public static List <(Vector <double> Image, Vector <double> Label)> CombineImagesAndLabels(List <byte[]> images, List <Vector <double> > labels)
{
if (images == null | labels == null)
{
throw new ArgumentNullException();
}
if (images.Count != labels.Count)
{
throw new ArgumentException("List lengths don't match.");
}
List <(Vector <double>, Vector <double>)> ret = new List <(Vector <double>, Vector <double>)>();
for (int i = 0; i < images.Count; i++)
{
ret.Add((MINSTDataLoader.Normalize(images[i]), labels[i]));
}
return(ret);
}
static void Main(string[] args)
{
const string dataRoot = @"C:\HaishiRooster\Data\MINST";
//Load training data
var trainingImages = MINSTDataLoader.LoadImages(Path.Combine(dataRoot, "train-images-idx3-ubyte.gz"));
var trainingLabels = MINSTDataLoader.LoadLabels(Path.Combine(dataRoot, "train-labels-idx1-ubyte.gz"));
var trainingSet = MINSTDataLoader.CombineImagesAndLabels(trainingImages, trainingLabels);
//Load test data
var testingImages = MINSTDataLoader.LoadImages(Path.Combine(dataRoot, "t10k-images-idx3-ubyte.gz"));
var testingLabels = MINSTDataLoader.LoadLabels(Path.Combine(dataRoot, "t10k-labels-idx1-ubyte.gz"));
var testingSet = MINSTDataLoader.CombineImagesAndLabels(testingImages, testingLabels);
//Print training set information and a sample image
Console.WriteLine("Training set size: " + trainingSet.Count);
Console.WriteLine("Testing set size: " + testingSet.Count);
int index = mRand.Next(trainingSet.Count);
Console.WriteLine(string.Format("Here's a random picture from the training set: #{0} ({1})", index, convertToByte(trainingSet[index].Label)));
MINSTDataVisualizer.PrintImage(trainingSet[index].Image, 28);
Network network;
if (args.Length > 0)
{
using (StreamReader reader = new StreamReader(File.OpenRead(args[0])))
{
network = Network.Load(reader);
}
Console.WriteLine("\nNetwork is loaded!");
dumpHyperParameters(network.HyperParameters);
}
else
{
////MSR
//HyperParameters hyperParameters = new HyperParameters { CostFunctionName = "QuadraticCost", Epochs = 30, MiniBatchSize = 10, LearningRate = 3, TestSize = 10, AutoSave = true, AutoSaveThreshold = 0.951 };
//network = new Network(hyperParameters, 784, 30, 10);
////Cross-entropy
//HyperParameters hyperParameters = new HyperParameters { CostFunctionName = "CrossEntropyCost", Epochs = 30, MiniBatchSize = 10, LearningRate = 0.5, TestSize = testingSet.Count, AutoSave = true, AutoSaveThreshold = 0.967 };
//network = new Network(hyperParameters, 784, 100, 10);
////Cross-entropy with regulation
//HyperParameters hyperParameters = new HyperParameters { CostFunctionName = "CrossEntropyCost", Epochs = 60, MiniBatchSize = 10, LearningRate = 0.1, RegulationLambda = 5.0, TestSize = testingSet.Count, AutoSave = true, AutoSaveThreshold = 0.98 };
//network = new Network(hyperParameters, 784, 100, 10);
////Cross-entropy with regulation - 120 epochs
//HyperParameters hyperParameters = new HyperParameters { CostFunctionName = "CrossEntropyCost", Epochs = 120, MiniBatchSize = 10, LearningRate = 0.1, RegulationLambda = 5.0, TestSize = testingSet.Count, AutoSave = true, AutoSaveThreshold = 0.98 };
//network = new Network(hyperParameters, 784, 100, 10);
////Cross-entropy with regulation - 120 epochs - with dropouts
HyperParameters hyperParameters = new HyperParameters {
CostFunctionName = "CrossEntropyCost", Epochs = 240, MiniBatchSize = 10, LearningRate = 1, RegulationLambda = 5.0, TestSize = testingSet.Count, AutoSave = true, AutoSaveThreshold = 0.98, UseDropouts = true
};
network = new Network(hyperParameters, 784, 100, 10);
hookupEvents(network);
dumpHyperParameters(hyperParameters);
//Train the network
network.Train(trainingSet, (actual, expected) =>
{
return(convertToByte(actual) == convertToByte(expected));
}, testingSet);
Console.WriteLine("\nNetwork is trained!");
}
//Now validate
while (true)
{
Console.Write(string.Format("\nPlease enter a test image index [0 - {0}]. Enter '-1' to evaluate all test images. Enter '-2' to exit:", testingSet.Count - 1));
if (int.TryParse(Console.ReadLine(), out index))
{
if (index == -1)
{
int count = 0;
Console.Write(string.Format("Detecting {0} pictures", testingSet.Count));
for (int i = 0; i < testingSet.Count; i++)
{
var detection = network.Detect(testingSet[i].Image);
if (convertToByte(detection) == convertToByte(testingSet[i].Label))
{
Console.Write(".");
count++;
}
else
{
Console.Write("X");
}
}
Console.WriteLine("\nDetected {0} out of {1} pictures, correct rate is: {2:0.0%}", count, testingSet.Count, count * 1.0 / testingSet.Count);
}
else if (index >= 0 && index < testingSet.Count)
{
Console.WriteLine(string.Format("Test image: #{0} ({1})", index, convertToByte(testingSet[index].Label)));
MINSTDataVisualizer.PrintImage(testingSet[index].Image, 28);
var detection = network.Detect(testingSet[index].Image);
Console.WriteLine(string.Format("\nDetected number: {0} - {1}", detection, convertToByte(detection) == convertToByte(testingSet[index].Label) ? "SUCCESS!" : "FAIL!"));
}
else if (index == -2)
{
break;
}
else if (index == -3)
{
Console.Write("Please enter file name: ");
string fileName = Console.ReadLine();
using (StreamWriter writer = new StreamWriter(File.Create(fileName)))
{
network.Save(writer);
}
}
}
}
}
