//Task execution
public override void Execute()
{
if (ReconstructionSource == ReconstructionSource.INPUT)
{
InputLayer.RBMInputForward();
for (int i = 0; i < CD_k; i++)
{
RBMForwardPass(CurrentLayerIndex, i >= (CD_k - 1));
RBMBackwardPass(CurrentLayerIndex, i >= (CD_k - 1));
}
}
else
{
MyRBMLayer layer = ((MyRBMLayer)layers[CurrentLayerIndex]);
if (layer.Target != null && layer.Target.Count > 0)
{
layer.Output.CopyFromMemoryBlock(layer.Target, 0, 0, layer.Neurons);
}
RBMBackwardPass(CurrentLayerIndex, false);
for (int i = 0; i < CD_k; i++)
{
RBMForwardPass(CurrentLayerIndex, i >= (CD_k - 1));
RBMBackwardPass(CurrentLayerIndex, i >= (CD_k - 1));
}
}
MyLog.DEBUG.WriteLine("RBM reconstruction between layers [" + CurrentLayerIndex + ";" + (CurrentLayerIndex + 1) + "], step " + step + ".");
++step;
}
//Task execution
public override void Execute()
{
if (layers.Count <= CurrentLayerIndex)
{
MyLog.ERROR.WriteLine("Invalid CurrentLayerIndex " + CurrentLayerIndex +
". Must be smaller than number of layers which is " + layers.Count);
return;
}
if (ReconstructionSource == ReconstructionSource.INPUT)
{
InputLayer.RBMInputForward();
for (int i = 0; i < CD_k; i++)
{
RBMForwardPass(CurrentLayerIndex, i >= (CD_k - 1));
RBMBackwardPass(CurrentLayerIndex, i >= (CD_k - 1));
}
}
else
{
// we want to reconstruct from hidden, set layer index to first hidden if it was set to visible
if (CurrentLayerIndex == 0)
{
CurrentLayerIndex = 1;
}
MyRBMLayer layer = ((MyRBMLayer)layers[CurrentLayerIndex]);
if (layer.Target != null && layer.Target.Count > 0)
{
layer.Output.CopyFromMemoryBlock(layer.Target, 0, 0, layer.Neurons);
}
RBMBackwardPass(CurrentLayerIndex, false);
for (int i = 0; i < CD_k; i++)
{
RBMForwardPass(CurrentLayerIndex, i >= (CD_k - 1));
RBMBackwardPass(CurrentLayerIndex, i >= (CD_k - 1));
}
}
MyLog.DEBUG.WriteLine("RBM reconstruction between layers [" + CurrentLayerIndex + ";" + (CurrentLayerIndex + 1) + "], step " + step + ".");
++step;
}
//Task execution
public override void Execute()
{
// sampling between input and hidden layer
if (CurrentLayerIndex <= 0)
{
InputLayer.RBMInputForwardAndStore();
MyRBMInputLayer visible = InputLayer;
hidden = (MyRBMLayer)layers[CurrentLayerIndex + 1];
if (hidden.Dropout > 0)
hidden.CreateDropoutMask();
// forward to visible and hidden layers and store for sampling biases
hidden.RBMForwardAndStore(SigmoidSteepness);
// sample positive weight data
hidden.RBMSamplePositive();
if (RandomHidden)
hidden.RBMRandomActivation();
// do k-step Contrastive Divergence (go back and forth between visible and hidden)
for (int i = 0; i < CD_k - 1; i++)
{
// back
hidden.RBMBackward(visible.Bias, SigmoidSteepness);
if (RandomVisible)
visible.RBMRandomActivation();
// and forth
hidden.RBMForward(SigmoidSteepness, true);
// randomly activate the just updated hidden neurons if needed
if (RandomHidden)
hidden.RBMRandomActivation();
}
// in the last (= k-th) step of CD-k, we use probabilistic modeling -> we sample both with probability, not random activation
hidden.RBMBackward(visible.Bias, SigmoidSteepness);
hidden.RBMForward(SigmoidSteepness, true);
if (hidden.StoreEnergy)
hidden.Energy.Fill(0);
// update biases of both layers based on the sampled data stored by RBMForwardAndStore()
visible.RBMUpdateBiases(LearningRate, Momentum, WeightDecay);
hidden.RBMUpdateBiases(LearningRate, Momentum, WeightDecay);
// sample negative weight data AND adapt weights at the same time
hidden.RBMUpdateWeights(LearningRate, Momentum, WeightDecay);
}
// sampling between hidden layers
// spatny indexovani!
