public void Run(int firstHiddenLayerSize, int secondHiddenLayerSize, int iterations, float learningRate, int batchSize)
{
var digitReader = new HandwrittenDigitReader();
var trainDigits = digitReader.Iterate(
MnistFiles.FindByType(MNISTFileType.TrainImages).FileName,
MnistFiles.FindByType(MNISTFileType.TrainLabels).FileName);
var testDigits = digitReader.Iterate(
MnistFiles.FindByType(MNISTFileType.TestImages).FileName,
MnistFiles.FindByType(MNISTFileType.TestLabels).FileName);
var trainingData = trainDigits.Select(im => im.ToDataPoint());
var testData = testDigits.Select(im => im.ToDataPoint());
var cancellationToken = new CancellationTokenSource();
_model = new PerceptronModel(
HandwrittenDigit.ImageSize * HandwrittenDigit.ImageSize,
firstHiddenLayerSize,
secondHiddenLayerSize,
10);
_classifier = new PerceptronClassifier(
_model,
learningRate,
batchSize);
var watch = Stopwatch.StartNew();
_classifier.IterationComplete += j =>
{
//Console.WriteLine($"Epoch {j}: {MNISTEvaulate(net, testData)} / {testData.Count}");
OnLog(
$"Iteration #{j} has completed: {EvaluateClassifier(_classifier, testData)} / {testData.Count()} [{watch.ElapsedMilliseconds} ms]");
watch = Stopwatch.StartNew();
if (j == iterations)
{
cancellationToken.Cancel();
}
EpochComplete?.Invoke(j);
};
_classifier.Train(trainingData, cancellationToken.Token);
}
public DemoRunner()
{
MnistFiles = new MNISTDatabase();
var defaultFile = new FileInfo("network.xml");
if (defaultFile.Exists)
{
var xml = defaultFile.OpenRead();
using (var textReader = new StreamReader(xml))
{
var serializer = new PerceptronModelSerializer();
_model = serializer.Deserialize(textReader);
}
}
else
{
_model = new PerceptronModel(30);
}
_classifier = new PerceptronClassifier(_model, 2, 50);
}
