Resilient Backpropagation learning algorithm.

This class implements the resilient backpropagation (RProp) learning algorithm. The RProp learning algorithm is one of the fastest learning algorithms for feed-forward learning networks which use only first-order information.

Sample usage (training network to calculate XOR function):

// initialize input and output values double[][] input = new double[4][] { new double[] {0, 0}, new double[] {0, 1}, new double[] {1, 0}, new double[] {1, 1} }; double[][] output = new double[4][] { new double[] {0}, new double[] {1}, new double[] {1}, new double[] {0} }; // create neural network ActivationNetwork network = new ActivationNetwork( SigmoidFunction( 2 ), 2, // two inputs in the network 2, // two neurons in the first layer 1 ); // one neuron in the second layer // create teacher ResilientBackpropagationLearning teacher = new ResilientBackpropagationLearning( network ); // loop while ( !needToStop ) { // run epoch of learning procedure double error = teacher.RunEpoch( input, output ); // check error value to see if we need to stop // ... }
Inheritance: ISupervisedLearning
ResilientBackpropagationLearning Class Documentation
Beispiel #1
0
        public ResilientBackpropagationLearningForm()
        {
            InitializeComponent();

            watch = new Stopwatch();

            backgroundWorkerTrainer.Disposed += backgroundWorkerTrainer_Disposed;
            backgroundWorkerTrainer.DoWork += backgroundWorkerTrainer_DoWork;
            backgroundWorkerTrainer.ProgressChanged += backgroundWorkerTrainer_ProgressChanged;
            backgroundWorkerTrainer.WorkerSupportsCancellation = true;
            backgroundWorkerTrainer.WorkerReportsProgress = true;
            saveFileDialog1.Filter = "Resilient network files (*.rbn)|*.rbn";
            saveFileDialog1.Title = "Save Resilient network file";
            saveFileDialog1.InitialDirectory = null;
            saveFileDialog1.FileName = null;

            openFileDialog1.Filter = "Resilient network files (*.rbn)|*.rbn";
            openFileDialog1.Title = "Load Resilient network file";
            openFileDialog1.InitialDirectory = null;
            openFileDialog1.FileName = null;

            backgroundWorkerSignal.Disposed += backgroundWorkerSignal_Disposed;
            backgroundWorkerSignal.WorkerSupportsCancellation = true;
            backgroundWorkerSignal.WorkerReportsProgress = true;
            backgroundWorkerSignal.DoWork += backgroundWorkerSignal_DoWork;
            backgroundWorkerSignal.RunWorkerCompleted += backgroundWorkerSignal_RunWorkerCompleted;

            // initialize input and output values
            input = new double[4][] {
                new double[] {0, 0}, new double[] {0, 1},
                new double[] {1, 0}, new double[] {1, 1}
            };
            output = new double[4][] {
                new double[] {0}, new double[] {1},
                new double[] {1}, new double[] {0}
            };

            network = new ActivationNetwork(new SigmoidFunction(2), 2, 2, 1);

            teacher = new  ResilientBackpropagationLearning(network);

            //logg.Show();

            // pane used to draw your chart
            myPane = new GraphPane();

            // poing pair lists
            listPointsOne = new PointPairList();
        }