public float[] OutputNetwork(InOutPair IOP, bool Normalize = false)
{
float[] returnArray = runNetwork(IOP.Inputs, Normalize);
Console.WriteLine("");
Console.WriteLine("The output of the network is :");
for (int i = 0; i < returnArray.Length; i++)
{
Console.WriteLine("Node " + (i + 1) + ": " + returnArray[i]);
}
Console.WriteLine("");
return(returnArray);
}
public static void Main()
{
int height = 100;
int width = 100;
float[] input = new float[height * width];
for (int i = 0; i < input.Length; i++)
{
input[i] = .5f;
}
Random rnd = new Random();
InOutPair[] IO = new InOutPair[1];
float[] toAdd = new float[height * width];
for (int i = 0; i < height * width; i++)
{
toAdd[i] = 1f / i;
}
IO[0] = new InOutPair(toAdd, new float[] { .1f, .5f, -.1f });
Epoch Ep = new Epoch(IO, 1000, true);
NeuralNetwork NN = new NeuralNetwork(9, rnd, .01f, 1, 10, 200);
NN.AddLayer(5, "HypTan");
NN.AddLayer(5, "HypTan");
NN.AddLayer(5, "HypTan");
NN.AddLayer(3, "HypTan");
Convolution CC = new Convolution(height, width);
CC.AddLayer(5, 5, 5);
CC.AddLayer(2, 2, 1, new HyperbolicTangent());
CC.AddLayer(6, 6, 1);
CC.AddLayer(2, 2, 2, true);
CC.AddLayer(2, 2, 1, new HyperbolicTangent());
CC.AddLayer(2, 2, 2, true);
Combination C = new Combination(CC, NN);
float[] layerOut = C.Run(input);
for (int i = 0; i < layerOut.Length; i++)
{
Console.WriteLine(layerOut[i]);
}
C.Train(Ep);
Console.WriteLine();
layerOut = C.Run(input);
for (int i = 0; i < layerOut.Length; i++)
{
Console.WriteLine(layerOut[i]);
}
}