private static void TrainNetwork(FeedForwardNeuralNet net)
{
List <double[]> trainingInput = new List <double[]>();
List <double[]> trainingOutput = new List <double[]>();
List <double[]> validationInput = new List <double[]>();
List <double[]> validationOutput = new List <double[]>();
int i = 0;
foreach (var image in MnistReader.ReadTrainingData())
{
double[] input = Encode(image.Data);
double[] output = Encode(image.Label);
if (i < 50000)
{
trainingInput.Add(input);
trainingOutput.Add(output);
}
else
{
validationInput.Add(input);
validationOutput.Add(output);
}
i++;
}
Console.WriteLine("Started Training:");
net.Train(trainingInput, trainingOutput, 30, 10, 5.0, validationInput, validationOutput);
Console.WriteLine("Finished training.");
}
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);
}
}
public void Train(double targetErrorRate = 0.05)
{
int count = 0;
int sum = 0;
int epochs = 0;
double totalError = 1.0;
while (totalError > targetErrorRate)
{
epochs++;
_errors.Clear();
foreach (Image image in MnistReader.ReadTrainingData())
{
count++;
_inputLayer.SetNeurons(image.Data);
SetNewDesiredValues(image.Label);
FeedForward();
_errors.Add(CalculateError());
BackPropagate();
if (count == 1000)
{
sum += count;
count = 0;
Console.WriteLine("Images read: " + sum);
}
}
totalError = CalculateTotalError();
Console.WriteLine("Current total error: " + totalError + "\tEpoch " + epochs);
}
Console.WriteLine("Finished training with total error of: " + totalError);
}
private static void MnistTest()
{
var model = new SequentialModel(
new DenseLayer(784, 800),
//new ActivationLayer(new Sigmoid(), 800),
new ActivationLayer(new LeakyReLU(0.5d), 800),
new DenseLayer(800, 10),
new ActivationLayer(new Sigmoid(), 10)
);
var mnistTrain = MnistReader.ReadTrainingData()
.Select(mnistImage =>
{
var image = new double[mnistImage.Width * mnistImage.Height];
for (int i = 0; i < mnistImage.Height; i++)
{
for (int j = 0; j < mnistImage.Width; j++)
{
image[(i * mnistImage.Width) + j] = mnistImage.Image[i, j] / 255d;
}
}
var label = new double[10];
label[mnistImage.Label] = 1;
return(image, label);
}).ToArray();
var rng = new Random();
(double[] Input, double[] ExpectedOutput) dataSource() => mnistTrain[rng.Next(0, mnistTrain.Length - 1)];
//using var window = new RenderWindow(new VideoMode(600, 600), "Map");
//window.Clear(Color.Black);
//var (exampleInput, exampleExpectedOutput) = dataSource();
//var img = new Image(28, 28);
//for (uint i = 0; i < 28; i++)
//{
// for (uint j = 0; j < 28; j++)
// {
// var brightness = (byte)(exampleInput[(j * 28) + i] * 255d);
// img.SetPixel(i, j, new Color(brightness, brightness, brightness));
// }
//}
//window.Draw(new Sprite(new Texture(img)) { Scale = new Vector2f(600 / 28f, 600 / 28f) });
//window.Draw(new Text("Actual: " + Array.IndexOf(exampleExpectedOutput, 1), new Font(Resources.Arial)));
//window.Display();
model.Train(
dataSource: dataSource,
epochs: 60000,
batchSize: 1000,
learningRate: 0.001d,
errorFunction: new MeanSquareError(),
callback: (i, error, metric) => Console.WriteLine("[" + i.ToString().PadLeft(5) + "] (" + (metric * 100d)?.ToString("0.00").PadLeft(6) + "%) Error: " + error),
metric: (expected, actual) =>
{
if (expected.Length == 0)
{
throw new ArgumentException("Expected array to be non-empty.", nameof(expected));
}
if (expected.Length != actual.Length)
{
throw new ArgumentException("Expected array of same size as " + nameof(expected) + ".", nameof(actual));
}
var maxExpected = 0;
var maxActual = 0;
for (int i = 1; i < expected.Length; i++)
{
if (expected[i] > expected[maxExpected])
{
maxExpected = i;
}
if (actual[i] > actual[maxActual])
{
maxActual = i;
}
}
return(maxExpected == maxActual ? 1 : 0);
});
}
