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 Main(string[] args)
{
const double high = .99;
const double low = .1;
var doubleTrainingSet = new DoubleTrainingSet
{
InputSet = new[]
{
new[] { high, high },
new[] { low, high },
new[] { high, low },
new[] { low, low }
},
OutputSet = new[]
{
new[] { low },
new[] { high },
new[] { high },
new[] { low }
},
Tollerance = 0.0000000000001,
IterationsPerRun = 5
};
var configuration = new NeuralNetConfiguration
{
HiddenLayerNodes = new Dictionary <int, int>
{
{ 1, 6 },
{ 2, 6 }
},
LearningSpeed = 3,
NrOfInputNeurons = doubleTrainingSet.InputSet.Max(d => d.Length),
NumberOfOutputNeurons = doubleTrainingSet.OutputSet.Max(d => d.Length)
};
var net = new FeedForwardNeuralNet(configuration);
net.Initialize(1);
var count = 0;
while (!doubleTrainingSet.Finished)
{
count++;
net.Train(doubleTrainingSet);
Console.SetCursorPosition(0, 1);
foreach (var output in doubleTrainingSet.CalculatedOutputSet)
{
var sb = new StringBuilder();
foreach (var d in output)
{
sb.Append(Math.Round(d, 14) + " ");
}
Console.WriteLine(sb.ToString());
}
}
Console.WriteLine($"{count * doubleTrainingSet.IterationsPerRun} iterations required for training");
Console.ReadKey();
}
