FANNCSharp.Float.NeuralNet.SetCallback C# (CSharp) Method

        public void SetCallback(TrainingCallback callback, Object userData)
        {
            Callback = callback;
            UserData = userData;
            GCHandle handle = GCHandle.Alloc(userData);
            UnmanagedCallback = new training_callback(InternalCallback);
            fannfloatPINVOKE.neural_net_set_callback(neural_net.getCPtr(this.net), Marshal.GetFunctionPointerForDelegate(UnmanagedCallback), (IntPtr)handle);
        }

        static void Main()
        {
            const uint num_layers = 3;
            const uint num_neurons_hidden = 96;
            const float desired_error = 0.00007F;

            using (TrainingData trainData = new TrainingData())
            using (TrainingData testData = new TrainingData())
            {
                trainData.CreateTrainFromCallback(374, 48, 3, TrainingDataCallback);
                testData.CreateTrainFromCallback(594, 48, 3, TestDataCallback);

                // Test Accessor classes
                for (int i = 0; i < trainData.TrainDataLength; i++)
                {
                    Console.Write("Input {0}: ", i);
                    for (int j = 0; j < trainData.InputCount; j++)
                    {
                        Console.Write("{0}, ", trainData.InputAccessor[i][j]);
                    }
                    Console.Write("\nOutput {0}: ", i);
                    for (int j = 0; j < trainData.OutputCount; j++)
                    {
                        Console.Write("{0}, ", trainData.OutputAccessor[i][j]);
                    }
                    Console.WriteLine("");
                }

                for (float momentum = 0.0F; momentum < 0.7F; momentum += 0.1F)
                {
                    Console.WriteLine("============= momentum = {0} =============\n", momentum);
                    using (NeuralNet net = new NeuralNet(NetworkType.LAYER, num_layers, trainData.InputCount, num_neurons_hidden, trainData.OutputCount))
                    {
                        net.SetCallback(TrainingCallback, "Hello!");

                        net.TrainingAlgorithm = TrainingAlgorithm.TRAIN_INCREMENTAL;

                        net.LearningMomentum = momentum;

                        net.TrainOnData(trainData, 20000, 500, desired_error);

                        Console.WriteLine("MSE error on train data: {0}", net.TestData(trainData));
                        Console.WriteLine("MSE error on test data: {0}", net.TestData(testData));
                    }

                }
            }
            Console.ReadKey();
        }