public static void Main(string[] args)
{
Console.WriteLine("--------Neural Network--------");
Stopwatch sw = new Stopwatch();
Network imageNeuralNetwork;
Console.Write("Loading Images....");
LoadImageData(out List <Image> trainImages, out List <Image> testImages);
Console.WriteLine("Success!\n");
//PrintStatistics(testImages, "Testing Image Set");
//PrintStatistics(trainImages, "Training Image Set");
//PrintImage(1, 3737);
Console.Write("Initializing Network...");
imageNeuralNetwork = new Network(784, 89, 10, 0.2, 0.9, 0.005);
imageNeuralNetwork.Initialize(ActivationFunctions.Sigmoid, ActivationFunctions.Sigmoid);
Console.WriteLine("Success!\n");
Console.WriteLine("*** Learning-Cycle ***");
sw.Start();
//imageNeuralNetwork.Learn(trainImages, testImages);
imageNeuralNetwork.Learn(trainImages);
sw.Stop();
Console.Write("Testing Network....");
int[,] confusionMatrix = imageNeuralNetwork.Test(testImages);
Console.WriteLine("Success!");
ConfusionMatrixDrawer.Draw(confusionMatrix);
Console.WriteLine("Total Learning Time taken: {0}\n", sw.Elapsed.ToString());
Console.ReadKey();
}
//optionally provide test-images to print a confusion matrix after each Cycle. Helps to find sweet spot between over and underfitting.
public void Learn(List <Image> Images, List <Image> TestImages = null)
{
Stopwatch watch = new Stopwatch();
int nrOfRuns = 0;
double networkError = 0.0;
//Train Network until desired error rate is reached
do
{
double networkErrorSum = 0.0;
nrOfRuns++;
watch.Start();
//For all Images in the training set
for (int i = 0; i < Images.Count; i++)
{
//Set Image
InputLayer.NeuronValues = Images[i].Data;
for (int j = 0; j < NrOutputNeurons; j++)
{
if (j == (int)Images[i].Label)
{
OutputLayer.DesiredNeuronValues[j] = 1.0;
}
else
{
OutputLayer.DesiredNeuronValues[j] = 0.0;
}
}
//Calculate values and adjust weights according to error.
this.FeedForward();
this.BackPropagate();
networkErrorSum += CalculateError();
networkError = networkErrorSum / (i + 1);
}
watch.Stop();
//Print error rate and time elapsed for this epoch
Console.Write("Error rate after epoch {0}: ", nrOfRuns, networkError);
Console.ForegroundColor = ConsoleColor.Red;
Console.WriteLine("{0}", networkError);
Console.ForegroundColor = ConsoleColor.Gray;
Console.WriteLine("Time elapsed: {1}", nrOfRuns, watch.Elapsed.ToString());
Console.WriteLine();
if (TestImages != null)
{
ConfusionMatrixDrawer.Draw(Test(TestImages));
}
} while (networkError > GoalErrorRate);
}
