/// <summary>
/// Creates a new layer
/// </summary>
/// <param name="neuronCount">
/// Number of neurons in the layer
/// </param>
/// <exception cref="ArgumentException">
/// If <c>neuronCount</c> is not positive
/// </exception>
protected Layer(int neuronCount)
{
Helper.ValidatePositive(neuronCount, "neuronCount");
this.neurons = new TNeuron[neuronCount];
this.learningRateFunction = new LinearFunction(0.3d, 0.05d);
}
/// <summary>
/// Trains the neural network for the given training set (Batch Training)
/// </summary>
/// <param name="trainingSet">
/// The training set to use
/// </param>
/// <param name="trainingEpochs">
/// Number of training epochs. (All samples are trained in some random order, in every
/// training epoch)
/// </param>
/// <exception cref="ArgumentNullException">
/// if <c>trainingSet</c> is <c>null</c>
/// </exception>
/// <exception cref="ArgumentException">
/// if <c>trainingEpochs</c> is zero or negative
/// </exception>
public virtual void Learn(Ajusteentrenamiento trainingSet, int trainingEpochs)
{
// Validate
Helper.ValidateNotNull(trainingSet, "trainingSet");
Helper.ValidatePositive(trainingEpochs, "trainingEpochs");
if ((trainingSet.InputVectorLength != inputLayer.NeuronCount) ||
(trainingMethod == TrainingMethod.Supervised && trainingSet.OutputVectorLength != outputLayer.NeuronCount) ||
(trainingMethod == TrainingMethod.Unsupervised && trainingSet.OutputVectorLength != 0))
{
throw new ArgumentException("Invalid training set");
}
// Reset isStopping
isStopping = false;
// Re-Initialize the network
Initialize();
for (int currentIteration = 0; currentIteration < trainingEpochs; currentIteration++)
{
int[] randomOrder = Helper.GetRandomOrder(trainingSet.TrainingSampleCount);
// Beginning a new training epoch
OnBeginEpoch(currentIteration, trainingSet);
// Check for Jitter Epoch
if (jitterEpoch > 0 && currentIteration % jitterEpoch == 0)
{
for (int i = 0; i < connectors.Count; i++)
{
connectors[i].Jitter(jitterNoiseLimit);
}
}
for (int index = 0; index < trainingSet.TrainingSampleCount; index++)
{
Entrenamiento randomSample = trainingSet[randomOrder[index]];
// Learn a random training sample
OnBeginSample(currentIteration, randomSample);
LearnSample(trainingSet[randomOrder[index]], currentIteration, trainingEpochs);
OnEndSample(currentIteration, randomSample);
// Check if we need to stop
if (isStopping)
{
isStopping = false; return;
}
}
// Training Epoch successfully complete
OnEndEpoch(currentIteration, trainingSet);
// Check if we need to stop
if (isStopping)
{
isStopping = false; return;
}
}
}
/// <summary>
/// Trains the network for the given training sample (Online training mode). Note that this
/// method trains the sample only once, irrespective of what current epoch is. The arguments
/// are just used to find out training progress and adjust parameters depending on it.
/// </summary>
/// <param name="trainingSample">
/// Training sample to use
/// </param>
/// <param name="currentIteration">
/// Current training iteration
/// </param>
/// <param name="trainingEpochs">
/// Number of training epochs
/// </param>
/// <exception cref="ArgumentNullException">
/// If <c>trainingSample</c> is <c>null</c>
/// </exception>
/// <exception cref="ArgumentException">
/// If <c>trainingEpochs</c> is not positive
/// </exception>
/// <exception cref="ArgumentOutOfRangeException">
/// If <c>currentIteration</c> is negative or, if it is not less than <c>trainingEpochs</c>
/// </exception>
public virtual void Learn(Entrenamiento trainingSample, int currentIteration, int trainingEpochs)
{
Helper.ValidateNotNull(trainingSample, "trainingSample");
Helper.ValidatePositive(trainingEpochs, "trainingEpochs");
Helper.ValidateWithinRange(currentIteration, 0, trainingEpochs - 1, "currentIteration");
OnBeginSample(currentIteration, trainingSample);
LearnSample(trainingSample, currentIteration, trainingEpochs);
OnEndSample(currentIteration, trainingSample);
}
public Ajusteentrenamiento(int inputVectorLength, int outputVectorLength)
{
// Validation
Helper.ValidatePositive(inputVectorLength, "inputVectorLength");
Helper.ValidateNotNegative(outputVectorLength, "outputVectorLength");
// Initialize instance variables
this.inputVectorLength = inputVectorLength;
this.outputVectorLength = outputVectorLength;
this.trainingSamples = new List <Entrenamiento>();
}
