private static void SampleIris()
{
SigmaEnvironment sigma = SigmaEnvironment.Create("iris");
sigma.SetRandomSeed(0);
sigma.Prepare();
IDataset dataset = Defaults.Datasets.Iris();
ITrainer trainer = sigma.CreateGhostTrainer("iris-trainer");
trainer.Network.Architecture = InputLayer.Construct(4)
+ FullyConnectedLayer.Construct(12)
+ FullyConnectedLayer.Construct(3)
+ OutputLayer.Construct(3)
+ SquaredDifferenceCostLayer.Construct();
//trainer.Network = Serialisation.ReadBinaryFileIfExists("iris.sgnet", trainer.Network);
trainer.TrainingDataIterator = new MinibatchIterator(50, dataset);
trainer.AddNamedDataIterator("validation", new UndividedIterator(dataset));
trainer.Optimiser = new GradientDescentOptimiser(learningRate: 0.06);
trainer.Operator = new CudaSinglethreadedOperator();
trainer.AddInitialiser("*.*", new GaussianInitialiser(standardDeviation: 0.1));
//trainer.AddGlobalHook(new StopTrainingHook(atEpoch: 100));
//trainer.AddLocalHook(new EarlyStopperHook("optimiser.cost_total", 20, target: ExtremaTarget.Min));
trainer.AddLocalHook(new AccumulatedValueReporter("optimiser.cost_total", TimeStep.Every(1, TimeScale.Epoch), reportEpochIteration: true));
//.On(new ExtremaCriteria("optimiser.cost_total", ExtremaTarget.Min)));
//trainer.AddLocalHook(new DiskSaviorHook<INetwork>("network.self", Namers.Dynamic("iris_epoch{0}.sgnet", "epoch"), verbose: true)
// .On(new ExtremaCriteria("optimiser.cost_total", ExtremaTarget.Min)));
trainer.AddHook(new MultiClassificationAccuracyReporter("validation", TimeStep.Every(1, TimeScale.Epoch), tops: 1));
trainer.AddHook(new StopTrainingHook(new ThresholdCriteria("shared.classification_accuracy_top1", ComparisonTarget.GreaterThanEquals, 0.98)));
trainer.AddLocalHook(new RunningTimeReporter(TimeStep.Every(599, TimeScale.Iteration), 128));
trainer.AddLocalHook(new RunningTimeReporter(TimeStep.Every(1, TimeScale.Epoch), 4));
//Serialisation.WriteBinaryFile(trainer, "trainer.sgtrainer");
//trainer = Serialisation.ReadBinaryFile<ITrainer>("trainer.sgtrainer");
sigma.AddTrainer(trainer);
sigma.AddMonitor(new HttpMonitor("http://+:8080/sigma/"));
sigma.PrepareAndRun();
}
private static void SampleMnist()
{
SigmaEnvironment sigma = SigmaEnvironment.Create("mnist");
sigma.SetRandomSeed(0);
IDataset dataset = Defaults.Datasets.Mnist();
ITrainer trainer = sigma.CreateTrainer("mnist-trainer");
trainer.Network = new Network();
trainer.Network.Architecture = InputLayer.Construct(28, 28)
+ DropoutLayer.Construct(0.2)
+ FullyConnectedLayer.Construct(1000, activation: "rel")
+ DropoutLayer.Construct(0.4)
+ FullyConnectedLayer.Construct(800, activation: "rel")
+ DropoutLayer.Construct(0.4)
+ FullyConnectedLayer.Construct(10, activation: "sigmoid")
+ OutputLayer.Construct(10)
+ SoftMaxCrossEntropyCostLayer.Construct();
trainer.TrainingDataIterator = new MinibatchIterator(100, dataset);
trainer.AddNamedDataIterator("validation", new UndividedIterator(Defaults.Datasets.MnistValidation()));
//trainer.Optimiser = new GradientDescentOptimiser(learningRate: 0.01);
//trainer.Optimiser = new MomentumGradientOptimiser(learningRate: 0.01, momentum: 0.9);
trainer.Optimiser = new AdagradOptimiser(baseLearningRate: 0.02);
trainer.Operator = new CudaSinglethreadedOperator();
trainer.AddInitialiser("*.weights", new GaussianInitialiser(standardDeviation: 0.1));
trainer.AddInitialiser("*.bias*", new GaussianInitialiser(standardDeviation: 0.05));
trainer.AddLocalHook(new ValueReporter("optimiser.cost_total", TimeStep.Every(1, TimeScale.Iteration), reportEpochIteration: true)
.On(new ExtremaCriteria("optimiser.cost_total", ExtremaTarget.Min)));
