/// <summary>
/// Construct a trainer for flat networks to use the Manhattan update rule.
/// </summary>
///
/// <param name="network">The network to train.</param>
/// <param name="training">The training data to use.</param>
/// <param name="learningRate">The learning rate to use.</param>
public TrainFlatNetworkManhattan(FlatNetwork network,
IEngineDataSet training, double learningRate)
: base(network, training)
{
this.learningRate = learningRate;
this.zeroTolerance = RPROPConst.DEFAULT_ZERO_TOLERANCE;
}
/// <summary>
/// Construct the training object.
/// </summary>
///
/// <param name="network">The network to train.</param>
/// <param name="training">The training data to use.</param>
public TrainFlatNetworkSCG(FlatNetwork network,
IEngineDataSet training)
: base(network, training)
{
this.success = true;
this.success = true;
this.delta = 0;
this.lambda2 = 0;
this.lambda = TrainFlatNetworkSCG.FIRST_LAMBDA;
this.oldError = 0;
this.magP = 0;
this.restart = false;
this.weights = EngineArray.ArrayCopy(network.Weights);
int numWeights = this.weights.Length;
// this.gradients = new double[numWeights];
this.oldWeights = new double[numWeights];
this.oldGradient = new double[numWeights];
this.p = new double[numWeights];
this.r = new double[numWeights];
this.shouldInit = true;
}
/// <summary>
/// Construct a trainer for flat networks to use the Manhattan update rule.
/// </summary>
///
/// <param name="network">The network to train.</param>
/// <param name="training">The training data to use.</param>
/// <param name="learningRate">The learning rate to use.</param>
public TrainFlatNetworkManhattan(FlatNetwork network,
IEngineDataSet training, double learningRate)
: base(network, training)
{
this.learningRate = learningRate;
this.zeroTolerance = RPROPConst.DEFAULT_ZERO_TOLERANCE;
}
/// <summary>
/// Construct a backprop trainer for flat networks.
/// </summary>
///
/// <param name="network">The network to train.</param>
/// <param name="training">The training data.</param>
/// <param name="learningRate">The learning rate.</param>
/// <param name="momentum">The momentum.</param>
public TrainFlatNetworkBackPropagation(FlatNetwork network,
IEngineDataSet training, double learningRate,
double momentum)
: base(network, training)
{
this.momentum = momentum;
this.learningRate = learningRate;
this.lastDelta = new double[network.Weights.Length];
}
/// <summary>
/// Construct a backprop trainer for flat networks.
/// </summary>
///
/// <param name="network">The network to train.</param>
/// <param name="training">The training data.</param>
/// <param name="learningRate">The learning rate.</param>
/// <param name="momentum">The momentum.</param>
public TrainFlatNetworkBackPropagation(FlatNetwork network,
IEngineDataSet training, double learningRate,
double momentum)
: base(network, training)
{
this.momentum = momentum;
this.learningRate = learningRate;
this.lastDelta = new double[network.Weights.Length];
}
/// <summary>
/// Construct a resilient trainer for flat networks.
/// </summary>
///
/// <param name="network">The network to train.</param>
/// <param name="training">The training data to use.</param>
/// <param name="zeroTolerance">How close a number should be to zero to be counted as zero.</param>
/// <param name="initialUpdate">The initial update value.</param>
/// <param name="maxStep">The maximum step value.</param>
public TrainFlatNetworkResilient(FlatNetwork network,
IEngineDataSet training, double zeroTolerance,
double initialUpdate, double maxStep)
: base(network, training)
{
this.updateValues = new double[network.Weights.Length];
this.zeroTolerance = zeroTolerance;
this.maxStep = maxStep;
for (int i = 0; i < this.updateValues.Length; i++)
{
this.updateValues[i] = initialUpdate;
}
}
/// <summary>
/// Construct a resilient trainer for flat networks.
/// </summary>
///
/// <param name="network">The network to train.</param>
/// <param name="training">The training data to use.</param>
/// <param name="zeroTolerance">How close a number should be to zero to be counted as zero.</param>
/// <param name="initialUpdate">The initial update value.</param>
/// <param name="maxStep">The maximum step value.</param>
public TrainFlatNetworkResilient(FlatNetwork network,
IEngineDataSet training, double zeroTolerance,
double initialUpdate, double maxStep)
: base(network, training)
{
this.updateValues = new double[network.Weights.Length];
this.zeroTolerance = zeroTolerance;
this.maxStep = maxStep;
for (int i = 0; i < this.updateValues.Length; i++)
{
this.updateValues[i] = initialUpdate;
}
}
/// <summary>
/// Train a flat network multithreaded.
