private async void LoadModel()
{
//Load a machine learning model
StorageFile modelFile = await StorageFile.GetFileFromApplicationUriAsync(new Uri($"ms-appx:///Assets/MNIST.onnx"));
ModelGen = await MNISTModel.CreateMNISTModel(modelFile);
}
public MNISTModelVM()
{
model = new MNISTModel();
model.ResultIsReady += ResultEventHandler;
ImageResults = new ObservableCollection <MNISTModelResult>();
ImageClasses = new ObservableCollection <ObservableCollection <MNISTModelResult> >();
ClassesInfo = new ObservableCollection <string>();
for (int i = 0; i < MNISTModel.NumOfClasses; i++)
{
ImageClasses.Add(new ObservableCollection <MNISTModelResult>());
ClassesInfo.Add(ClassInfoProcess(i, 0));
}
Source = new CancellationTokenSource();
}
static async Task Main(string[] args)
{
MNISTModel model = new MNISTModel();
Console.CancelKeyPress += new ConsoleCancelEventHandler(CancelEventHandler);
model.ResultIsReady += ResultEventHandler;
try
{
// Console.Write("Choose directories with images: ");
string dirPath = @"..\MNISTModelLib\Samples\Decoded"; //Console.ReadLine();
await model.PredImages(dirPath, source.Token);
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
}
}
private void Evaluate_Click(object sender, EventArgs e)
{
// preprocess bitmap
var preprocessed = new BitmapPreprocessor(bmp).PreprocessImage();
var bitmap = new BitmapData(preprocessed);
// convert bitmap to dataset
var data = new HandwrittenDigit {
PixelData = bitmap.ToFloatArray()
};
// train model (load from file if model exists)
MNISTModel model = new MNISTModel();
model.Train();
// create PredictionEngine
PredictionWrapper <HandwrittenDigit, HandwrittenDigitPrediction> wrapper =
new PredictionWrapper <HandwrittenDigit, HandwrittenDigitPrediction>(model.Context, model.Transformer);
wrapper.InitPredictionEngine();
// prediction result
HandwrittenDigitPrediction prediction;
wrapper.Predict(data, out prediction);
// find highest confidence
// find max score and retrieve number
int max = 0;
float maxScore = prediction.Score[0];
// iterate through all scoresto find max
for (int i = 1; i < 10; i++)
{
// update max under correct condition
if (prediction.Score[i] > maxScore)
{
max = i;
maxScore = prediction.Score[i];
}
}
// update output label
PredictionLabel.Text = max.ToString();
}
public ImageClassifier()
{
model = new MNISTModel(@"..\\..\\MNISTModelLib\\mnist-8.onnx");
}