var validationTimeStep = TimeStep.Every(1, TimeScale.Epoch);
trainer.AddHook(new MultiClassificationAccuracyReporter("validation", validationTimeStep, tops: new[] { 1, 2, 3 }));
for (int i = 0; i < 10; i++)
{
trainer.AddGlobalHook(new TargetMaximisationReporter(trainer.Operator.Handler.NDArray(ArrayUtils.OneHot(i, 10), 10), TimeStep.Every(1, TimeScale.Epoch)));
}
trainer.AddLocalHook(new RunningTimeReporter(TimeStep.Every(10, TimeScale.Iteration), 32));
trainer.AddLocalHook(new RunningTimeReporter(TimeStep.Every(1, TimeScale.Epoch), 4));
trainer.AddHook(new StopTrainingHook(atEpoch: 10));
sigma.PrepareAndRun();
}
public static ITrainer CreateTicTacToeTrainer(SigmaEnvironment sigma)
{
IDataset dataset = Defaults.Datasets.TicTacToe();
ITrainer trainer = sigma.CreateTrainer("tictactoe-trainer");
trainer.Network = new Network();
trainer.Network.Architecture = InputLayer.Construct(9)
+ FullyConnectedLayer.Construct(72, "tanh")
+ FullyConnectedLayer.Construct(99, "tanh")
+ FullyConnectedLayer.Construct(3, "tanh")
+ OutputLayer.Construct(3)
+ SoftMaxCrossEntropyCostLayer.Construct();
trainer.TrainingDataIterator = new MinibatchIterator(21, dataset);
trainer.AddNamedDataIterator("validation", new UndividedIterator(dataset));
trainer.Optimiser = new MomentumGradientOptimiser(learningRate: 0.01, momentum: 0.9);
trainer.Operator = new CpuSinglethreadedOperator();
trainer.AddInitialiser("*.*", new GaussianInitialiser(standardDeviation: 0.1));
trainer.AddLocalHook(new AccumulatedValueReporter("optimiser.cost_total", TimeStep.Every(1, TimeScale.Epoch)));
trainer.AddHook(new MultiClassificationAccuracyReporter("validation", TimeStep.Every(1, TimeScale.Epoch), tops: new[] { 1, 2 }));
trainer.AddGlobalHook(new DiskSaviorHook <INetwork>(TimeStep.Every(1, TimeScale.Epoch), "network.self", Namers.Static("tictactoe.sgnet"), verbose: true)
.On(new ExtremaCriteria("shared.classification_accuracy_top1", ExtremaTarget.Max)));
return(trainer);
}
private static ITrainer CreateParkinsonsTrainer(SigmaEnvironment sigma)
{
IDataset dataset = Defaults.Datasets.Parkinsons();
ITrainer trainer = sigma.CreateTrainer("parkinsons-trainer");
trainer.Network = new Network
{
Architecture = InputLayer.Construct(22)
+ FullyConnectedLayer.Construct(140)
+ FullyConnectedLayer.Construct(20)
+ FullyConnectedLayer.Construct(1)
+ OutputLayer.Construct(1)
+ SquaredDifferenceCostLayer.Construct()
};
trainer.TrainingDataIterator = new MinibatchIterator(10, dataset);
trainer.AddNamedDataIterator("validation", new UndividedIterator(dataset));
trainer.Optimiser = new AdagradOptimiser(baseLearningRate: 0.01);
trainer.Operator = new CpuSinglethreadedOperator(new DebugHandler(new CpuFloat32Handler()));
trainer.AddInitialiser("*.*", new GaussianInitialiser(standardDeviation: 0.1));
trainer.AddLocalHook(new AccumulatedValueReporter("optimiser.cost_total", TimeStep.Every(1, TimeScale.Epoch)));
trainer.AddHook(new UniClassificationAccuracyReporter("validation", 0.5, TimeStep.Every(1, TimeScale.Epoch)));
return(trainer);
}
private static ITrainer CreateIrisTrainer(SigmaEnvironment sigma)
{
IDataset dataset = Defaults.Datasets.Iris();
ITrainer trainer = sigma.CreateTrainer("iris-trainer");
trainer.Network = new Network();
trainer.Network.Architecture = InputLayer.Construct(4)
+ FullyConnectedLayer.Construct(12)
+ FullyConnectedLayer.Construct(3)
+ OutputLayer.Construct(3)
+ SquaredDifferenceCostLayer.Construct();
//trainer.Network = Serialisation.ReadBinaryFileIfExists("iris.sgnet", trainer.Network);
trainer.TrainingDataIterator = new MinibatchIterator(50, dataset);
