// Backpropagate
public void BackPropagate(double learningRate, double error,
SecondLayerNeuron output)
{
// Obtain the sensitivity
double sensitivity = Sensitivity(error, output);
// Calculate the adjustments
double[] adjustments = new double[inputs.Count + 1];
// For the weights Dwjk = lr * sensitivity * input
for (int i = 0; i < inputs.Count; i++)
{
adjustments[i] = learningRate * sensitivity * inputs[i].Output;
}
// For the bias is the same, but the input is always 1
adjustments[inputs.Count] = learningRate * sensitivity;
// Apply adjustments to the weights for the inputs
for (int i = 0; i < inputs.Count; i++)
{
weights[i] += adjustments[i];
}
// Apply the adjustment to the bias
bias += adjustments[inputs.Count];
}
// Contructor
public SimpleNeuralNet1(
int numberOfInputs,
int numberOfHiddenNodes,
double learningRate,
int maxRepetitions)
{
this.learningRate = learningRate;
this.maxRepetitions = maxRepetitions;
// Initialize the inputs, set the value to 0
inputs = new List <Neuron>(numberOfInputs);
for (int i = 0; i < numberOfInputs; i++)
{
inputs.Insert(i, new InputNeuron(0));
}
// Initialize the hidden nodes, set the bias to 0
hiddenLayer = new List <Neuron>(numberOfHiddenNodes);
for (int i = 0; i < numberOfHiddenNodes; i++)
{
hiddenLayer.Insert(i, new FirstLayerNeuron(inputs, 0));
}
// Initialize the second layer
secondLayer = new SecondLayerNeuron(hiddenLayer, 0);
}
// Computes the sensitivity of the unit
private double Sensitivity(double error, SecondLayerNeuron output)
{
// The fprime times the sum of the weighed sensitivities of outputs
// fprime(net) * sum(wji * sensitivity_k)
double outputSensitivity = output.Sensitivity(error);
int indexOfThisNeuron = output.Inputs.IndexOf(this);
double weightOfThisNeuron = output.Weights[indexOfThisNeuron];
return(fprime(sum) * outputSensitivity * weightOfThisNeuron);
}
