private static void PrettyPrint()
{
while (true)
{
PrintPrettyPrintOptions();
SharedConsoleCommands.YourInput();
var input = Colorful.Console.ReadLine();
ITrainingDatasetDefinition datasetDefinition;
switch (input)
{
case EXIT_CHOICE:
return;
case DIGITS_CHOICE:
datasetDefinition = new EMNISTDigitDataset();
break;
case LETTERS_CHOICE:
datasetDefinition = new EMNISTLetterDataset();
break;
case UPPERCASE_LETTERS_CHOICE:
datasetDefinition = new EMNISTUppercaseLetterDataset();
break;
default:
SharedConsoleCommands.InvalidCommand(input);
continue;
}
try
{
Colorful.Console.WriteLine("Choose line:", Color.Gray);
CntkDatasetRow prettyPrintInput = null;
try
{
var inputLineNum = Int32.Parse(Colorful.Console.ReadLine());
prettyPrintInput =
new CntkEmnistDatasetStreamFetcher().GetRowFromDefinition(datasetDefinition, inputLineNum);
}
catch (Exception ex) when(ex is ArgumentNullException || ex is FormatException)
{
Colorful.Console.WriteLine($"Not a valid line number!", Color.IndianRed);
continue;
}
new CntkEmnistDatasetStreamPrinter().PrettyPrint(prettyPrintInput);
}
catch (InvalidEmnistDatasetFeatureLengthException)
{
Colorful.Console.WriteLine($"Feature stream is not of length 28*28. Cannot pretty print it.", Color.Gray);
}
}
}
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);
}