static void Main(string[] args)
{
var data = FileReaderMNIST.LoadImagesAndLables(
"./data/train-labels-idx1-ubyte.gz",
"./data/train-images-idx3-ubyte.gz");
var training = data.Take(50000);
var test = FileReaderMNIST.LoadImagesAndLables(
"./data/t10k-labels-idx1-ubyte.gz",
"./data/t10k-images-idx3-ubyte.gz");
// var network = new Network(new[] { 784, 30, 10 });
// network.GradientDescent(training, 30, 10, 3.0, test);
Console.WriteLine("Creating a " + 784 + "-" + 30 + "-" + 10 + " neural network");
var network = new NeuralNetwork(new[] { 784, 100, 10 });
var trainingData = ConvertData(training);
var testData = ConvertData(test);
network.GradientDescent(trainingData, 30, 10, 3.0, testData);
}
// http://yann.lecun.com/exdb/mnist/
public static IEnumerable <(double[] image, int label)> GetMNISTHandwrittenNumbers(string labelFileName, string imageFileName)
{
var dataDirectory = $"{TrainingDataDir}/{MNISTHandwrittenNumbersDir}";
EnsureMNISTHandwrittenNumbersDataExists(dataDirectory);
var trainingDataSet = FileReaderMNIST.LoadImagesAndLables($"{dataDirectory}/{labelFileName}", $"{dataDirectory}/{imageFileName}");
foreach (var trainingData in trainingDataSet)
{
var trainingDataAsDoubleArray = new double[784];
var trainingDataAsDouble = trainingData.AsDouble();
for (var i = 0; i < 28; i++)
{
for (var j = 0; j < 28; j++)
{
trainingDataAsDoubleArray[j + (28 * i)] = trainingDataAsDouble[i, j];
}
}
yield return(trainingDataAsDoubleArray, trainingData.Label);
}
}
public MNISTmanager(string LPath, string IPath)
{
ClusterImageIndex = 0;
ClusterLabelIndex = 0;
data = Shuffle(FileReaderMNIST.LoadImagesAndLables(LPath, IPath).ToList());
}
public IEnumerator <TestCase> LoadFiles(string labelsZipFile, string imagesZipFile)
{
var imagesAndLabels = FileReaderMNIST.LoadImagesAndLables(labelsZipFile, imagesZipFile);
return(imagesAndLabels.GetEnumerator());
}
