static void Train_predict_and_save(string filepath) { // train & testing data var training_data = new Dictionary <int, Tuple <float[], float[]> > { // ys xs [0] = new Tuple <float[], float[]>(new float[] { 0, 1 }, new float[] { 1 }), [1] = new Tuple <float[], float[]>(new float[] { 1, 0 }, new float[] { 1 }), [2] = new Tuple <float[], float[]>(new float[] { 0, 0 }, new float[] { 0 }), [3] = new Tuple <float[], float[]>(new float[] { 1, 1 }, new float[] { 0 }) }; var snn = new SimpleNeuralNetwork(2, 0.4f, Activation.FunctionsEnum.Sigmoid); snn.Add(4); snn.Add(1); // train Console.WriteLine("Entrenamiento:\n"); Random random = new Random(); int j = 0; Console.WriteLine("Training ..."); for (int i = 0; i < 100000; i++) { j = random.Next(4); snn.Train(training_data[j].Item1, training_data[j].Item2); } // predict Console.WriteLine("\nPredicciones:\n"); for (int i = 0; i < 4; i++) { var res = snn.Predict(training_data[i].Item1); Console.WriteLine(string.Format("xs [ {0}, {1} ] = {2}", training_data[i].Item1[0], training_data[i].Item1[1], res[0])); } SimpleNeuralNetwork.Save(snn, filepath); Console.WriteLine("\nRed Neuronal guardada !!.\n"); }