public void TestCsvRecordReaderExtract() { CsvRecordReader reader = new CsvRecordReader(new FileSource(".")); Assert.Throws <ArgumentException>(() => reader.Extractor()); Assert.Throws <ArgumentException>(() => reader.Extractor("name", "name")); Assert.Throws <ArgumentException>(() => reader.Extractor(1)); Assert.AreEqual(new[] { 0, 1, 2, 3, 6 }, reader.Extractor("inputs", new[] { 0, 3 }, 6).NamedColumnIndexMapping["inputs"]); }
/// <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); }