protected override void LearnSample(Entrenamiento trainingSample, int currentIteration, int trainingEpochs)
{
// No validation here
int layerCount = layers.Count;
// Set input vector
inputLayer.SetInput(trainingSample.InputVector);
for (int i = 0; i < layerCount; i++)
{
layers[i].Run();
}
// Set Errors
meanSquaredError += (outputLayer as ActivationLayer).SetErrors(trainingSample.OutputVector);
// Backpropagate errors
for (int i = layerCount; i > 0;)
{
ActivationLayer layer = layers[--i] as ActivationLayer;
if (layer != null)
{
layer.EvaluateErrors();
}
}
// Optimize synapse weights and neuron bias values
for (int i = 0; i < layerCount; i++)
{
layers[i].Learn(currentIteration, trainingEpochs);
}
}
public ActivationNeuron(ActivationLayer parent)
{
Helper.ValidateNotNull(parent, "parent");
this.input = 0d;
this.output = 0d;
this.error = 0d;
this.bias = 0d;
this.parent = parent;
}
public RedBackpropagation(ActivationLayer inputLayer, ActivationLayer outputLayer)
: base(inputLayer, outputLayer, TrainingMethod.Supervised)
{
this.meanSquaredError = 0d;
this.isValidMSE = false;
}
