public void Run()
{
activation = 0;
for (synapticIndex = 0; synapticIndex < TotalInputSynaptics; synapticIndex++)
{
synaticConnection = _ListOfInputSynaptics[synapticIndex];
activation = activation + synaticConnection._SourceNeuron._Value * synaticConnection._Weight;
}
_Value = _ContainerLayer._InternalMemory.ThresholdFunction(activation, _ContainerLayer._NeuralNetwork._LearningRate);
}
public void CreateNetwork(string topoloy, float learningRate)
{
#region Initialization
RaiseTaskStartedEvent(this, "Constructing Network Topology: " + topoloy);
_ListOfLayers = new List <Layer>();
_ListOfNeurons = new List <Neuron>();
_ListOfSynapticConnections = new List <SynapticConnection>();
_SourceTargetNeuronSynapticDictionary = new Dictionary <string, SynapticConnection>();
int neuronid, layerid, synapticid;
neuronid = 0;
layerid = 0;
synapticid = 0;
_TopoloyString = topoloy;
char[] sep = { ',' };
string[] toplogyarray = topoloy.Split(sep);
int numberofneurons = int.Parse(toplogyarray[0]);
int index;
Neuron neuron;
float progress;
#endregion Initialization
_LearningRate = learningRate;
#region InputLayer Code
layerid++;
_InputLayer = new InputLayer(layerid, this);
_ListOfLayers.Add(_InputLayer);
RaiseTaskProgressEvent(this, 0.00f, "Adding Neurons to Input Layer");
for (index = 1; index <= numberofneurons; index++)
{
neuronid++;
neuron = new Neuron(neuronid, _InputLayer);
_InputLayer.AddNeurons(neuron);
_ListOfNeurons.Add(neuron);
progress = ((float)(index) / (float)(numberofneurons)) * 100;
RaiseTaskProgressEvent(this, progress, "");
}
RaiseTaskProgressEvent(this, 0.00f, _InputLayer._ListOfNeurons.Count.ToString() + " Neuron(s) Has Been Added in InputLayer");
#endregion InputLayer Code
#region HiddenLayer Code
int hiddenLayerIndex;
HiddenLayer hiddenLayer;
for (hiddenLayerIndex = 1; hiddenLayerIndex < toplogyarray.Length - 1; hiddenLayerIndex++)
{
layerid++;
RaiseTaskProgressEvent(this, 0.00f, "Adding Neurons to Hidden Layer No.: " + hiddenLayerIndex.ToString());
numberofneurons = int.Parse(toplogyarray[hiddenLayerIndex]);
hiddenLayer = new HiddenLayer(layerid, this);
for (index = 1; index <= numberofneurons; index++)
{
neuronid++;
neuron = new Neuron(neuronid, hiddenLayer);
hiddenLayer.AddNeurons(neuron);
_ListOfNeurons.Add(neuron);
progress = ((float)(index) / (float)(numberofneurons)) * 100;
RaiseTaskProgressEvent(this, progress, "");
}
_ListOfLayers.Add(hiddenLayer);
progress = ((float)(hiddenLayerIndex) / (float)(toplogyarray.Length - 1)) * 100;
RaiseTaskProgressEvent(this, progress, "");
RaiseTaskProgressEvent(this, 0.00f, hiddenLayer._ListOfNeurons.Count.ToString() + " Neuron(s) Has Been Added in HiddenLayer No.: " + hiddenLayerIndex.ToString());
}
#endregion HiddenLayer Code
#region OutputLayer Code
RaiseTaskProgressEvent(this, 0.00f, "Adding Neurons to OutputLayer");
layerid++;
_OutputLayer = new OutputLayer(layerid, this);
neuronid++;
neuron = new Neuron(neuronid, _OutputLayer);
_OutputLayer.AddNeurons(neuron);
_ListOfLayers.Add(_OutputLayer);
_ListOfNeurons.Add(neuron);
RaiseTaskProgressEvent(this, 100.00f, "");
RaiseTaskProgressEvent(this, 0.00f, _OutputLayer._ListOfNeurons.Count.ToString() + " Neuron(s) Has Been Added in OutputLayer");
#endregion OutputLayer Code
Neuron sourceNeuron, targetNeuron;
int sourceNeuronIndex, targetNeuronIndex;
int sourceLayerIndex, targetLayerIndex;
int totalSourceNeurons, totalTargetNeurons;
int totalSourceLayer, totalTargetLayer;
Layer sourceLayer, targetLayer;
SynapticConnection synapticConnection;
float weight;
Random rnd = new Random();
Random rndSign = new Random();
#region Input - First Hidden Synapyic Connection Creation Code
