Implements basic functionality that is needed by each of the propagation methods. The specifics of each of the propagation methods is implemented inside of the PropagationMethod interface implementors.
Inheritance: Encog.ML.Train.BasicTraining, ITrain, IMultiThreadable
Propagation Class Documentation
Exemplo n.º 1
0
        /// <summary>
        /// Construct a gradient worker.
        /// </summary>
        ///
        /// <param name="theNetwork">The network to train.</param>
        /// <param name="theOwner">The owner that is doing the training.</param>
        /// <param name="theTraining">The training data.</param>
        /// <param name="theLow">The low index to use in the training data.</param>
        /// <param name="theHigh">The high index to use in the training data.</param>
        /// <param name="theFlatSpots">Holds an array of flat spot constants.</param>
        public GradientWorker(FlatNetwork theNetwork,
                              Propagation theOwner, IMLDataSet theTraining,
                              int theLow, int theHigh, double[] theFlatSpots, IErrorFunction ef)
        {
            _errorCalculation = new ErrorCalculation();
            _network          = theNetwork;
            _training         = theTraining;
            _low      = theLow;
            _high     = theHigh;
            _owner    = theOwner;
            _flatSpot = theFlatSpots;

            _layerDelta = new double[_network.LayerOutput.Length];
            _gradients  = new double[_network.Weights.Length];
            _actual     = new double[_network.OutputCount];

            _weights         = _network.Weights;
            _layerIndex      = _network.LayerIndex;
            _layerCounts     = _network.LayerCounts;
            _weightIndex     = _network.WeightIndex;
            _layerOutput     = _network.LayerOutput;
            _layerSums       = _network.LayerSums;
            _layerFeedCounts = _network.LayerFeedCounts;
            _ef = ef;
        }
Exemplo n.º 2
0
        /// <summary>
        /// Construct a gradient worker.
        /// </summary>
        ///
        /// <param name="theNetwork">The network to train.</param>
        /// <param name="theOwner">The owner that is doing the training.</param>
        /// <param name="theTraining">The training data.</param>
        /// <param name="theLow">The low index to use in the training data.</param>
        /// <param name="theHigh">The high index to use in the training data.</param>
        /// <param name="theFlatSpots">Holds an array of flat spot constants.</param>
        public GradientWorker(FlatNetwork theNetwork,
                                 Propagation theOwner, IMLDataSet theTraining,
                                 int theLow, int theHigh, double[] theFlatSpots, IErrorFunction ef)
        {
            _errorCalculation = new ErrorCalculation();
            _network = theNetwork;
            _training = theTraining;
            _low = theLow;
            _high = theHigh;
            _owner = theOwner;
            _flatSpot = theFlatSpots;

            _layerDelta = new double[_network.LayerOutput.Length];
            _gradients = new double[_network.Weights.Length];
            _actual = new double[_network.OutputCount];

            _weights = _network.Weights;
            _layerIndex = _network.LayerIndex;
            _layerCounts = _network.LayerCounts;
            _weightIndex = _network.WeightIndex;
            _layerOutput = _network.LayerOutput;
            _layerSums = _network.LayerSums;
            _layerFeedCounts = _network.LayerFeedCounts;
            _ef = ef;

            _pair = BasicMLDataPair.CreatePair(_network.InputCount,
                                              _network.OutputCount);
        }