public void StartTrainingAsync(Network network, TrainingCallback callback)
{
if (alreadyDisposed)
{
throw new ObjectDisposedException("Object already disposed");
}
Action <Network, TrainingCallback> action = Train;
action.BeginInvoke(network, callback, action.EndInvoke, action);
}
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 StartTrainingAsync(Network network, TrainingCallback callback)
{
if (alreadyDisposed)
{
throw new ObjectDisposedException("Object already disposed");
}
Action<Network, TrainingCallback> action = Train;
action.BeginInvoke(network, callback, action.EndInvoke, action);
}
public Task <Network> Train(NetworkConfiguration networkConfiguration, int[] spectralImagesIndexesToConsider, TrainingCallback callback)
{
var network = networkFactory.Create(
networkConfiguration.ActivationFunction,
DefaultFingerprintSize,
networkConfiguration.HiddenLayerCount,
networkConfiguration.OutputCount);
var spectralImagesToTrain = trainingDataProvider.GetSpectralImagesToTrain(
spectralImagesIndexesToConsider, (int)System.Math.Pow(2, networkConfiguration.OutputCount));
var trainingSet = trainingDataProvider.MapSpectralImagesToBinaryOutputs(
spectralImagesToTrain, networkConfiguration.OutputCount);
normalizeStrategy.NormalizeInputInPlace(networkConfiguration.ActivationFunction, trainingSet.Inputs);
normalizeStrategy.NormalizeOutputInPlace(networkConfiguration.ActivationFunction, trainingSet.Outputs);
return(Task.Factory.StartNew(
() =>
{
var dataset = new BasicNeuralDataSet(trainingSet.Inputs, trainingSet.Outputs);
var learner = new Backpropagation(network, dataset);
double correctOutputs = 0.0;
for (int idynIndex = 0; idynIndex < Idyn; idynIndex++)
{
correctOutputs = networkPerformanceMeter.MeasurePerformance(
network, dataset, networkConfiguration.ActivationFunction);
callback(TrainingStatus.OutputReordering, correctOutputs, learner.Error, idynIndex * Edyn);
var bestPairs = GetBestPairsForReordering(
(int)System.Math.Pow(2, networkConfiguration.OutputCount), network, spectralImagesToTrain, trainingSet);
ReorderOutputsAccordingToBestPairs(bestPairs, trainingSet, dataset);
for (int edynIndex = 0; edynIndex < Edyn; edynIndex++)
{
correctOutputs = networkPerformanceMeter.MeasurePerformance(
network, dataset, networkConfiguration.ActivationFunction);
callback(
TrainingStatus.RunningDynamicEpoch,
correctOutputs,
learner.Error,
(idynIndex * Edyn) + edynIndex);
learner.Iteration();
}
}
for (int efixedIndex = 0; efixedIndex < Efixed; efixedIndex++)
{
correctOutputs = networkPerformanceMeter.MeasurePerformance(
network, dataset, networkConfiguration.ActivationFunction);
callback(
TrainingStatus.FixedTraining, correctOutputs, learner.Error, (Idyn * Edyn) + efixedIndex);
learner.Iteration();
}
network.ComputeMedianResponses(trainingSet.Inputs, TrainingSongSnippets);
callback(TrainingStatus.Finished, correctOutputs, learner.Error, (Idyn * Edyn) + Efixed);
return network;
}));
}
/* Method: SetCallback
Sets the callback function for use during training. The user_data is passed to
the callback. It can point to arbitrary data that the callback might require and
can be NULL if it is not used.
See <FANN::callback_type at http://libfann.github.io/fann/docs/files/fann_data_cpp-h.html#callback_type> for more information about the callback function.
The default callback function simply prints out some status information.
This function appears in FANN >= 2.0.0.
*/
public void SetCallback(TrainingCallback callback, Object userData)
{
Callback = callback;
UserData = userData;
GCHandle handle = GCHandle.Alloc(userData);
UnmanagedCallback = new training_callback(InternalCallback);
fannfloatPINVOKE.neural_net_set_callback(neural_net.getCPtr(this.net), Marshal.GetFunctionPointerForDelegate(UnmanagedCallback), (IntPtr)handle);
}
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;
}
}
