private void ComputeGradient(Gradients gradients, List <double> inputs, List <double> requiredOutputs)
{
Activities(inputs);
for (int i = NumberOfLayers() - 1; i >= 1; i--)
{
NeuralLayer currentLayer = GetLayer(i);
if (currentLayer.IsLayerTop())
{
for (int j = 0; j < currentLayer.NumberOfNeurons(); j++)
{
Neuron currentNeuron = currentLayer.GetNeuron(j);
gradients.SetThreshold(i, j,
currentNeuron.Output * (1 - currentNeuron.Output) *
(currentNeuron.Output - requiredOutputs[j]));
}
for (int j = 0; j < currentLayer.NumberOfNeurons(); j++)
{
Neuron currentNeuron = currentLayer.GetNeuron(j);
for (int k = 0; k < currentNeuron.NumberOfInputs(); k++)
{
NeuralInput currentInput = currentNeuron.GetInput(k);
gradients.SetWeight(i, j, k,
gradients.GetThreshold(i, j) * currentLayer.LowerLayer().GetNeuron(k).Output);
}
}
}
else
{
for (int j = 0; j < currentLayer.NumberOfNeurons(); j++)
{
double aux = 0;
for (int ia = 0; ia < currentLayer.UpperLayer().NumberOfNeurons(); ia++)
{
aux += gradients.GetThreshold(i + 1, ia) *
currentLayer.UpperLayer().GetNeuron(ia).GetInput(j).Weight;
}
gradients.SetThreshold(i, j,
currentLayer.GetNeuron(j).Output *(1 - currentLayer.GetNeuron(j).Output) * aux);
}
for (int j = 0; j < currentLayer.NumberOfNeurons(); j++)
{
Neuron currentNeuron = currentLayer.GetNeuron(j)
;
for (int k = 0; k < currentNeuron.NumberOfInputs(); k++)
{
NeuralInput currentInput = currentNeuron.GetInput(k);
gradients.SetWeight(i, j, k,
gradients.GetThreshold(i, j) * currentLayer.LowerLayer().GetNeuron(k).Output);
}
}
}
}
}
private void LoadFromXml(string filePath)
{
Console.WriteLine("NeuralNetwork : loading network topology from file " + filePath);
// Read XML to XmlDocument
XmlDocument xmlDocument = new XmlDocument();
xmlDocument.Load(filePath);
string rootNodeName = xmlDocument.Name;
if (rootNodeName != "neuralNetwork")
{
throw new InvalidOperationException(
"[Error] NN-Load: Parse error in XML file, neural network couldn't be loaded.");
}
var nodeList = xmlDocument.ChildNodes;
for (int i = 0; i < nodeList.Count; i++)
{
var nodeStructure = nodeList.Item(i);
if (nodeStructure != null && nodeStructure.Name == "structure")
{
var nodeStructureContent = nodeStructure.ChildNodes;
for (int j = 0; j < nodeStructureContent.Count; j++)
{
var nodeLayer = nodeStructureContent.Item(j);
if (nodeLayer != null && nodeLayer.Name == "layer")
{
NeuralLayer neuralLayer = new NeuralLayer(this);
listLayers.Add(neuralLayer);
var nodeLayerContent = nodeLayer.ChildNodes;
for (int k = 0; k < nodeLayerContent.Count; k++)
{
var nodeNeuron = nodeLayerContent.Item(k);
if (nodeNeuron != null && nodeNeuron.Name == "neuron")
{
Neuron neuron = new Neuron(double.Parse(nodeNeuron.Attributes["threshold"].ToString()), neuralLayer);
neuralLayer.listNeurons.Add(neuron);
var nodeNeuronContent = nodeNeuron.ChildNodes;
for (int l = 0; l < nodeNeuronContent.Count; l++)
{
var nodeNeuralInput = nodeNeuronContent.Item(l);
if (nodeNeuralInput != null && nodeNeuralInput.Name == "input")
{
var neuralInput = new NeuralInput(double.Parse(nodeNeuralInput.Attributes["weight"].ToString()), neuron);
neuron.listInputs.Add(neuralInput);
}
}
}
}
}
}
}
}
}
