void Predict()
{
var image = reader.Read(currentImageIndex);
viewPort.Source = image.Inverse().ToBitmapSource(reader.NumberOfColumns, reader.NumberOfRows);
var result = ira.Predict(prepareInput(image));
prediction.Text = result.IndexOfMax().ToString();
expected.Text = lablesReader.Read(currentImageIndex).ToString();
}
void drawMNIST()
{
var ira = new IRINA_NN(new int[] { 784, 200, 10 }, 0.2);
var imageReader = new MNISTDataReader.ImageDataReader(@"D:\NeuralNetworks\DataSets\MNIST\train-images.idx3-ubyte");
var labelReader = new MNISTDataReader.LabelDataReader(@"D:\NeuralNetworks\DataSets\MNIST\train-labels.idx1-ubyte");
var itemsCount = imageReader.ItemsCount;
var random = new Random();
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
for (int epoch = 0; epoch < count; ++epoch)
{
//var errors = new List<double>();
for (int sample = 0; sample < samplesInBatch; ++sample)
{
var realIndex = random.Next(0, itemsCount);
var input = prepareInput(imageReader.Read(realIndex));
var label = labelReader.Read(realIndex);
var target = prepareTarget(label);
ira.Train(input, target);
//var predicted = ira.Predict(input);
//errors.Add(0.99 - predicted[label]);
}
//SeriesCollection[0].Values.Add(errors.Average());
}
stopwatch.Stop();
IRINA_NN.Export(ira, @"mnist.nn");
MessageBox.Show($"Complete { count } epochs, { samplesInBatch } samples per one. Estimated time: { stopwatch.ElapsedMilliseconds }ms");
}