public double CalculateGradient(double?target = null)
{
if (target == null)
{
return(Gradient = OutputSynapses.Sum(a => a.OutputNeuron.Gradient * a.Weight) * Sigmoid.Derivative(Value));
}
return(Gradient = CalculateError(target.Value) * Sigmoid.Derivative(Value));
}
public float CalculateGradient(float?target = null)
{
if (target == null)
{
return(Gradient = OutputSynapses.Sum(x => x.OutputNeuton.Gradient * x.Weight) * Sigmoid.Derivative(Value));
}
return(Gradient = CalculateError(target.Value) * Sigmoid.Derivative(Value));
}
private void BackPropagation(double target)
{
OutputNeuron.Error =
Sigmoid.Derivative(OutputNeuron.Output) *
(target - OutputNeuron.Output);
OutputNeuron.AdjustWeights();
for (int h = 0; h < HiddenLayers; h++)
{
HiddenNeurons[h, 0].Error =
Sigmoid.Derivative(HiddenNeurons[h, 0].Output) *
OutputNeuron.Error *
OutputNeuron.Weights[0];
HiddenNeurons[h, 1].Error =
Sigmoid.Derivative(HiddenNeurons[h, 1].Output) *
OutputNeuron.Error *
OutputNeuron.Weights[1];
HiddenNeurons[h, 0].AdjustWeights();
HiddenNeurons[h, 1].AdjustWeights();
}
}
/// <summary>
/// Updates the error of the output neuron based on some desired output and a sigmoid activation function.
/// </summary>
/// <param name="activation">The activation with which the output will be calculated/</param>
/// <param name="desired">The desired output of this neuron for a given training set.</param>
public override void UpdateError(Sigmoid activation, double desired)
{
Error = (this.Output - desired) * activation.Derivative(this.Net);
}
/// <summary>
/// Updates the error of the neuron based on some activation function and some error coefficient (subject to change in Output Neurons).
/// </summary>
/// <param name="activation">The activation function with which the error will be calculated.</param>
/// <param name="errorCoefficient">The standard coefficient of error for neurons.
/// SUM (for I in Posterior Neurons) Error_i * W_ij. Where j is this neuron.</param>
/// <returns>The neural error of the neuron.</returns>
public virtual void UpdateError(Sigmoid activation, double errorCoefficient)
{
Error = activation.Derivative(Net) * errorCoefficient;
}