private static void SampleLoadExtractIterate()
{
SigmaEnvironment sigma = SigmaEnvironment.Create("test");
sigma.Prepare();
//var 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("inputs2", new[] { 0, 3 }, "targets2", 4).AddValueMapping(4, "Iris-setosa", "Iris-versicolor", "Iris-virginica");
//irisExtractor = irisExtractor.Preprocess(new OneHotPreprocessor(sectionName: "targets2", minValue: 0, maxValue: 2), new NormalisingPreprocessor(sectionNames: "inputs2", minInputValue: 0, maxInputValue: 6));
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));
IComputationHandler handler = new CpuFloat32Handler();
ExtractedDataset dataset = new ExtractedDataset("mnist-training", ExtractedDataset.BlockSizeAuto, mnistImageExtractor, mnistTargetExtractor);
IDataset[] slices = dataset.SplitRecordwise(0.8, 0.2);
IDataset trainingData = slices[0];
IDataset validationData = slices[1];
MinibatchIterator trainingIterator = new MinibatchIterator(1, trainingData);
MinibatchIterator validationIterator = new MinibatchIterator(1, validationData);
while (true)
{
foreach (var block in trainingIterator.Yield(handler, sigma))
{
Thread.Sleep(100);
PrintFormattedBlock(block, PrintUtils.AsciiGreyscalePalette);
Thread.Sleep(1000);
}
}
//Random random = new Random();
//INDArray array = new ADNDArray<float>(3, 1, 2, 2);
//new GaussianInitialiser(0.05, 0.05).Initialise(array, Handler, random);
//Console.WriteLine(array);
//new ConstantValueInitialiser(1).Initialise(array, Handler, random);
//Console.WriteLine(array);
//dataset.InvalidateAndClearCaches();
}
private static void SampleCachedFastIteration()
{
SigmaEnvironment sigma = SigmaEnvironment.Create("test");
IDataSource dataSource = new CompressedSource(new MultiSource(new FileSource("train-images-idx3-ubyte.gz"), new UrlSource("http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz")));
ByteRecordReader mnistImageReader = new ByteRecordReader(headerLengthBytes: 16, recordSizeBytes: 28 * 28, source: dataSource);
IRecordExtractor mnistImageExtractor = mnistImageReader.Extractor("inputs", new[] { 0L, 0L }, new[] { 28L, 28L }).Preprocess(new NormalisingPreprocessor(0, 255));
IDataset dataset = new ExtractedDataset("mnist-training", ExtractedDataset.BlockSizeAuto, mnistImageExtractor);
IDataset[] slices = dataset.SplitRecordwise(0.8, 0.2);
IDataset trainingData = slices[0];
Stopwatch stopwatch = Stopwatch.StartNew();
IDataIterator iterator = new MinibatchIterator(10, trainingData);
foreach (var block in iterator.Yield(new CpuFloat32Handler(), sigma))
{
//PrintFormattedBlock(block, PrintUtils.AsciiGreyscalePalette);
}
Console.Write("\nFirst iteration took " + stopwatch.Elapsed + "\n+=+ Iterating over dataset again +=+ Dramatic pause...");
ArrayUtils.Range(1, 10).ToList().ForEach(i =>
{
Thread.Sleep(500);
Console.Write(".");
});
stopwatch.Restart();
foreach (var block in iterator.Yield(new CpuFloat32Handler(), sigma))
{
//PrintFormattedBlock(block, PrintUtils.AsciiGreyscalePalette);
}
Console.WriteLine("Second iteration took " + stopwatch.Elapsed);
}
public void TestMinibatchIteratorYield(int minibatchSize)
{
string filename = ".unittestfile" + nameof(TestMinibatchIteratorYield);
CreateCsvTempFile(filename);
SigmaEnvironment.Clear();
FileSource source = new FileSource(filename, Path.GetTempPath());
CsvRecordExtractor extractor = (CsvRecordExtractor) new CsvRecordReader(source).Extractor(new CsvRecordExtractor(new Dictionary <string, int[][]> {
["inputs"] = new[] { new[] { 0 } }
}));
ExtractedDataset dataset = new ExtractedDataset("test", 1, new DiskCacheProvider(Path.GetTempPath() + "/" + nameof(TestMinibatchIteratorYield)), true, extractor);
MinibatchIterator iterator = new MinibatchIterator(minibatchSize, dataset);
IComputationHandler handler = new CpuFloat32Handler();
SigmaEnvironment sigma = SigmaEnvironment.Create("test");
Assert.Throws <ArgumentNullException>(() => iterator.Yield(null, null).GetEnumerator().MoveNext());
Assert.Throws <ArgumentNullException>(() => iterator.Yield(handler, null).GetEnumerator().MoveNext());
Assert.Throws <ArgumentNullException>(() => iterator.Yield(null, sigma).GetEnumerator().MoveNext());
int index = 0;
foreach (var block in iterator.Yield(handler, sigma))
{
//pass through each more than 5 times to ensure consistency
if (index++ > 20)
{
break;
}
Assert.Contains(block["inputs"].GetValue <float>(0, 0, 0), new float[] { 5.1f, 4.9f, 4.7f });
}
dataset.Dispose();
DeleteTempFile(filename);
}
