private static void SampleHutter() { const long timeWindowSize = 10L; SigmaEnvironment sigma = SigmaEnvironment.Create("recurrent"); IDataSource source = new MultiSource(new FileSource("enwik8"), new CompressedSource(new MultiSource(new FileSource("enwik8.zip"), new UrlSource("http://mattmahoney.net/dc/enwik8.zip")))); IRecordExtractor extractor = new CharacterRecordReader(source, (int)(timeWindowSize + 1), Encoding.ASCII) .Extractor(new ArrayRecordExtractor <short>(ArrayRecordExtractor <short> .ParseExtractorParameters("inputs", new[] { 0L }, new[] { timeWindowSize }, "targets", new[] { 0L }, new[] { timeWindowSize })) .Offset("targets", 1L)) .Preprocess(new PermutePreprocessor(0, 2, 1)) .Preprocess(new OneHotPreprocessor(0, 255)); IDataset dataset = new ExtractedDataset("hutter", ExtractedDataset.BlockSizeAuto, false, extractor); ITrainer trainer = sigma.CreateTrainer("hutter"); trainer.Network.Architecture = InputLayer.Construct(256) + RecurrentLayer.Construct(256) + OutputLayer.Construct(256) + SoftMaxCrossEntropyCostLayer.Construct(); trainer.TrainingDataIterator = new MinibatchIterator(32, dataset); trainer.AddNamedDataIterator("validation", new MinibatchIterator(100, dataset)); trainer.Optimiser = new AdagradOptimiser(baseLearningRate: 0.07); trainer.Operator = new CudaSinglethreadedOperator(); trainer.AddInitialiser("*.*", new GaussianInitialiser(standardDeviation: 0.05)); trainer.AddLocalHook(new AccumulatedValueReporter("optimiser.cost_total", TimeStep.Every(1, TimeScale.Iteration), averageValues: true)); trainer.AddLocalHook(new RunningTimeReporter(TimeStep.Every(10, TimeScale.Iteration))); sigma.PrepareAndRun(); }
private static void SampleTicTacToe() { SigmaEnvironment sigma = SigmaEnvironment.Create("tictactoe"); IDataset dataset = Defaults.Datasets.TicTacToe(); ITrainer trainer = sigma.CreateTrainer("tictactoe-trainer"); trainer.Network = new Network(); trainer.Network.Architecture = InputLayer.Construct(9) + FullyConnectedLayer.Construct(63, "tanh") + FullyConnectedLayer.Construct(90, "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))); 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 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 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(); }