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);
}
