private List <EducationLayer> GetEducationNetwork(NeuralNetworkObj neuralNetworkObj)
{
if (this._educationNetwork != null)
{
return(this._educationNetwork);
}
List <EducationLayer> result = new List <EducationLayer>();
foreach (Layer layer in neuralNetworkObj.GetListLayers())
{
EducationLayer educationLayer = new EducationLayer(layer.Id);
foreach (NeuronObj neuron in layer.GetListNeurons())
{
educationLayer.AddNeuron(new EducationNeuron(neuron));
}
result.Add(educationLayer);
}
for (int educationLayerId = 0; educationLayerId < result.Count - 1; educationLayerId++)
{
EducationLayer educationLayer = result[educationLayerId];
EducationLayer nextEducationLayer = result[educationLayerId + 1];
foreach (EducationNeuron educationNeuron in educationLayer.GetListNeurons())
{
foreach (EducationNeuron nextLayerEducationNeuron in nextEducationLayer.GetListNeurons())
{
Synapse synapse = this.GetSynapse(
neuralNetworkObj,
educationLayer.Id,
educationNeuron.NeuronObj.Id,
nextLayerEducationNeuron.NeuronObj.Id
);
if (synapse == null)
{
throw new Exception("Something goes wrong with id layers.");
}
educationNeuron.AddEducationSynapse(new EducationSynapse(nextLayerEducationNeuron, synapse));
}
}
}
this._educationNetwork = result;
return(this._educationNetwork);
}
public NeuralNetworkObj GetTaughtNeuralNetwork(NeuralNetworkObj neuralNetworkObj, double[] inputVector, double[] expectedResultVector)
{
double[] resultVector = this.GetResultVector(neuralNetworkObj, inputVector);
List <EducationLayer> educationNetwork = this.GetEducationNetwork(neuralNetworkObj);
for (int educationLayerId = educationNetwork.Count - 1; educationLayerId >= 0; educationLayerId--)
{
EducationLayer educationLayer = educationNetwork[educationLayerId];
foreach (EducationNeuron educationNeuron in educationLayer.GetListNeurons())
{
NeuronObj neuron = educationNeuron.NeuronObj;
if (neuron.IsInLastLayer())
{
//deltaWeight0 = (OUT(ideal) - OUT(actual)) * f'(IN)
educationNeuron.WeightDelta = (expectedResultVector[neuron.Id] - resultVector[neuron.Id]) * activationFunc.GetDerivativeValue(neuron.GetInputData());
}
else
{
//deltaWeightH = f'(IN) * sum(Wi * delataWeighti)
double sumWeightDeltaInNextLayer = 0;
foreach (EducationSynapse educationSynapse in educationNeuron.GetEducationSynapses())
{
sumWeightDeltaInNextLayer += educationSynapse.Synapse.Weight * educationSynapse.EducationNeuron.WeightDelta;
}
educationNeuron.WeightDelta = this.activationFunc.GetDerivativeValue(neuron.GetInputData()) * sumWeightDeltaInNextLayer;
foreach (EducationSynapse educationSynapse in educationNeuron.GetEducationSynapses())
{
//grad(A, B) = deltaWeight(B) * OUT(A)
double grad = educationSynapse.EducationNeuron.WeightDelta * educationNeuron.NeuronObj.GetOutputData();
educationSynapse.Synapse.Weight += this.learningRate * grad;//todo moment a*deltaWi
}
}
}
}
return(neuralNetworkObj);
}
