public static void Result() { while (true) { string filename = Console.ReadLine(); try { Console.WriteLine(network.GetResult(InputImage(new Bitmap(filename)))[0] * 2); } catch { } } }
static void Main(string[] args) { MyActivateFunc = delegate(double value) { return(1 / (1 + Math.Exp(-value))); }; try { NetworkREP network = new NetworkREP(new Network.Functions(MyActivateFunc, null, null), 2, 4, 1, 1); network.LearningNorm = 0.25; network.InertialTerm = 1; network[0, 0].AddNextNeuron(network[1, 1], -0.2); network[0, 0].AddNextNeuron(network[1, 2], -0.1); network[0, 1].AddNextNeuron(network[1, 1], 0.1); network[0, 1].AddNextNeuron(network[1, 2], 0.3); network[1, 0].AddNextNeuron(network[1, 1], 0.1); network[1, 0].AsOffset(); network[1, 3].AddNextNeuron(network[1, 2], 0.1); network[1, 3].AsOffset(); network[1, 1].AddNextNeuron(network[3, 0], 0.2); network[1, 2].AddNextNeuron(network[3, 0], 0.3); network[2, 0].AddNextNeuron(network[3, 0], 0.2); network[2, 0].AsOffset(); foreach (double res in network.GetResult(1, 0)) { Outputter.Log("Ответ: " + res); } Console.ReadLine(); Console.WriteLine(); for (int i = 0; i < 10000; i++) { network.Learning(new double[] { 0, 1 }, new double[] { 0 }); network.Learning(new double[] { 0, 0 }, new double[] { 0 }); network.Learning(new double[] { 1, 0 }, new double[] { 0 }); network.Learning(new double[] { 1, 1 }, new double[] { 1 }); } Console.WriteLine("Done."); Console.ReadLine(); foreach (double res in network.GetResult(1, 0)) { Outputter.Log("Ответ: " + res); } Console.ReadLine(); foreach (double res in network.GetResult(0, 1)) { Outputter.Log("Ответ: " + res); } Console.ReadLine(); foreach (double res in network.GetResult(0, 0)) { Outputter.Log("Ответ: " + res); } Console.ReadLine(); foreach (double res in network.GetResult(1, 1)) { Outputter.Log("Ответ: " + res); } } catch (Exception ex) { Outputter.Error(ex.Message); } Console.ReadLine(); }