public void Mnist([DefaultValue(true)] bool gui)
{
const int batchSize = 128;
const int hSize = 20;
MklProvider.TryUseMkl(true, ConsoleProgressWriter.Instance);
string dataDir = Path.Combine(Path.GetTempPath(), "Retia_datasets", "MNIST");
DownloadDataset(dataDir);
Console.WriteLine("Loading training set");
var trainSet = LoadTrainingSet(dataDir);
trainSet.BatchSize = batchSize;
var network = new LayeredNet <float>(batchSize, 1,
new AffineLayer <float>(trainSet.InputSize, hSize, AffineActivation.Sigmoid),
new LinearLayer <float>(hSize, trainSet.TargetSize),
new SoftMaxLayer <float>(trainSet.TargetSize));
var optimizer = new AdamOptimizer <float>();
network.Optimizer = optimizer;
var trainer = new OptimizingTrainer <float>(network, optimizer, trainSet,
new OptimizingTrainerOptions(1)
{
ErrorFilterSize = 100,
MaxEpoch = 1,
ProgressWriter = ConsoleProgressWriter.Instance,
ReportProgress = new EachIteration(100),
ReportMesages = true
}, new OptimizingSession("MNIST"));
RetiaGui retiaGui;
if (gui)
{
retiaGui = new RetiaGui();
retiaGui.RunAsync(() => new TrainingWindow(new TypedTrainingModel <float>(trainer)));
}
var runner = ConsoleRunner.Create(trainer, network);
runner.Run();
}
public void Xor()
{
MklProvider.TryUseMkl(true, ConsoleProgressWriter.Instance);
var optimizer = new RMSPropOptimizer <float>(1e-3f);
var net = new LayeredNet <float>(1, 1, new AffineLayer <float>(2, 3, AffineActivation.Tanh), new AffineLayer <float>(3, 1, AffineActivation.Tanh)
{
ErrorFunction = new MeanSquareError <float>()
})
{
Optimizer = optimizer
};
var trainer = new OptimizingTrainer <float>(net, optimizer, new XorDataset(true), new OptimizingTrainerOptions(1)
{
ErrorFilterSize = 0,
ReportProgress = new EachIteration(1),
ReportMesages = true,
ProgressWriter = ConsoleProgressWriter.Instance,
LearningRateScaler = new ProportionalLearningRateScaler(new EachIteration(1), 9e-5f)
}, new OptimizingSession("XOR"));
var runner = ConsoleRunner.Create(trainer, net);
trainer.TrainReport += (sender, args) =>
{
if (args.Errors.Last().RawError < 1e-7f)
{
runner.Stop();
Console.WriteLine("Finished training.");
}
};
var gui = new RetiaGui();
gui.RunAsync(() => new TrainingWindow(new TypedTrainingModel <float>(trainer)));
runner.Run();
}
public void Learn(
[Aliases("b"), Required] string batchesPath,
[Aliases("c")] string configPath,
[Aliases("r"), DefaultValue(0.0002f)] float learningRate,
[DefaultValue(false)] bool gpu,
[DefaultValue(true)] bool gui)
{
MklProvider.TryUseMkl(true, ConsoleProgressWriter.Instance);
Console.WriteLine($"Loading test set from {batchesPath}");
_dataProvider.Load(batchesPath);
var optimizer = new RMSPropOptimizer <float>(learningRate, 0.95f, 0.0f, 0.9f);
LayeredNet <float> network;
if (string.IsNullOrEmpty(configPath))
{
network = CreateNetwork(_dataProvider.TrainingSet.InputSize, 128, _dataProvider.TrainingSet.TargetSize);
network.Optimizer = optimizer;
}
else
{
network = CreateNetwork(configPath);
network.Optimizer = optimizer;
}
network.ResetOptimizer();
if (gpu)
{
Console.WriteLine("Brace yourself for GPU!");
network.UseGpu();
}
var trainOptions = new OptimizingTrainerOptions(SEQ_LEN)
{
ErrorFilterSize = 100,
ReportMesages = true,
MaxEpoch = 1000,
ProgressWriter = ConsoleProgressWriter.Instance,
ReportProgress = new EachIteration(10)
};
trainOptions.LearningRateScaler = new ProportionalLearningRateScaler(new ActionSchedule(1, PeriodType.Iteration), 9.9e-5f);
var session = new OptimizingSession(Path.GetFileNameWithoutExtension(batchesPath));
var trainer = new OptimizingTrainer <float>(network, optimizer, _dataProvider.TrainingSet, trainOptions, session);
RetiaGui retiaGui;
TypedTrainingModel <float> model = null;
if (gui)
{
retiaGui = new RetiaGui();
retiaGui.RunAsync(() =>
{
model = new TypedTrainingModel <float>(trainer);
return(new TrainingWindow(model));
});
}
var epochWatch = new Stopwatch();
trainer.EpochReached += sess =>
{
epochWatch.Stop();
Console.WriteLine($"Trained epoch in {epochWatch.Elapsed.TotalSeconds} s.");
// Showcasing plot export
if (model != null)
{
using (var stream = new MemoryStream())
{
model.ExportErrorPlot(stream, 600, 400);
stream.Seek(0, SeekOrigin.Begin);
session.AddFileToReport("ErrorPlots\\plot.png", stream);
}
}
epochWatch.Restart();
};
trainer.PeriodicActions.Add(new UserAction(new ActionSchedule(100, PeriodType.Iteration), () =>
{
if (gpu)
{
network.TransferStateToHost();
}
string text = TestRNN(network.Clone(1, SEQ_LEN), 500, _dataProvider.Vocab);
Console.WriteLine(text);
session.AddFileToReport("Generated\\text.txt", text);
trainOptions.ProgressWriter.ItemComplete();
}));
var runner = ConsoleRunner.Create(trainer, network);
epochWatch.Start();
runner.Run();
}