;
double[] states = previouslayer.GetStates();
for (int i = 0; i < thislayer.Count; i++)
{
for (int j = 0; j < previouslayer.Count; j++)
{
if (!PForward)
{
input += PWeights.GetWeightSet(beginlayer - 1).GetWeight(i, j) * states[j];
}
else
{
input += PWeights.GetWeightSet(beginlayer).GetWeight(j, i) * states[j];
}
}
thislayer.SetState(i, input);
input = 0;
}
}
public object Clone()
{
AutoencoderWeights retval = new AutoencoderWeights();
retval.numweightsets = numweightsets;
retval.weights = new RBMWeightSet[numweightsets];
for (int i = 0; i < numweightsets; i++)
{
retval.weights[i] = (RBMWeightSet)weights[i].Clone();
}
return(retval);
}
internal Autoencoder(List <RBMLayer> PLayers, AutoencoderLearningRate PTrainingInfo
, IWeightInitializer PWInitializer)
{
numlayers = PLayers.Count;
layers = PLayers.ToArray();
learnrate = PTrainingInfo;
recognitionweights = new AutoencoderWeights(numlayers, layers, PWInitializer);
generativeweights = new AutoencoderWeights(numlayers, layers, PWInitializer);
errorobservers = new List <IErrorObserver>();
InitBiases(PWInitializer);
InitTrainingData();
}
////////////////////////////////////////////////////////////////////////////////////////////////////
/// <summary> Pre training complete. </summary>
///
/// <exception cref="Exception"> Thrown when an exception error condition occurs. </exception>
////////////////////////////////////////////////////////////////////////////////////////////////////
public void PreTrainingComplete()
{
if (!pretraining)
{
throw new AutoEncoderException("You can only call PreTrainingComplete() once!");
}
pretraining = false;
for (int i = 0; i < numlayers; i++)
{
generativeweights = (AutoencoderWeights)recognitionweights.Clone();
}
}
public void PreTrainingComplete()
{
if (!pretraining)
{
throw new Exception("You have already called PreTrainingComplete()!"
+ " Don't call it twice!");
}
pretraining = false;
for (int i = 0; i < numlayers; i++)
{
generativeweights = (AutoencoderWeights)recognitionweights.Clone();
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////
/// <summary> Creates a new object that is a copy of the current instance. </summary>
///
/// <returns> A new object that is a copy of this instance. </returns>
///
/// <seealso cref="M:System.ICloneable.Clone()"/>
////////////////////////////////////////////////////////////////////////////////////////////////////
public object Clone()
{
AutoencoderWeights retval = new AutoencoderWeights
{
numweightsets = numweightsets,
weights = new RestrictedBoltzmannMachineWeightSet[numweightsets]
};
for (int i = 0; i < numweightsets; i++)
{
retval.weights[i] = (RestrictedBoltzmannMachineWeightSet)weights[i].Clone();
}
return(retval);
}