/// </summary>
///
/// <param name="network">The network to train.</param>
/// <param name="training">The training data to use.</param>
public TrainFlatNetworkProp(FlatNetwork network,
IEngineDataSet training)
{
if (!(training is IEngineIndexableSet))
{
throw new EncogEngineError(
"Training data must be Indexable for this training type.");
}
this.training = training;
this.network = network;
this.gradients = new double[this.network.Weights.Length];
this.lastGradient = new double[this.network.Weights.Length];
this.indexable = (IEngineIndexableSet)training;
this.numThreads = 0;
this.reportedException = null;
}
/// <summary>
/// Train a flat network multithreaded.
/// </summary>
///
/// <param name="network">The network to train.</param>
/// <param name="training">The training data to use.</param>
/// <param name="profile">The OpenCL training profile.</param>
public TrainFlatNetworkOpenCL(FlatNetwork network,
IEngineDataSet training, OpenCLTrainingProfile profile)
{
(new ValidateForOpenCL()).Validate(network);
if (!(training is IEngineIndexableSet))
{
throw new EncogEngineError(
"Training data must be Indexable for this training type.");
}
if (EncogEngine.Instance.CL == null)
{
throw new EncogEngineError(
"You must enable OpenCL before using this training type.");
}
this.profile = profile;
this.network = network;
this.training = (IEngineIndexableSet)training;
}
/// <summary>
/// Construct the training object.
/// </summary>
///
/// <param name="network">The network to train.</param>
/// <param name="training">The training data to use.</param>
public TrainFlatNetworkSCG(FlatNetwork network,
IEngineDataSet training)
: base(network, training)
{
this.success = true;
this.success = true;
this.delta = 0;
this.lambda2 = 0;
this.lambda = TrainFlatNetworkSCG.FIRST_LAMBDA;
this.oldError = 0;
this.magP = 0;
this.restart = false;
this.weights = EngineArray.ArrayCopy(network.Weights);
int numWeights = this.weights.Length;
// this.gradients = new double[numWeights];
this.oldWeights = new double[numWeights];
this.oldGradient = new double[numWeights];
this.p = new double[numWeights];
this.r = new double[numWeights];
this.shouldInit = true;
}
/// <summary>
/// Tran a network using RPROP.
/// </summary>
///
/// <param name="flat">The network to train.</param>
/// <param name="trainingSet">The training data to use.</param>
public TrainFlatNetworkResilient(FlatNetwork flat,
IEngineDataSet trainingSet)
: this(flat, trainingSet, RPROPConst.DEFAULT_ZERO_TOLERANCE, RPROPConst.DEFAULT_INITIAL_UPDATE, RPROPConst.DEFAULT_MAX_STEP)
{
}
/// <summary>
/// Tran a network using RPROP.
/// </summary>
///
/// <param name="flat">The network to train.</param>
/// <param name="trainingSet">The training data to use.</param>
public TrainFlatNetworkResilient(FlatNetwork flat,
IEngineDataSet trainingSet)
: this(flat, trainingSet, RPROPConst.DEFAULT_ZERO_TOLERANCE, RPROPConst.DEFAULT_INITIAL_UPDATE, RPROPConst.DEFAULT_MAX_STEP)
{
}
/// <summary>
/// Train a flat network multithreaded.
/// </summary>
///
/// <param name="network">The network to train.</param>
/// <param name="training">The training data to use.</param>
public TrainFlatNetworkProp(FlatNetwork network,
IEngineDataSet training)
{
if (!(training is IEngineIndexableSet))
{
throw new EncogEngineError(
"Training data must be Indexable for this training type.");
}
this.training = training;
this.network = network;
this.gradients = new double[this.network.Weights.Length];
this.lastGradient = new double[this.network.Weights.Length];
this.indexable = (IEngineIndexableSet)training;
this.numThreads = 0;
this.reportedException = null;
}
/// <summary>
/// Train a flat network multithreaded.
/// </summary>
///
/// <param name="network">The network to train.</param>
/// <param name="training">The training data to use.</param>
/// <param name="profile">The OpenCL training profile.</param>
public TrainFlatNetworkOpenCL(FlatNetwork network,
IEngineDataSet training, OpenCLTrainingProfile profile)
{
(new ValidateForOpenCL()).Validate(network);
if (!(training is IEngineIndexableSet))
{
throw new EncogEngineError(
"Training data must be Indexable for this training type.");
}
if (EncogEngine.Instance.CL == null)
{
throw new EncogEngineError(
"You must enable OpenCL before using this training type.");
}
this.profile = profile;
this.network = network;
this.training = (IEngineIndexableSet)training;
}
