/// <summary>
/// This method implements weights update procedure for the single neuron for
/// the back propagation with momentum factor
/// </summary>
/// <param name="neuron"> neuron to update weights </param>
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;
// tanh can be used to minimise the impact of big error values, which can cause network instability
// suggested at https://sourceforge.net/tracker/?func=detail&atid=1107579&aid=3130561&group_id=238532
// double neuronError = Math.tanh(neuron.getError());
Weight weight = connection.Weight;
MomentumWeightTrainingData weightTrainingData = (MomentumWeightTrainingData)weight.TrainingData;
//double currentWeightValue = weight.getValue();
double previousWeightValue = weightTrainingData.previousValue;
double weightChange = this.learningRate * neuronError * input + momentum * (weight.value - previousWeightValue);
// save previous weight value
//weight.getTrainingData().set(TrainingData.PREVIOUS_WEIGHT, currentWeightValue);
weightTrainingData.previousValue = weight.value;
// if the learning is in batch mode apply the weight change immediately
if (this.InBatchMode == false)
{
weight.weightChange = weightChange;
weight.value += weightChange;
} // otherwise, sum the weight changes and apply them after at the end of epoch
else
{
weight.weightChange += weightChange;
}
}
}
/// <summary>
/// Creates connections with shared weights between two feature maps
/// Assumes that toMap is from Pooling layer.
/// <p/>
/// In this implementation, there is no overlapping between kernel positions.
/// </summary>
/// <param name="fromMap"> source feature map </param>
/// <param name="toMap"> destination feature map </param>
public override void connectMaps(FeatureMapLayer fromMap, FeatureMapLayer toMap)
{
int kernelWidth = kernel.Width;
int kernelHeight = kernel.Height;
Weight weight = new Weight(1);
for (int x = 0; x < fromMap.Width - kernelWidth + 1; x += kernelWidth) // < da li step treba da je kernel
{
for (int y = 0; y < fromMap.Height - kernelHeight + 1; y += kernelHeight)
{
Neuron toNeuron = toMap.getNeuronAt(x / kernelWidth, y / kernelHeight);
for (int dy = 0; dy < kernelHeight; dy++)
{
for (int dx = 0; dx < kernelWidth; dx++)
{
int fromX = x + dx;
int fromY = y + dy;
Neuron fromNeuron = fromMap.getNeuronAt(fromX, fromY);
ConnectionFactory.createConnection(fromNeuron, toNeuron, weight);
}
}
}
}
}
/// <summary>
/// Creates connection between two specified neurons
/// </summary>
/// <param name="fromNeuron">
/// neuron to connect (connection source) </param>
/// <param name="toNeuron">
/// neuron to connect to (connection target) </param>
/// <param name="weight">
/// connection weight </param>
public static void createConnection(Neuron fromNeuron, Neuron toNeuron, Weight weight)
{
Connection connection = new Connection(fromNeuron, toNeuron, weight);
toNeuron.addInputConnection(connection);
}
