/// <summary>
///
/// </summary>
/// <param name="inputs"></param>
/// <returns></returns>
public double[] Compute(double[] inputs)
{
// local variable to avoid mutlithread conflicts
double[] output = new double[NbNeurons];
// compute each neuron
for (int i = 0; i < Neurons.Length; i++)
{
output[i] = Neurons[i].Compute(inputs);
}
// assign output property as well (works correctly for single threaded usage)
if (Equation is SoftMaxActivation)
{
(Equation as SoftMaxActivation).CalculerDiviseur(output);
//on divise par la somme calculer
for (int i = 0; i < Neurons.Length; i++)
{
output[i] = Neurons[i].SetOutput(output[i] / (Equation as SoftMaxActivation).Diviseur);
}
//pour reset le diviseur
Equation = new SoftMaxActivation();
}
this.Output = output;
return(output);
}
/// <summary>
///
/// </summary>
/// <param name="precedent"></param>
/// <returns></returns>
public double[] Compute(Layer precedent)
{
//pareil que lautre mais on peut envoyer un layer
double[] output = new double[NbNeurons];
for (int i = 0; i < Neurons.Length; i++)
{
output[i] = Neurons[i].Compute(precedent.Output);
}
if (Equation is SoftMaxActivation)
{
(Equation as SoftMaxActivation).CalculerDiviseur(output);
//on divise par la somme calculer
for (int i = 0; i < Neurons.Length; i++)
{
output[i] = Neurons[i].SetOutput(output[i] / (Equation as SoftMaxActivation).Diviseur);
}
//pour reset le diviseur
Equation = new SoftMaxActivation();
}
this.Output = output;
return(output);
}
