/// <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); }
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); }
/// <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 SampleXor() { SigmaEnvironment sigma = SigmaEnvironment.Create("logical"); sigma.SetRandomSeed(0); sigma.Prepare(); RawDataset dataset = new RawDataset("xor"); dataset.AddRecords("inputs", new[] { 0, 0 }, new[] { 0, 1 }, new[] { 1, 0 }, new[] { 1, 1 }); dataset.AddRecords("targets", new[] { 0 }, new[] { 0 }, new[] { 0 }, new[] { 1 }); ITrainer trainer = sigma.CreateTrainer("xor-trainer"); trainer.Network.Architecture = InputLayer.Construct(2) + FullyConnectedLayer.Construct(2) + FullyConnectedLayer.Construct(1) + OutputLayer.Construct(1) + SquaredDifferenceCostLayer.Construct(); trainer.TrainingDataIterator = new MinibatchIterator(1, dataset); trainer.AddNamedDataIterator("validation", new UndividedIterator(dataset)); trainer.Optimiser = new GradientDescentOptimiser(learningRate: 0.1); trainer.Operator = new CudaSinglethreadedOperator(); trainer.AddInitialiser("*.*", new GaussianInitialiser(standardDeviation: 0.05)); trainer.AddLocalHook(new StopTrainingHook(atEpoch: 10000)); trainer.AddLocalHook(new AccumulatedValueReporter("optimiser.cost_total", TimeStep.Every(1, TimeScale.Epoch), averageValues: true)); trainer.AddLocalHook(new AccumulatedValueReporter("optimiser.cost_total", TimeStep.Every(1, TimeScale.Stop), averageValues: true)); trainer.AddLocalHook(new ValueReporter("network.layers.*<external_output>._outputs.default.activations", TimeStep.Every(1, TimeScale.Stop))); trainer.AddLocalHook(new ValueReporter("network.layers.*-fullyconnected.weights", TimeStep.Every(1, TimeScale.Stop))); trainer.AddLocalHook(new ValueReporter("network.layers.*-fullyconnected.biases", TimeStep.Every(1, TimeScale.Stop))); sigma.Run(); }
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(); }
private static void SampleNetworkArchitecture() { SigmaEnvironment sigma = SigmaEnvironment.Create("test"); IComputationHandler handler = new CpuFloat32Handler(); ITrainer trainer = sigma.CreateTrainer("test_trainer"); trainer.Network = new Network(); trainer.Network.Architecture = InputLayer.Construct(2, 2) + ElementwiseLayer.Construct(2 * 2) + FullyConnectedLayer.Construct(2) + 2 * (FullyConnectedLayer.Construct(4) + FullyConnectedLayer.Construct(2)) + OutputLayer.Construct(2); trainer.Network = (INetwork)trainer.Network.DeepCopy(); trainer.Operator = new CpuMultithreadedOperator(10); trainer.AddInitialiser("*.weights", new GaussianInitialiser(standardDeviation: 0.1f)); trainer.AddInitialiser("*.bias*", new GaussianInitialiser(standardDeviation: 0.01f, mean: 0.03f)); trainer.Initialise(handler); trainer.Network = (INetwork)trainer.Network.DeepCopy(); Console.WriteLine(trainer.Network.Registry); IRegistryResolver resolver = new RegistryResolver(trainer.Network.Registry); Console.WriteLine("==============="); object[] weights = resolver.ResolveGet <object>("layers.*.weights"); Console.WriteLine(string.Join("\n", weights)); Console.WriteLine("==============="); //foreach (ILayerBuffer buffer in trainer.Network.YieldLayerBuffersOrdered()) //{ // Console.WriteLine(buffer.Layer.Name + ": "); // Console.WriteLine("inputs:"); // foreach (string input in buffer.Inputs.Keys) // { // Console.WriteLine($"\t{input}: {buffer.Inputs[input].GetHashCode()}"); // } // Console.WriteLine("outputs:"); // foreach (string output in buffer.Outputs.Keys) // { // Console.WriteLine($"\t{output}: {buffer.Outputs[output].GetHashCode()}"); // } //} }
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(); }
/// <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); }
private static ITrainer CreateXorTrainer(SigmaEnvironment sigma) { RawDataset dataset = new RawDataset("xor"); dataset.AddRecords("inputs", new[] { 0, 0 }, new[] { 0, 1 }, new[] { 1, 0 }, new[] { 1, 1 }); dataset.AddRecords("targets", new[] { 0 }, new[] { 1 }, new[] { 1 }, new[] { 0 }); ITrainer trainer = sigma.CreateTrainer("xor-trainer"); trainer.Network = new Network(); trainer.Network.Architecture = InputLayer.Construct(2) + FullyConnectedLayer.Construct(1) + OutputLayer.Construct(1) + SquaredDifferenceCostLayer.Construct(); trainer.TrainingDataIterator = new UndividedIterator(dataset); trainer.AddNamedDataIterator("validation", new UndividedIterator(dataset)); trainer.Operator = new CpuSinglethreadedOperator(); trainer.Optimiser = new GradientDescentOptimiser(learningRate: 0.01); trainer.AddInitialiser("*.*", new GaussianInitialiser(standardDeviation: 0.1)); trainer.AddLocalHook(new AccumulatedValueReporter("optimiser.cost_total", TimeStep.Every(1, TimeScale.Epoch), reportEpochIteration: true)); trainer.AddLocalHook(new ValueReporter("network.layers.1-fullyconnected._outputs.default.activations", TimeStep.Every(1, TimeScale.Epoch))); return(trainer); }
public static INetwork GenerateNetwork(double number) { if (_environment == null) { _environment = SigmaEnvironment.Create("TestAverageNetworkMergerEnvironment"); } ITrainer trainer = _environment.CreateTrainer($"trainer{_count++}"); Network net = new Network(); net.Architecture = InputLayer.Construct(2, 2) + FullyConnectedLayer.Construct(2 * 2) + OutputLayer.Construct(2); trainer.Network = net; trainer.AddInitialiser("*.weights", new ConstantValueInitialiser(number)); trainer.Operator = new CpuSinglethreadedOperator(); trainer.Initialise(new CpuFloat32Handler()); SigmaEnvironment.Clear(); return(net); }