RaiseTaskProgressEvent(this, 0.00f, "Creating Synaptic Connection Between Input - Hidden Layer No.: 1");
sourceLayer = _InputLayer;
targetLayer = _ListOfLayers[1];
totalSourceNeurons = sourceLayer._ListOfNeurons.Count;
totalTargetNeurons = targetLayer._ListOfNeurons.Count;
for (sourceNeuronIndex = 0; sourceNeuronIndex < totalSourceNeurons; sourceNeuronIndex++)
{
sourceNeuron = sourceLayer._ListOfNeurons[sourceNeuronIndex];
for (targetNeuronIndex = 0; targetNeuronIndex < totalTargetNeurons; targetNeuronIndex++)
{
weight = (float)rnd.NextDouble();
if (((float)rndSign.NextDouble()) < 0.5)
{
weight = weight * -1;
}
targetNeuron = targetLayer._ListOfNeurons[targetNeuronIndex];
synapticid++;
synapticConnection = new SynapticConnection(synapticid, sourceNeuron, targetNeuron, weight);
sourceNeuron._ListOfOutputSynaptics.Add(synapticConnection);
targetNeuron._ListOfInputSynaptics.Add(synapticConnection);
_ListOfSynapticConnections.Add(synapticConnection);
_SourceTargetNeuronSynapticDictionary.Add(sourceNeuron.ID.ToString() + "-" + targetNeuron.ID.ToString(), synapticConnection);
}
progress = ((float)(sourceNeuronIndex + 1) / (float)(totalSourceNeurons)) * 100;
RaiseTaskProgressEvent(this, progress, "");
}
#endregion Input - First Hidden Synapyic Connection Creation Code
#region Hidden to Hidden Synapyic Connection Creation Code
totalSourceLayer = _ListOfLayers.Count - 3;
totalTargetLayer = _ListOfLayers.Count - 1;
for (sourceLayerIndex = 1; sourceLayerIndex <= totalSourceLayer; sourceLayerIndex++)
{
sourceLayer = _ListOfLayers[sourceLayerIndex];
//for (targetLayerIndex = sourceLayerIndex + 1; targetLayerIndex < totalTargetLayer; targetLayerIndex++)
//{
targetLayerIndex = sourceLayerIndex + 1;
RaiseTaskProgressEvent(this, 0.00f, "Creating Synaptic Connection Between Hidden Layer No.: " + sourceLayerIndex.ToString() + " - Hidden Layer No.: " + targetLayerIndex.ToString());
targetLayer = _ListOfLayers[targetLayerIndex];
totalSourceNeurons = sourceLayer._ListOfNeurons.Count;
totalTargetNeurons = targetLayer._ListOfNeurons.Count;
for (sourceNeuronIndex = 0; sourceNeuronIndex < totalSourceNeurons; sourceNeuronIndex++)
{
sourceNeuron = sourceLayer._ListOfNeurons[sourceNeuronIndex];
for (targetNeuronIndex = 0; targetNeuronIndex < totalTargetNeurons; targetNeuronIndex++)
{
weight = (float)rnd.NextDouble();
if (((float)rndSign.NextDouble()) < 0.5)
{
weight = weight * -1;
}
targetNeuron = targetLayer._ListOfNeurons[targetNeuronIndex];
synapticid++;
synapticConnection = new SynapticConnection(synapticid, sourceNeuron, targetNeuron, weight);
sourceNeuron._ListOfOutputSynaptics.Add(synapticConnection);
targetNeuron._ListOfInputSynaptics.Add(synapticConnection);
_ListOfSynapticConnections.Add(synapticConnection);
_SourceTargetNeuronSynapticDictionary.Add(sourceNeuron.ID.ToString() + "-" + targetNeuron.ID.ToString(), synapticConnection);
}
}
//}
progress = ((float)(sourceLayerIndex) / (float)(totalSourceLayer)) * 100;
RaiseTaskProgressEvent(this, progress, "");
}
#endregion Hidden to Hidden Synapyic Connection Creation Code
#region Hidden to Output Synapyic Connection Creation Code
RaiseTaskProgressEvent(this, 0.00f, "Creating Synaptic Connection Between Last Hidden Layer and Output Layer");
sourceLayer = _ListOfLayers[_ListOfLayers.Count - 2];
targetLayer = _OutputLayer;
totalSourceNeurons = sourceLayer._ListOfNeurons.Count;
totalTargetNeurons = targetLayer._ListOfNeurons.Count;
for (sourceNeuronIndex = 0; sourceNeuronIndex < totalSourceNeurons; sourceNeuronIndex++)
{
sourceNeuron = sourceLayer._ListOfNeurons[sourceNeuronIndex];
for (targetNeuronIndex = 0; targetNeuronIndex < totalTargetNeurons; targetNeuronIndex++)
{
weight = (float)rnd.NextDouble();
if (((float)rndSign.NextDouble()) < 0.5)
