/// <summary>
/// A protected helper function used to train single learning sample
/// </summary>
/// <param name="trainingSample">
/// Training sample to use
/// </param>
/// <param name="currentIteration">
/// Current training epoch (Assumed to be positive and less than <c>trainingEpochs</c>)
/// </param>
/// <param name="trainingEpochs">
/// Number of training epochs (Assumed to be positive)
/// </param>
protected override void LearnSample(TrainingSample 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);
}
}
/// <summary>
/// Create a new activation neuron
/// </summary>
/// <param name="parent">
/// The parent layer containing this neuron
/// </param>
/// <exception cref="System.ArgumentNullException">
/// If <c>parent</c> is <c>null</c>
/// </exception>
public ActivationNeuron(ActivationLayer parent)
{
Helper.ValidateNotNull(parent, "parent");
this.input = 0d;
this.output = 0d;
this.error = 0d;
this.bias = 0d;
this.parent = parent;
}
/// <summary>
/// Creates a new Back Propagation Network, with the specified input and output layers. (You
/// are required to connect all layers using appropriate synapses, before using the constructor.
/// Any changes made to the structure of the network after its creation may lead to complete
/// malfunctioning)
/// </summary>
/// <param name="inputLayer">
/// The input layer
/// </param>
/// <param name="outputLayer">
/// The output layer
/// </param>
/// <exception cref="ArgumentNullException">
/// If <c>inputLayer</c> or <c>outputLayer</c> is <c>null</c>
/// </exception>
public BackpropagationNetwork(ActivationLayer inputLayer, ActivationLayer outputLayer)
: base(inputLayer, outputLayer, TrainingMethod.Supervised)
{
this.meanSquaredError = 0d;
this.isValidMSE = false;
}