public void Train(Network network, TrainingCallback callback)
{
IActivationFunction activationFunctionInput = network.GetActivation(0);
int outputNeurons = network.GetLayerNeuronCount(network.LayerCount - 1);
double error = 0;
callback.Invoke(TrainingStatus.FillingStandardInputs, 0, 0, 0); /*First operation is filling standard input/outputs*/
Dictionary<int, List<BasicMLData>> trackIdFingerprints = GetNormalizedTrackFingerprints(activationFunctionInput, trainingSongSnippets, outputNeurons);
workingThread = Thread.CurrentThread;
IActivationFunction activationFunctionOutput = network.GetActivation(network.LayerCount - 1);
double[][] normalizedBinaryCodes = GetNormalizedBinaryCodes(activationFunctionOutput, outputNeurons);
Tuple<double[][], double[][]> tuple = FillStandardInputsOutputs(trackIdFingerprints, normalizedBinaryCodes); /*Fill standard input output*/
double[][] inputs = tuple.Item1;
double[][] outputs = tuple.Item2;
if (inputs == null || outputs == null)
{
callback.Invoke(TrainingStatus.Exception, 0, 0, 0);
return;
}
int currentIterration = 0;
double correctOutputs = 0.0;
BasicNeuralDataSet dataset = new BasicNeuralDataSet(inputs, outputs);
ITrain learner = new ResilientPropagation(network, dataset);
try
{
// Dynamic output reordering cycle
/*Idyn = 50*/
for (int i = 0; i < Idyn; i++)
{
if (paused)
{
pauseSem.WaitOne();
}
correctOutputs = NetworkPerformanceMeter.MeasurePerformance(network, dataset);
callback.Invoke(TrainingStatus.OutputReordering, correctOutputs, error, currentIterration);
ReorderOutput(network, dataset, trackIdFingerprints, normalizedBinaryCodes);
/*Edyn = 10*/
for (int j = 0; j < Edyn; j++)
{
if (paused)
{
pauseSem.WaitOne();
}
correctOutputs = NetworkPerformanceMeter.MeasurePerformance(network, dataset);
callback.Invoke(TrainingStatus.RunningDynamicEpoch, correctOutputs, error, currentIterration);
learner.Iteration();
error = learner.Error;
currentIterration++;
}
}
for (int i = 0; i < Efixed; i++)
{
if (paused)
{
pauseSem.WaitOne();
}
correctOutputs = NetworkPerformanceMeter.MeasurePerformance(network, dataset);
callback.Invoke(TrainingStatus.FixedTraining, correctOutputs, error, currentIterration);
learner.Iteration();
error = learner.Error;
currentIterration++;
}
network.ComputeMedianResponses(inputs, trainingSongSnippets);
callback.Invoke(TrainingStatus.Finished, correctOutputs, error, currentIterration);
}
catch (ThreadAbortException)
{
callback.Invoke(TrainingStatus.Aborted, correctOutputs, error, currentIterration);
paused = false;
}
}
public void Train(Network network, TrainingCallback callback)
{
IActivationFunction activationFunctionInput = network.GetActivation(0);
int outputNeurons = network.GetLayerNeuronCount(network.LayerCount - 1);
double error = 0;
callback.Invoke(TrainingStatus.FillingStandardInputs, 0, 0, 0); /*First operation is filling standard input/outputs*/
Dictionary <int, List <BasicMLData> > trackIdFingerprints = GetNormalizedTrackFingerprints(activationFunctionInput, trainingSongSnippets, outputNeurons);
workingThread = Thread.CurrentThread;
IActivationFunction activationFunctionOutput = network.GetActivation(network.LayerCount - 1);
double[][] normalizedBinaryCodes = GetNormalizedBinaryCodes(activationFunctionOutput, outputNeurons);
Tuple <double[][], double[][]> tuple = FillStandardInputsOutputs(trackIdFingerprints, normalizedBinaryCodes); /*Fill standard input output*/
double[][] inputs = tuple.Item1;
double[][] outputs = tuple.Item2;
if (inputs == null || outputs == null)
{
callback.Invoke(TrainingStatus.Exception, 0, 0, 0);
return;
}
int currentIterration = 0;
double correctOutputs = 0.0;
BasicNeuralDataSet dataset = new BasicNeuralDataSet(inputs, outputs);
ITrain learner = new ResilientPropagation(network, dataset);
try
{
// Dynamic output reordering cycle
/*Idyn = 50*/
for (int i = 0; i < Idyn; i++)
{
if (paused)
{
pauseSem.WaitOne();
}
correctOutputs = NetworkPerformanceMeter.MeasurePerformance(network, dataset);
callback.Invoke(TrainingStatus.OutputReordering, correctOutputs, error, currentIterration);
ReorderOutput(network, dataset, trackIdFingerprints, normalizedBinaryCodes);
/*Edyn = 10*/
for (int j = 0; j < Edyn; j++)
{
if (paused)
{
pauseSem.WaitOne();
}
correctOutputs = NetworkPerformanceMeter.MeasurePerformance(network, dataset);
callback.Invoke(TrainingStatus.RunningDynamicEpoch, correctOutputs, error, currentIterration);
learner.Iteration();
error = learner.Error;
currentIterration++;
}
}
for (int i = 0; i < Efixed; i++)
{
if (paused)
{
pauseSem.WaitOne();
}
correctOutputs = NetworkPerformanceMeter.MeasurePerformance(network, dataset);
callback.Invoke(TrainingStatus.FixedTraining, correctOutputs, error, currentIterration);
learner.Iteration();
error = learner.Error;
currentIterration++;
}
network.ComputeMedianResponses(inputs, trainingSongSnippets);
callback.Invoke(TrainingStatus.Finished, correctOutputs, error, currentIterration);
}
catch (ThreadAbortException)
{
callback.Invoke(TrainingStatus.Aborted, correctOutputs, error, currentIterration);
paused = false;
}
}
