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 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();
}