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();
}
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 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();
}