trainer.AddNamedDataIterator("validation", new UndividedIterator(dataset));
trainer.Optimiser = new GradientDescentOptimiser(learningRate: 0.06);
trainer.Operator = new CpuSinglethreadedOperator();
trainer.AddInitialiser("*.*", new GaussianInitialiser(standardDeviation: 0.1));
//trainer.AddGlobalHook(new StopTrainingHook(atEpoch: 100));
//trainer.AddLocalHook(new EarlyStopperHook("optimiser.cost_total", 20, target: ExtremaTarget.Min));
trainer.AddLocalHook(new AccumulatedValueReporter("optimiser.cost_total", TimeStep.Every(1, TimeScale.Epoch), reportEpochIteration: true));
//.On(new ExtremaCriteria("optimiser.cost_total", ExtremaTarget.Min)));
//trainer.AddLocalHook(new DiskSaviorHook<INetwork>("network.self", Namers.Dynamic("iris_epoch{0}.sgnet", "epoch"), verbose: true)
// .On(new ExtremaCriteria("optimiser.cost_total", ExtremaTarget.Min)));
trainer.AddHook(new MultiClassificationAccuracyReporter("validation", TimeStep.Every(1, TimeScale.Epoch), tops: 1));
return(trainer);
}
private static void SampleParkinsons()
{
SigmaEnvironment sigma = SigmaEnvironment.Create("parkinsons");
IDataset dataset = Defaults.Datasets.Parkinsons();
ITrainer trainer = sigma.CreateGhostTrainer("parkinsons-trainer");
trainer.Network.Architecture = InputLayer.Construct(22)
+ FullyConnectedLayer.Construct(140)
+ FullyConnectedLayer.Construct(20)
+ FullyConnectedLayer.Construct(1)
+ OutputLayer.Construct(1)
+ SquaredDifferenceCostLayer.Construct();
trainer.TrainingDataIterator = new MinibatchIterator(10, dataset);
trainer.AddNamedDataIterator("validation", new UndividedIterator(dataset));
trainer.Optimiser = new AdagradOptimiser(baseLearningRate: 0.01);
trainer.AddInitialiser("*.*", new GaussianInitialiser(standardDeviation: 0.1));
trainer.AddLocalHook(new AccumulatedValueReporter("optimiser.cost_total", TimeStep.Every(1, TimeScale.Epoch)));
trainer.AddHook(new UniClassificationAccuracyReporter("validation", 0.5, TimeStep.Every(1, TimeScale.Epoch)));
sigma.AddTrainer(trainer);
sigma.PrepareAndRun();
}
private static void SampleWdbc()
{
SigmaEnvironment sigma = SigmaEnvironment.Create("wdbc");
IDataset dataset = Defaults.Datasets.Wdbc();
ITrainer trainer = sigma.CreateGhostTrainer("wdbc-trainer");
trainer.Network.Architecture = InputLayer.Construct(30)
+ FullyConnectedLayer.Construct(42)
+ FullyConnectedLayer.Construct(24)
+ FullyConnectedLayer.Construct(1)
+ OutputLayer.Construct(1)
+ SquaredDifferenceCostLayer.Construct();
trainer.TrainingDataIterator = new MinibatchIterator(72, dataset);
trainer.AddNamedDataIterator("validation", new UndividedIterator(dataset));
trainer.Optimiser = new GradientDescentOptimiser(learningRate: 0.005);
trainer.AddInitialiser("*.*", new GaussianInitialiser(standardDeviation: 0.1));
trainer.AddLocalHook(new AccumulatedValueReporter("optimiser.cost_total", TimeStep.Every(1, TimeScale.Epoch)));
trainer.AddHook(new UniClassificationAccuracyReporter("validation", 0.5, TimeStep.Every(1, TimeScale.Epoch)));
sigma.AddTrainer(trainer);
sigma.AddMonitor(new HttpMonitor("http://+:8080/sigma/"));
sigma.PrepareAndRun();
}
/// <summary>
/// Set the trainer and hook. Attach the hook.
/// </summary>
/// <param name="trainer">The trainer that will be set.</param>
/// <param name="hook">The hook that will be applied.</param>
protected void Init(ITrainer trainer, VisualAccumulatedValueReporterHook hook)
{
Trainer = trainer;
AttachedHook = hook;
Trainer.AddHook(hook);
Trainer.AddGlobalHook(new LambdaHook(TimeStep.Every(1, TimeScale.Stop), (registry, resolver) => Clear()));
// TODO: is a formatter the best solution?
