public static void Run()
{
trainingData = ImageSample.LoadTrainingImages(); // 50,000 training images
testingData = ImageSample.LoadTestingImages(); // 10,000 testing images
var net = new NeuralNet(ImageWidthHeight * ImageWidthHeight, 20, 10);
var trainer = new Trainer(net);
var sw = new Stopwatch();
sw.Start();
trainer.Train(trainingData, testingData, learningRate: .01, epochs: 10);
sw.Stop();
Console.WriteLine($"Training Ellapsed: {sw.Elapsed.TotalSeconds} seconds = {sw.Elapsed.TotalSeconds / 60} minutes");
var testInfos = GetImageTestInfo(new FiringNet(net), testingData).ToList();
var failures =
(from testInfo in testInfos
where !testInfo.IsCorrect
select new { testInfo.ImageSample.Label, testInfo.TotalLoss, testInfo.OutputValues }).ToList();
Console.WriteLine($"Test set accuracy: { (testInfos.Count - failures.Count) * 100 / testInfos.Count}");
Console.WriteLine("Failures with highest loss");
foreach (var f in failures.OrderByDescending(f => f.TotalLoss).Take(100))
{
Console.WriteLine($"Label: {f.Label}, Predicted: {HelperMethods.IndexOfMax(f.OutputValues)}, TotalLoss: {f.TotalLoss}");
}
}
async Task Main()
{
trainingData = ImageSample.LoadTrainingImages(); // 50,000 training images
testingData = ImageSample.LoadTestingImages(); // 10,000 testing images
var net = new NeuralNet(ImageWidthHeight * ImageWidthHeight, 50, 10);
var trainer = new Trainer(net);
await Task.Run(() => trainer.Train(trainingData, testingData, learningRate: .01, epochs: 10));
var failures =
from testInfo in GetImageTestInfo(new FiringNet(net), testingData)
where !testInfo.IsCorrect
select new { testInfo.Image, testInfo.ImageSample.Label, testInfo.TotalLoss, testInfo.OutputValues };
failures.OrderByDescending(f => f.TotalLoss).Take(100).PrintDump();
}
