private void createMatrixLayers()
{
matrixLayers = new MatrixLayer[sourceNetwork.LayersCount];
matrixLayers[0] = new MatrixInputLayer(sourceNetwork.getLayerAt(0).NeuronsCount);
MatrixLayer prevLayer = matrixLayers[0];
for (int i = 1; i < sourceNetwork.LayersCount; i++)
{
Layer layer = sourceNetwork.getLayerAt(i);
MatrixMlpLayer newBpLayer = new MatrixMlpLayer(layer, prevLayer, new Tanh());
matrixLayers[i] = newBpLayer;
prevLayer = newBpLayer;
}
}
// http://netbeans.org/kb/docs/java/profiler-profilingpoints.html
protected internal virtual void updateLayerWeights(MatrixMlpLayer layer, double[] errors)
{
double[] inputs = layer.Inputs;
double[][] weights = layer.Weights;
double[][] deltaWeights = layer.DeltaWeights;
for (int neuronIdx = 0; neuronIdx < layer.NeuronsCount; neuronIdx++) // iterate neurons
{
for (int weightIdx = 0; weightIdx < weights[neuronIdx].Length; weightIdx++) // iterate weights
{
// calculate weight change
double deltaWeight = this.learningRate * errors[neuronIdx] * inputs[weightIdx] + momentum * (deltaWeights[neuronIdx][weightIdx]);
deltaWeights[neuronIdx][weightIdx] = deltaWeight; // save weight change to calculate momentum
weights[neuronIdx][weightIdx] += deltaWeight; // apply weight change
}
}
}
/// <summary>
/// This method implements weights update procedure for the output neurons
/// </summary>
/// <param name="patternError">
/// single pattern error vector </param>
protected internal override void calculateErrorAndUpdateOutputNeurons(double[] patternError)
{
// get output layer
MatrixMlpLayer outputLayer = (MatrixMlpLayer)matrixLayers[matrixLayers.Length - 1];
TransferFunction transferFunction = outputLayer.TransferFunction;
// get output vector
double[] outputs = outputLayer.Outputs;
double[] netInputs = outputLayer.NetInput;
double[] neuronErrors = outputLayer.Errors; // these will hold -should be set from here!!!!
// calculate errors(deltas) for all output neurons
for (int i = 0; i < outputs.Length; i++)
{
neuronErrors[i] = patternError[i] * transferFunction.getDerivative(netInputs[i]); // ovde mi treba weighted sum, da ne bi morao ponovo da racunam
}
// update weights
this.updateLayerWeights(outputLayer, neuronErrors);
Console.WriteLine("MSE:" + ErrorFunction.TotalError);
}
/// <summary>
/// backpropogate errors through all hidden layers and update conneciion weights
/// for those layers.
/// </summary>
protected internal override void calculateErrorAndUpdateHiddenNeurons()
{
int layersCount = matrixMlp.LayersCount;
for (int layerIdx = layersCount - 2; layerIdx > 0; layerIdx--)
{
MatrixMlpLayer currentLayer = (MatrixMlpLayer)matrixLayers[layerIdx];
TransferFunction transferFunction = currentLayer.TransferFunction;
int neuronsCount = currentLayer.NeuronsCount;
double[] neuronErrors = currentLayer.Errors;
double[] netInputs = currentLayer.NetInput;
MatrixMlpLayer nextLayer = (MatrixMlpLayer)currentLayer.NextLayer;
double[] nextLayerErrors = nextLayer.Errors;
double[][] nextLayerWeights = nextLayer.Weights;
// calculate error for each neuron in current layer
for (int neuronIdx = 0; neuronIdx < neuronsCount; neuronIdx++)
{
// calculate weighted sum of errors of all neuron it is attached to - calculate how much this neuron is contributing to errors in next layer
double weightedErrorsSum = 0;
for (int nextLayerNeuronIdx = 0; nextLayerNeuronIdx < nextLayer.NeuronsCount; nextLayerNeuronIdx++)
{
weightedErrorsSum += nextLayerErrors[nextLayerNeuronIdx] * nextLayerWeights[nextLayerNeuronIdx][neuronIdx];
}
// calculate the error for this neuron
neuronErrors[neuronIdx] = transferFunction.getDerivative(netInputs[neuronIdx]) * weightedErrorsSum;
} // neuron iterator
this.updateLayerWeights(currentLayer, neuronErrors);
} // layer iterator
}
