protected DeepLearningRunner(DeviceDescriptor device, TrainingSessionConfiguration configuration, IMessagePrinter printer)
{
Device = device ?? throw new System.ArgumentNullException(nameof(device));
//TODO: User more detailed validation, ie. check all required props/fields for runner to work
Configuration = configuration ?? throw new System.ArgumentNullException(nameof(configuration));
this.MessagePrinter = printer ?? throw new ArgumentNullException(nameof(printer));
}
private static void RunEmnistTraining(string choice)
{
ITrainingDatasetDefinition datasetDefinition = null;
switch (choice)
{
case LETTERS_CHOICE:
datasetDefinition = new EMNISTLetterDataset();
break;
case DIGITS_CHOICE:
datasetDefinition = new EMNISTDigitDataset();
break;
case UPPERCASE_LETTERS_CHOICE:
datasetDefinition = new EMNISTUppercaseLetterDataset();
break;
default:
SharedConsoleCommands.InvalidCommand(choice);
return;
}
TrainingSessionStart(choice);
var msgPrinter = new ConsolePrinter();
var outputDir = $"./{DateTime.Now.ToString("yyyyMMddHHmmss", CultureInfo.InvariantCulture)}/";
var device = DeviceDescriptor.GPUDevice(0);
var trainingConfiguration = new TrainingSessionConfiguration
{
Epochs = 200,
DumpModelSnapshotPerEpoch = true,
ProgressEvaluationSeverity = EvaluationSeverity.PerEpoch,
MinibatchConfig = new MinibatchConfiguration
{
MinibatchSize = 64,
HowManyMinibatchesPerSnapshot = (60000 / 32),
HowManyMinibatchesPerProgressPrint = 500,
DumpModelSnapshotPerMinibatch = false,
AsyncMinibatchSnapshot = false
},
PersistenceConfig = TrainingModelPersistenceConfiguration.CreateWithAllLocationsSetTo(outputDir)
};
msgPrinter.PrintMessage("\n" + trainingConfiguration + "\n");
using (var runner = new ConvolutionalNeuralNetworkRunner(device, trainingConfiguration, msgPrinter))
{
runner.RunUsing(datasetDefinition);
}
EmnistTrainingDone(choice);
}
public ConvolutionalNeuralNetworkRunner(DeviceDescriptor device, TrainingSessionConfiguration configuration, IMessagePrinter printer)
: base(device, configuration, printer)
{
}