else if (layers.Count > 2)//if ( pocet hidden vrstev vetsi nez 1)
{
InputLayer.RBMInputForward();
// hidden layers, up to the one that will be visible (excluding)
for (int i = 1; i < CurrentLayerIndex; i++)
{
((MyRBMLayer)layers[i]).RBMForward(SigmoidSteepness, false);
if (RandomPrevious)
((MyRBMLayer)layers[i]).RBMRandomActivation();
}
MyRBMLayer visible = (MyRBMLayer)layers[CurrentLayerIndex];
hidden = (MyRBMLayer)layers[CurrentLayerIndex + 1];
if (hidden.Dropout > 0)
hidden.CreateDropoutMask();
// forward to visible and hidden layers and store for sampling biases
visible.RBMForwardAndStore(SigmoidSteepness);
hidden.RBMForwardAndStore(SigmoidSteepness);
// sample positive weight data
hidden.RBMSamplePositive();
if (RandomHidden)
hidden.RBMRandomActivation();
// do k-step Contrastive Divergence (go back and forth between visible and hidden)
for (int i = 0; i < CD_k - 1; i++)
{
// back
hidden.RBMBackward(visible.Bias, SigmoidSteepness);
if (RandomVisible)
visible.RBMRandomActivation();
// and forth
hidden.RBMForward(SigmoidSteepness, true);
// randomly activate the just updated hidden neurons if needed
if (RandomHidden)
hidden.RBMRandomActivation();
}
// in last (= k-th) step of CD-k, we use probabilistic modeling -> we sample both with probability, not random activation
hidden.RBMBackward(visible.Bias, SigmoidSteepness);
hidden.RBMForward(SigmoidSteepness, true);
if (hidden.StoreEnergy)
hidden.Energy.Fill(0);
// update biases of both layers based on the sampled data stored by RBMForwardAndStore()
visible.RBMUpdateBiases(LearningRate, Momentum, WeightDecay);
hidden.RBMUpdateBiases(LearningRate, Momentum, WeightDecay);
// sample negative weight data AND adapt weights at the same time
hidden.RBMUpdateWeights(LearningRate, Momentum, WeightDecay);
}
else
{
MyLog.ERROR.WriteLine("Wrong index parameter. There are " + layers.Count + " total layers, can't sample from " + CurrentLayerIndex);
}
MyLog.DEBUG.WriteLine("RBM initialization between layers [" + CurrentLayerIndex + ";" + (CurrentLayerIndex + 1) + "], step " + step + ".");
++step;
}
//Task execution
public override void Execute()
{
// sampling between input and hidden layer
if (CurrentLayerIndex <= 0)
{
InputLayer.RBMInputForwardAndStore();
MyRBMInputLayer visible = InputLayer;
hidden = (MyRBMLayer)layers[CurrentLayerIndex + 1];
if (hidden.Dropout > 0)
{
hidden.CreateDropoutMask();
}
// forward to visible and hidden layers and store for sampling biases
hidden.RBMForwardAndStore(SigmoidSteepness);
// sample positive weight data
hidden.RBMSamplePositive();
if (RandomHidden)
{
hidden.RBMRandomActivation();
}
// do k-step Contrastive Divergence (go back and forth between visible and hidden)
for (int i = 0; i < CD_k - 1; i++)
{
// back
hidden.RBMBackward(visible.Bias, SigmoidSteepness);
if (RandomVisible)
{
visible.RBMRandomActivation();
}
// and forth
hidden.RBMForward(SigmoidSteepness, true);
// randomly activate the just updated hidden neurons if needed
if (RandomHidden)
{
hidden.RBMRandomActivation();
}
}
// in the last (= k-th) step of CD-k, we use probabilistic modeling -> we sample both with probability, not random activation
hidden.RBMBackward(visible.Bias, SigmoidSteepness);
hidden.RBMForward(SigmoidSteepness, true);
if (hidden.StoreEnergy)
{
hidden.Energy.Fill(0);
}
// update biases of both layers based on the sampled data stored by RBMForwardAndStore()
visible.RBMUpdateBiases(LearningRate, Momentum, WeightDecay);
hidden.RBMUpdateBiases(LearningRate, Momentum, WeightDecay);
// sample negative weight data AND adapt weights at the same time
hidden.RBMUpdateWeights(LearningRate, Momentum, WeightDecay);
}
// sampling between hidden layers
else if (layers.Count > 2) // if number of hidden layers is greater than 1)
{
InputLayer.RBMInputForward();
// hidden layers, up to the one that will be visible (excluding)
for (int i = 1; i < CurrentLayerIndex; i++)
{
((MyRBMLayer)layers[i]).RBMForward(SigmoidSteepness, false);
if (RandomPrevious)
{
((MyRBMLayer)layers[i]).RBMRandomActivation();
}
}
MyRBMLayer visible = (MyRBMLayer)layers[CurrentLayerIndex];
hidden = (MyRBMLayer)layers[CurrentLayerIndex + 1];
if (hidden.Dropout > 0)
{
hidden.CreateDropoutMask();
}
// forward to visible and hidden layers and store for sampling biases
visible.RBMForwardAndStore(SigmoidSteepness);
hidden.RBMForwardAndStore(SigmoidSteepness);
// sample positive weight data
hidden.RBMSamplePositive();
if (RandomHidden)
{
hidden.RBMRandomActivation();
}
// do k-step Contrastive Divergence (go back and forth between visible and hidden)
for (int i = 0; i < CD_k - 1; i++)
{
// back
hidden.RBMBackward(visible.Bias, SigmoidSteepness);
if (RandomVisible)
{
visible.RBMRandomActivation();
}
// and forth
hidden.RBMForward(SigmoidSteepness, true);
// randomly activate the just updated hidden neurons if needed
if (RandomHidden)
{
hidden.RBMRandomActivation();
}
}
// in last (= k-th) step of CD-k, we use probabilistic modeling -> we sample both with probability, not random activation
hidden.RBMBackward(visible.Bias, SigmoidSteepness);
hidden.RBMForward(SigmoidSteepness, true);
if (hidden.StoreEnergy)
{
hidden.Energy.Fill(0);
}
// update biases of both layers based on the sampled data stored by RBMForwardAndStore()
visible.RBMUpdateBiases(LearningRate, Momentum, WeightDecay);
hidden.RBMUpdateBiases(LearningRate, Momentum, WeightDecay);
// sample negative weight data AND adapt weights at the same time
hidden.RBMUpdateWeights(LearningRate, Momentum, WeightDecay);
}
else
{
MyLog.ERROR.WriteLine("Wrong CurrentLayerIndex parameter. There are " + layers.Count + " total layers, can't sample from " + CurrentLayerIndex);
}
MyLog.DEBUG.WriteLine("RBM initialization between layers [" + CurrentLayerIndex + ";" + (CurrentLayerIndex + 1) + "], step " + step + ".");
++step;
}