AxisX.LabelFormatter = number => (number * hook.TimeStep.Interval).ToString(CultureInfo.InvariantCulture);
AxisX.Unit = hook.TimeStep.Interval;
}
/// <summary>
/// Create an IRIS trainer that observers the current epoch and iteration
/// </summary>
/// <param name="sigma">The sigma environemnt.</param>
/// <returns>The newly created trainer that can be added to the environemnt.</returns>
private static ITrainer CreateIrisTrainer(SigmaEnvironment sigma)
{
CsvRecordReader irisReader = new CsvRecordReader(new MultiSource(new FileSource("iris.data"), new UrlSource("http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data")));
IRecordExtractor irisExtractor = irisReader.Extractor("inputs", new[] { 0, 3 }, "targets", 4).AddValueMapping(4, "Iris-setosa", "Iris-versicolor", "Iris-virginica");
irisExtractor = irisExtractor.Preprocess(new OneHotPreprocessor(sectionName: "targets", minValue: 0, maxValue: 2));
irisExtractor = irisExtractor.Preprocess(new PerIndexNormalisingPreprocessor(0, 1, "inputs", 0, 4.3, 7.9, 1, 2.0, 4.4, 2, 1.0, 6.9, 3, 0.1, 2.5));
Dataset dataset = new Dataset("iris", Dataset.BlockSizeAuto, irisExtractor);
IDataset trainingDataset = dataset;
IDataset validationDataset = dataset;
ITrainer trainer = sigma.CreateTrainer("test");
trainer.Network = new Network
{
Architecture = InputLayer.Construct(4)
+ FullyConnectedLayer.Construct(10)
+ FullyConnectedLayer.Construct(20)
+ FullyConnectedLayer.Construct(10)
+ FullyConnectedLayer.Construct(3)
+ OutputLayer.Construct(3)
+ SquaredDifferenceCostLayer.Construct()
};
trainer.TrainingDataIterator = new MinibatchIterator(4, trainingDataset);
trainer.AddNamedDataIterator("validation", new UndividedIterator(validationDataset));
trainer.Optimiser = new GradientDescentOptimiser(learningRate: 0.002);
trainer.Operator = new CpuSinglethreadedOperator();
trainer.AddInitialiser("*.weights", new GaussianInitialiser(standardDeviation: 0.4));
trainer.AddInitialiser("*.bias*", new GaussianInitialiser(standardDeviation: 0.01, mean: 0.05));
trainer.AddHook(new ValueReporterHook("optimiser.cost_total", TimeStep.Every(1, TimeScale.Epoch)));
trainer.AddHook(new ValidationAccuracyReporter("validation", TimeStep.Every(1, TimeScale.Epoch), tops: 1));
trainer.AddLocalHook(new CurrentEpochIterationReporter(TimeStep.Every(1, TimeScale.Epoch)));
return(trainer);
}
/// <summary>
/// Create a BitmapPanel that can easily be updated by a hook. This hook will be automatically attached to a given trainer
/// </summary>
/// <param name="title">The given tile.</param>
/// <param name="trainer">The trainer the hook will be applied to.</param>
/// <param name="headerContent">The content for the header. If <c>null</c> is passed, the title will be used.</param>
/// <param name="inputWidth">The width of the bitmappanel (not the actual width but the width of the data grid).</param>
/// <param name="inputHeight">The height of the bitmappanel (not the actual height but the height of the data grid).</param>
/// <param name="hook">A hook. </param>
protected BitmapHookPanel(string title, int inputWidth, int inputHeight, IHook hook, ITrainer trainer, object headerContent = null)
: this(title, inputWidth, inputHeight, headerContent)
{
if (hook == null)
{
throw new ArgumentNullException(nameof(hook));
}
if (trainer == null)
{
throw new ArgumentNullException(nameof(trainer));
}
trainer.AddHook(hook);
}
/// <summary>
/// Create an AccuracyPanel with a given title. It displays given accuracies per epoch.
/// If a title is not sufficient modify <see cref="SigmaPanel.Header" />.
/// </summary>
/// <param name="title">The given tile.</param>
/// <param name="trainer"></param>
/// <param name="headerContent">The content for the header. If <c>null</c> is passed,
/// the title will be used.</param>
/// <param name="tops"></param>
public AccuracyPanel(string title, ITrainer trainer, ITimeStep timeStep, object headerContent = null, params int[] tops) : base(title, headerContent)
{
if (timeStep == null)
{
throw new ArgumentNullException(nameof(timeStep));
}
// skip the first since its automatically generated
for (int i = 1; i < tops.Length; i++)
{
AddSeries(new LineSeries());
}
trainer.AddHook(new ChartValidationAccuracyReport(this, "validation", timeStep, tops));
trainer.AddGlobalHook(new LambdaHook(TimeStep.Every(1, TimeScale.Stop), (registry, resolver) => Clear()));
AxisY.MinValue = 0;
AxisY.MaxValue = 100;
}
