private void RunBenchmark(IPredictionModel model)
{
_loggingService.Log("Running benchmark has started");
_benchmarkView.IsBenchmarkRunning = true;
var provider = new BatchDataProvider(DirectoryHelper.TestLabelsPath, DirectoryHelper.TestImagesPath, 100);
var acc = 0;
for (var i = 0; i < 100; i++)
{
if (!_benchmarkView.IsBenchmarkRunning)
{
break;
}
MnistImageBatch data = provider.GetData();
int[] predictions = data.Pixels.Select(model.Predict).Select(x => x.ArgMax()).ToArray();
acc += data.Labels.Where((lbl, pred) => lbl == predictions[pred]).Count();
_benchmarkView.PerformProgressStep();
_benchmarkView.DrawGrid(new ImageGridModel(data, predictions));
}
_benchmarkView.SetAccuracy(acc);
_benchmarkView.IsBenchmarkRunning = false;
_loggingService.Log("Running benchmark has completed");
}
public ImageGridModel(MnistImageBatch batch, int[] predictions)
{
Labels = new List <int>(batch.Labels);
Predictions = new List <int>(predictions);
Images = new List <Image>(ProcessImages(batch.Pixels));
}
private static void Main()
{
Console.Title = "Digit Recognizer - Engine";
var learningRate = 0.00035;
var epochs = 10;
var regularizationFactor = 15.0;
LearningPipeline pipeline = new LearningPipeline()
.UseGradientClipping()
.UseL2Regularization(regularizationFactor)
.UseDropout(0.5)
.SetWeightsInitializer(InitializerType.RandomInitialization)
.SetEpochCount(epochs);
var layers = new List <NnLayer>
{
new NnLayer(784, 200, new LeakyRelu()),
new NnLayer(200, 100, new LeakyRelu()),
new NnLayer(100, 10, new Softmax())
};
var nn = new NeuralNetwork(layers, learningRate);
var optimizer = new MomentumOptimizer(nn, new CrossEntropy(), 0.93);
var provider = new BatchDataProvider(DirectoryHelper.ExpandedTrainLabelsPath, DirectoryHelper.ExpandedTrainImagesPath, 100);
pipeline.Add(optimizer);
pipeline.Add(nn);
pipeline.Add(provider);
PredictionModel model = pipeline.Run();
var provider1 = new BatchDataProvider(DirectoryHelper.TestLabelsPath, DirectoryHelper.TestImagesPath, 10000);
var acc = 0.0;
MnistImageBatch data = provider1.GetData();
List <double[]> predictions = data.Pixels.Select(pixels => model.Predict(pixels)).ToList();
for (var i = 0; i < data.Labels.Length; i++)
{
if (data.Labels[i] == predictions[i].ArgMax())
{
acc++;
}
}
acc /= 10000.0;
Console.WriteLine($"Accuracy on the test data is: {acc:P2}");
string basePath = Path.GetFullPath(Path.GetDirectoryName(AppDomain.CurrentDomain.BaseDirectory) +
DirectoryHelper.ModelsFolder);
string modelName = $"{Guid.NewGuid()}-{acc:N4}.nn";
string filename = $"{basePath}/{modelName}";
model.Save(filename);
Console.ReadKey();
}