{
weight = weight * -1;
}
targetNeuron = targetLayer._ListOfNeurons[targetNeuronIndex];
synapticid++;
synapticConnection = new SynapticConnection(synapticid, sourceNeuron, targetNeuron, weight);
sourceNeuron._ListOfOutputSynaptics.Add(synapticConnection);
targetNeuron._ListOfInputSynaptics.Add(synapticConnection);
_ListOfSynapticConnections.Add(synapticConnection);
_SourceTargetNeuronSynapticDictionary.Add(sourceNeuron.ID.ToString() + "-" + targetNeuron.ID.ToString(), synapticConnection);
}
progress = ((float)(sourceNeuronIndex + 1) / (float)(totalSourceNeurons)) * 100;
RaiseTaskProgressEvent(this, progress, "");
}
#endregion Hidden to Output Synapyic Connection Creation Code
RaiseTaskProgressEvent(this, 0.00f, "Total Synaptic Connections in Network (Network ID = " + ID.ToString() + ") are = " + _ListOfSynapticConnections.Count.ToString());
RaiseTaskCompleted(this, null, "Topology Construction Completed...");
}
public void Run()
{
if (TrainingDataTable == null)
{
throw new Exception("Training Engine (ID = " + ID.ToString() + " cannot start training because TrainingDataTable is Empty/NULL/Invalid");
}
_MCCCalculator.Reset();
_TSSE = 0.00f;
_RMSE = 0.00f;
for (patternIndex = 0; patternIndex < InputPatternCount; patternIndex++)
{
//Feed Farword
patternRow = _TrainingData.Rows[patternIndex];
_RuntimeEngine.PatternRow = patternRow;
y = _RuntimeEngine.Run();
//Feed Backword
//OutputLayer:- dpj=(Tpj-Ypj) Ypj (1-Ypj)
t = float.Parse(patternRow[_OutputAttribute].ToString());
_MCCCalculator.Update(t, y);
error = ((t - y) * y * (1 - y));
neuron = _NeuralNetwork._OutputLayer._ListOfNeurons[0];
_NeuronErrorDictionary[neuron.ID] = error;
//Compute TSSE and RMSE
_TSSE = _TSSE + ((t - y) * (t - y)) / 2;
_RMSE = (float)Math.Sqrt((2 * _TSSE) / InputPatternCount);
//HiddenLayer:- dpj= Ypj (1-Ypj) Sum(&pk Wkj); where &pk is the error post synaptic neuron k
totalLayers = _NeuralNetwork._ListOfLayers.Count;
for (hiddenLayerIndex = totalLayers - 2; hiddenLayerIndex > 0; hiddenLayerIndex--)
{
layer = _NeuralNetwork._ListOfLayers[hiddenLayerIndex];
totalNeurons = layer._ListOfNeurons.Count;
for (neuronIndex = 0; neuronIndex < totalNeurons; neuronIndex++)
{
neuron = layer._ListOfNeurons[neuronIndex];
y = neuron._Value;
postSynapticErrorSignal = 0;
postLayer = _NeuralNetwork._ListOfLayers[hiddenLayerIndex + 1];
for (postSynapticNeuronIndex = 0; postSynapticNeuronIndex < postLayer._ListOfNeurons.Count; postSynapticNeuronIndex++)
{
postSynapticNeuron = postLayer._ListOfNeurons[postSynapticNeuronIndex];
postSynapticNeuronError = _NeuronErrorDictionary[postSynapticNeuron.ID];
postSynapticConnection = _NeuralNetwork._SourceTargetNeuronSynapticDictionary[neuron.ID.ToString() + "-" + postSynapticNeuron.ID.ToString()];
postSynapticErrorSignal = postSynapticErrorSignal + (postSynapticNeuronError * postSynapticConnection._Weight);
}
error = y * (1 - y) * postSynapticErrorSignal;
_NeuronErrorDictionary[neuron.ID] = error;
}
}
//Change Weights
for (synapticIndex = 0; synapticIndex < _NeuralNetwork._ListOfSynapticConnections.Count; synapticIndex++)
{
synaptic = _NeuralNetwork._ListOfSynapticConnections[synapticIndex];
deltaWeight = synaptic._SourceNeuron._Value * _NeuronErrorDictionary[synaptic._TargetNeuron.ID] * _NeuralNetwork._LearningRate;
synaptic._Weight = synaptic._Weight + deltaWeight + (_Momentum * _PreviousWeightDictionary[synaptic.ID]);
_PreviousWeightDictionary[synaptic.ID] = deltaWeight;
}
//Progress report
// _NeuralNetwork.RaiseTaskProgressEvent(this ,((((float)(patternIndex + 1)) / ((float)(InputPatternCount))) * 100) ,"Training Engine Learning Pattern No: " + patternIndex.ToString() + " of " + InputPatternCount.ToString());
}
}
