/// <summary>
/// Construct a back propagation trainer.
/// </summary>
/// <param name="theNetwork">The network to train.</param>
/// <param name="theTraining">The training data to use. The coefficient for how much of the gradient is applied to each weight.</param>
/// <param name="theLearningRate">The learning rate. The coefficient for how much of the previous delta is applied to each weight.</param>
/// <param name="theMomentum">The momentum.</param>
public FreeformBackPropagation(FreeformNetwork theNetwork,
IMLDataSet theTraining, double theLearningRate,
double theMomentum)
: base(theNetwork, theTraining)
{
theNetwork.TempTrainingAllocate(1, 2);
_learningRate = theLearningRate;
_momentum = theMomentum;
}
/// <summary>
/// Construct a back propagation trainer.
/// </summary>
/// <param name="theNetwork">The network to train.</param>
/// <param name="theTraining">The training data to use. The coefficient for how much of the gradient is applied to each weight.</param>
/// <param name="theLearningRate">The learning rate. The coefficient for how much of the previous delta is applied to each weight.</param>
/// <param name="theMomentum">The momentum.</param>
public FreeformBackPropagation(FreeformNetwork theNetwork,
IMLDataSet theTraining, double theLearningRate,
double theMomentum)
: base(theNetwork, theTraining)
{
theNetwork.TempTrainingAllocate(1, 2);
_learningRate = theLearningRate;
_momentum = theMomentum;
}
/// <summary>
/// Construct the RPROP trainer.
/// </summary>
/// <param name="theNetwork">The network to train.</param>
/// <param name="theTraining">The training set.</param>
/// <param name="initialUpdate">The initial update.</param>
/// <param name="theMaxStep">The max step.</param>
public FreeformResilientPropagation(FreeformNetwork theNetwork,
IMLDataSet theTraining, double initialUpdate,
double theMaxStep)
: base(theNetwork, theTraining)
{
_maxStep = theMaxStep;
theNetwork.TempTrainingAllocate(1, 4);
theNetwork.PerformConnectionTask(c => c.SetTempTraining(TempUpdate,
initialUpdate));
}
/// <summary>
/// Construct the RPROP trainer.
/// </summary>
/// <param name="theNetwork">The network to train.</param>
/// <param name="theTraining">The training set.</param>
/// <param name="initialUpdate">The initial update.</param>
/// <param name="theMaxStep">The max step.</param>
public FreeformResilientPropagation(FreeformNetwork theNetwork,
IMLDataSet theTraining, double initialUpdate,
double theMaxStep)
: base(theNetwork, theTraining)
{
_maxStep = theMaxStep;
theNetwork.TempTrainingAllocate(1, 4);
theNetwork.PerformConnectionTask(c => c.SetTempTraining(TempUpdate,
initialUpdate));
}