/// <summary>
/// Trains the network
/// </summary>
/// <param name="network">the network to train</param>
private static void Train(Network network)
{
Tuple <double[], double[]>[] _trainingData = GetMNISTData(true, 60000);
Tuple <double[], double[]>[] _testingData = GetMNISTData(false, 5000);
NetworkTrainer _trainer = new NetworkTrainer(network);
foreach (Tuple <int, double?> _result in _trainer.StochasticGradientDescent(10000, _trainingData, 1000, 100, .05, _testingData))
{
Console.WriteLine($"Epoch {_result.Item1}: {_result.Item2}% error");
}
}
public ParkingSpaceClassifier()
{
//int dataWidth = 4;
//int dataHeight = 4;
//int inputSize = dataWidth * dataHeight;
//Net = new Network(inputSize, new[] { 20, 10 }, 1);
//NetworkTrainer trainer = new NetworkTrainer(Net);
////--------------------------------------------------
//// Training data
//List<Tuple<Bitmap, Bitmap>> training = new List<Tuple<Bitmap, Bitmap>>();
//int trainingSize = 1000;
//for (int i = 0; i < trainingSize;i++)
// training.Add(GenerateLabeled(dataWidth, dataHeight));
////--------------------------------------------------
//// Test data
//List<Tuple<Bitmap, Bitmap>> test = new List<Tuple<Bitmap, Bitmap>>();
//int testSize = 200;
//for (int i = 0; i < testSize; i++)
// test.Add(GenerateLabeled(dataWidth, dataHeight));
//Error = trainer.StochasticGradientDescent(trainingSize, training.Select(t => Flatten(t)).ToArray(), 1000, 100, .05, test.Select(t => Flatten(t)).ToArray());
if (File.Exists("./data/MNIST_weights.dat"))
{
Load();
}
else
{
Net = new Network(784, new[] { 100, 50 }, 10);
}
Iterations = 1000;
NetworkTrainer trainer = new NetworkTrainer(Net);
Tuple <double[], double[]>[] _testData = GetTestData();
Tuple <double[], double[]>[] _trainingData = GetTrainingData();
Error = trainer.StochasticGradientDescent(10000, _trainingData, Iterations, 100, .05, _testData);
}
