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);
}
public Synapse(NeuronObj neuronObj, double weight)
{
NeuronObj = neuronObj;
Weight = weight;
}
public EducationNeuron(NeuronObj neuronObj)
{
NeuronObj = neuronObj;
}