コード例 #1
0
ファイル: RPropTrainerSettings.cs プロジェクト: thild/NET
 /// <summary>
 /// Deep copy constructor
 /// </summary>
 /// <param name="source">Source instance</param>
 public RPropTrainerSettings(RPropTrainerSettings source)
 {
     NumOfAttempts      = source.NumOfAttempts;
     NumOfAttemptEpochs = source.NumOfAttemptEpochs;
     ZeroTolerance      = source.ZeroTolerance;
     PositiveEta        = source.PositiveEta;
     NegativeEta        = source.NegativeEta;
     IniDelta           = source.IniDelta;
     MinDelta           = source.MinDelta;
     MaxDelta           = source.MaxDelta;
     return;
 }
コード例 #2
0
        //Constructor
        /// <summary>
        /// Instantiates the RPropTrainer
        /// </summary>
        /// <param name="net">The FF network to be trained.</param>
        /// <param name="inputVectorCollection">The input vectors (input).</param>
        /// <param name="outputVectorCollection">The output vectors (ideal).</param>
        /// <param name="cfg">The configuration of the trainer.</param>
        /// <param name="rand">The random object to be used.</param>
        public RPropTrainer(FeedForwardNetwork net,
                            List <double[]> inputVectorCollection,
                            List <double[]> outputVectorCollection,
                            RPropTrainerSettings cfg,
                            Random rand
                            )
        {
            if (!net.Finalized)
            {
                throw new InvalidOperationException($"Can´t create trainer. Network structure was not finalized.");
            }
            _cfg                    = cfg;
            MaxAttempt              = _cfg.NumOfAttempts;
            MaxAttemptEpoch         = _cfg.NumOfAttemptEpochs;
            _net                    = net;
            _rand                   = rand;
            _inputVectorCollection  = inputVectorCollection;
            _outputVectorCollection = outputVectorCollection;
            _weigthsGradsAcc        = new double[_net.NumOfWeights];
            _weigthsPrevGradsAcc    = new double[_net.NumOfWeights];
            _weigthsPrevDeltas      = new double[_net.NumOfWeights];
            _weigthsPrevChanges     = new double[_net.NumOfWeights];
            //Parallel gradient workers (batch ranges) preparation
            int numOfWorkers = Math.Max(1, Math.Min(Environment.ProcessorCount - 1, _inputVectorCollection.Count));

            _gradientWorkerDataCollection = new GradientWorkerData[numOfWorkers];
            int workerBatchSize = _inputVectorCollection.Count / numOfWorkers;

            for (int workerIdx = 0, fromRow = 0; workerIdx < numOfWorkers; workerIdx++, fromRow += workerBatchSize)
            {
                GradientWorkerData gwd = new GradientWorkerData
                                         (
                    fromRow: fromRow,
                    toRow: (workerIdx == numOfWorkers - 1 ? _inputVectorCollection.Count - 1 : (fromRow + workerBatchSize) - 1),
                    numOfWeights: _net.NumOfWeights
                                         );
                _gradientWorkerDataCollection[workerIdx] = gwd;
            }
            InfoMessage = string.Empty;
            //Start training attempt
            Attempt = 0;
            NextAttempt();
            return;
        }