/// <summary> /// Create a MNIST trainer (writing recognition) will be added to an environemnt. /// </summary> /// <param name="sigma">The sigma environemnt this trainer will be assigned to.</param> /// <returns>The newly created trainer.</returns> private static ITrainer CreateMnistTrainer(SigmaEnvironment sigma) { ByteRecordReader mnistImageReader = new ByteRecordReader(headerLengthBytes: 16, recordSizeBytes: 28 * 28, source: new CompressedSource(new MultiSource(new FileSource("train-images-idx3-ubyte.gz"), new UrlSource("http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz")))); IRecordExtractor mnistImageExtractor = mnistImageReader.Extractor("inputs", new[] { 0L, 0L }, new[] { 28L, 28L }).Preprocess(new NormalisingPreprocessor(0, 255)); ByteRecordReader mnistTargetReader = new ByteRecordReader(headerLengthBytes: 8, recordSizeBytes: 1, source: new CompressedSource(new MultiSource(new FileSource("train-labels-idx1-ubyte.gz"), new UrlSource("http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz")))); IRecordExtractor mnistTargetExtractor = mnistTargetReader.Extractor("targets", new[] { 0L }, new[] { 1L }).Preprocess(new OneHotPreprocessor(minValue: 0, maxValue: 9)); IDataset dataset = new Dataset("mnist-training", Dataset.BlockSizeAuto, mnistImageExtractor, mnistTargetExtractor); ITrainer trainer = sigma.CreateTrainer("test"); trainer.Network = new Network { Architecture = InputLayer.Construct(28, 28) + 2 * FullyConnectedLayer.Construct(28 * 28) + FullyConnectedLayer.Construct(10) + OutputLayer.Construct(10) + SoftMaxCrossEntropyCostLayer.Construct() }; trainer.TrainingDataIterator = new MinibatchIterator(8, dataset); trainer.Optimiser = new AdagradOptimiser(baseLearningRate: 0.02); trainer.Operator = new CpuSinglethreadedOperator(); trainer.AddInitialiser("*.weights", new GaussianInitialiser(standardDeviation: 0.05f)); trainer.AddInitialiser("*.bias*", new GaussianInitialiser(standardDeviation: 0.01f, mean: 0.03f)); trainer.AddGlobalHook(new CurrentEpochIterationReporter(TimeStep.Every(1, TimeScale.Iteration))); return(trainer); }
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); }
/// <summary> /// Create a MNIST trainer (writing recognition) that will be added to an environemnt. /// </summary> /// <param name="sigma">The sigma environemnt this trainer will be assigned to.</param> /// <returns>The newly created trainer.</returns> private static ITrainer CreateMnistTrainer(SigmaEnvironment sigma) { 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 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))); trainer.AddLocalHook(new RunningTimeReporter(TimeStep.Every(1, TimeScale.Epoch), 4)); return(trainer); }
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(); }
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(); }