/// <summary>
/// Calculate the gradient value of this neuron, by multiply the error with the derivative
/// </summary>
/// <param name="target"></param>
/// <returns></returns>
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));
}
/// <summary>
/// Calculates and updates the current value stored in this neuron, by taking the output value of input neurons mutiplied by their weight, plus a bias
/// </summary>
/// <returns>the calculated value</returns>
public virtual double CalculateValue()
{
return(Value = Sigmoid.Output(InputSynapses.Sum(a => a.Weight * a.InputNeuron.Value) + Bias));
}
