public void Test()
{
double testDataAmount = 0;
double correctEstimate = 0;
foreach (Image image in MnistReader.ReadTestData())
{
testDataAmount++;
_inputLayer.SetNeurons(image.Data);
SetNewDesiredValues(image.Label);
FeedForward();
CalculateError();
_confusionMatrix[(int)image.Label, _outputLayer.GetIndexOfHighestNeuron()]++;
for (int i = 0; i < _outputLayer.DesiredValues.Length; i++)
{
if (_outputLayer.DesiredValues[i] == 1.0)
{
if (i == _outputLayer.GetIndexOfHighestNeuron())
{
correctEstimate++;
}
}
}
}
double accuracy = (correctEstimate / testDataAmount) * 100;
Console.WriteLine("Accuracy: " + accuracy + " %.\n");
PrintConfusionMatrix();
}
public void MNISTPropagateTest()
{
MnistReader.RootPath = Path.GetFullPath(Path.Combine(Environment.CurrentDirectory, @"..\..\..\MNIST"));
var images = MnistReader.ReadTestData().ToList();
Assert.IsTrue(images.Count() > 0);
var image = images.ElementAt(0);
NNArray input = image.Values;
NNArray output = Utils.OneHot(10, image.Label);
var nbInput = input.Length;
IActivation activation;
activation = new Softmax();
var network = new Network(
new NormalizedLayer(nbInput, 1),
new DenseLayerNoBias(nbInput, 10, activation, new CrossEntropyOneHot()));
// network.AddLayer(new DenseLayerNoBias(nbInput, 28, activation, new SquaredDistance()));
network.Initialize();
DateTime start;
DateTime end;
int epoc = 0, maxEpoc = 10000;
double error = double.MaxValue;
start = DateTime.Now;
while (++epoc < maxEpoc && error > 0.01)
{
error = network.Train(input, output, 0.01);
}
end = DateTime.Now;
var duration = (end - start).TotalMilliseconds / 1000;
Console.WriteLine($"Duration for activation {activation.Name}: {duration} \t epoc: {epoc}\terror: {error}");
Assert.AreEqual(image.Label, network.OutputLayer.Output.ArgMax());
Assert.IsTrue(epoc < maxEpoc);
foreach (var img in images.Where(i => i.Label == image.Label))
{
network.Evaluate(img.Values);
Console.WriteLine($"{network.OutputLayer.Output.ArgMax()}");
}
}
public static void ReadDataFromFile()
{
Console.WriteLine("Reading training data ... ");
foreach (var data in MnistReader.ReadTrainingData())
{
TrainData.Add(data);
}
Console.WriteLine("Reading test data ... ");
foreach (var data in MnistReader.ReadTestData())
{
TestData.Add(data);
}
}
private static void TestNetwork(FeedForwardNeuralNet net)
{
Console.WriteLine("Started Testing");
int testedImages = 0;
int rightGuesses = 0;
foreach (var image in MnistReader.ReadTestData())
{
double[] input = Encode(image.Data);
double[] output = net.Run(input);
int result = output.ToList().IndexOf(output.Max());
if (result == image.Label)
{
rightGuesses++;
}
testedImages++;
}
Console.WriteLine($"{testedImages} has been tested. {rightGuesses} were correctly classified.");
}