/// <summary>
/// Weight update by done by ResilientPropagation learning rule
/// Executed at the end of epoch (in batch mode) </summary>
/// <param name="weight"> </param>
protected internal virtual void resillientWeightUpdate(Weight weight)
{
// get resilient training data for the current weight
ResilientWeightTrainingtData weightData = (ResilientWeightTrainingtData)weight.TrainingData;
// multiply the current and previous gradient, and take the sign.
// We want to see if the gradient has changed its sign.
int gradientSignChange = sign(weightData.previousGradient * weightData.gradient);
double weightChange = 0; // weight change to apply (delta weight)
double delta; // adaptation factor
if (gradientSignChange > 0)
{
// if the gradient has retained its sign, then we increase delta (adaptation factor) so that it will converge faster
delta = Math.Min(weightData.previousDelta * increaseFactor, maxDelta);
// weightChange = -sign(weightData.gradient) * delta; // if error is increasing (gradient is positive) then subtract delta, if error is decreasing (gradient negative) then add delta
// note that our gradient has different sign eg. -dE_dw so we omit the minus here
weightChange = sign(weightData.gradient) * delta;
weightData.previousDelta = delta;
}
else if (gradientSignChange < 0)
{
// if gradientSignChange<0, then the sign has changed, and the last weight change was too big
delta = Math.Max(weightData.previousDelta * decreaseFactor, minDelta);
// weightChange = - weightData.previousDelta;// 0;// -delta - weightData.previousDelta; // ovo je problematicno treba da bude weightChange
weightChange = -weightData.previousWeightChange; // if it skipped min in previous step go back
// avoid double punishment
weightData.gradient = 0;
weightData.previousGradient = 0;
//move values in the past
weightData.previousDelta = delta;
}
else if (gradientSignChange == 0)
{
// if gradientSignChange==0 then there is no change to the delta
delta = weightData.previousDelta;
//delta = weightData.previousGradient; // note that encog does this
weightChange = sign(weightData.gradient) * delta;
}
weight.value += weightChange;
weightData.previousWeightChange = weightChange;
weightData.previousGradient = weightData.gradient; // as in moveNowValuesToPreviousEpochValues
weightData.gradient = 0;
}
/// <summary>
/// Calculate and sum gradients for each neuron's weight, the actual weight update is done in batch mode </summary>
/// <seealso cref= ResilientPropagation#resillientWeightUpdate(org.neuroph.core.Weight) </seealso>
public override void updateNeuronWeights(Neuron neuron)
{
foreach (Connection connection in neuron.InputConnections)
{
double input = connection.Input;
if (input == 0)
{
continue;
}
// get the error for specified neuron,
double neuronError = neuron.Error;
// get the current connection's weight
Weight weight = connection.Weight;
// ... and get the object that stores reislient training data for that weight
ResilientWeightTrainingtData weightData = (ResilientWeightTrainingtData)weight.TrainingData;
// calculate the weight gradient (and sum gradients since learning is done in batch mode)
weightData.gradient += neuronError * input;
}
}
