internal LearningResultUpdatedEventArgs(LearningEpoch epoch, double mse)
        {
            Contract.Requires(epoch != null);
            Contract.Requires(mse >= 0.0);

            Epoch = epoch;
            MSE = mse;
        }
Exemplo n.º 2
0
        internal LearningResult(LearningEpoch epoch, bool bestHolder = false)
            : base(epoch.SyncRoot)
        {
            Contract.Requires(epoch != null);

            Epoch = epoch;
            this.bestHolder = bestHolder;
        }
Exemplo n.º 3
0
        private static void Begin()
        {
            bool recurrent = false;

            var trainingProv = CreateProvider(10000, recurrent);
            var trainingStrat = new GaussianBatchingStrategy(.5);
            //var trainingStrat = new MonteCarloBatchingStrategy();
            var trainingBatcher = new ScriptCollectionBatcher(trainingStrat, trainingProv, 200, 500);

            var validProv = CreateProvider(1000, recurrent);
            var validStrat = new MonteCarloBatchingStrategy();
            var validBatcher = new ScriptCollectionBatcher(validStrat, validProv, 25, 10000);

            trainingBatcher.Initialize();
            validBatcher.Initialize();

            Console.WriteLine("Training samples: " + trainingProv.Count);
            Console.WriteLine("Validation samples: " + validProv.Count);

            // Rules:
            Console.WriteLine("Creating learning rules ...");
            var weightInitRule = new NoisedWeightInitializationRule { Noise = 0.5, IsEnabled = true };
            //var learningRule = new QuickpropRule { StepSize = 0.001 };
            var learningRule = new SCGRule();
            //var learningRule = new LMRule();
            //var learningRule = new MetaQSARule { Mode = LearningMode.Stochastic, Momentum = 0.1, StepSizeRange = new DoubleRange(0.0, 0.005), StepSize = 0.001, StochasticAdaptiveStateUpdate = true };
            //var learningRule = new SuperSABRule { Mode = LearningMode.Batch, Momentum = 0.8, StepSizeRange = new DoubleRange(0.0, 0.01), StepSize = 0.005, StochasticAdaptiveStateUpdate = false };
            //var learningRule = new SignChangesRule { Mode = LearningMode.Stochastic, Momentum = 0.2, StepSizeRange = new DoubleRange(0.0, 0.001), StepSize = 0.001, StochasticAdaptiveStateUpdate = false };
            //var learningRule = new GradientDescentRule { Mode = LearningMode.Stochastic, Momentum = 0.2, StepSize = 0.001 };
            //var learningRule = new QSARule();
            //var learningRule = new MAQRule();
            //var learningRule = new AdaptiveAnnealingRule { WeightGenMul = 0.1, AcceptProbMul = 0.05 };
            //var learningRule = new RpropRule { Momentum = 0.01, StepSize = 0.01 };
            //var learningRule = new CrossEntropyRule { PopulationSize = 50, NumberOfElites = 10, MutationChance = 0.01, MutationStrength = 0.01, DistributionType = DistributionType.Gaussian };
            //var learningRule = new GARule { PopulationSize = 40, MutationStrength = 0.01, MutationChance = 0.01 };

            var wdRule = (ILearningRule)learningRule as IWeightDecayedLearningRule;
            if (wdRule != null)
            {
                wdRule.WeightDecay = new WeightDecay { Factor = -0.0001, IsEnabled = false };
            }

            IterationRepeatPars iterationRepeatPars = new IterationRepeatPars(5, 10);

            // Net:
            Console.WriteLine("Creating Neural Network ...");
            var network = CreateNetwork(recurrent, weightInitRule, learningRule);
            var exec = new LearningExecution(network, iterationRepeatPars);

            // Epoch:
            Console.WriteLine("Initializing epoch ...");
            var epoch = new LearningEpoch(exec, trainingBatcher, validBatcher, 1);
            epoch.Initialize();
            epoch.CurrentResult.Updated += (sender, e) => WriteResult(epoch);
            epoch.BestValidationResult.Updated += (sender, e) => vbestNet = network.Clone();

            // Training loop:
            Console.WriteLine("Starting ...");

            bool done = false;
            do
            {
                //CodeBench.By("Epoch").Do = () =>
                //{
                //    epoch.Step();
                //};

                //CodeBench.By("Epoch").WriteToConsole();

                epoch.Step();

                //WriteResult(epoch);

                if (Console.KeyAvailable)
                {
                    var key = Console.ReadKey();
                    switch (key.Key)
                    {
                        case ConsoleKey.Escape:
                            done = true;
                            break;
                        case ConsoleKey.S:
                            Save(network.Clone());
                            break;
                        case ConsoleKey.V:
                            if (vbestNet != null) Save(vbestNet);
                            break;
                    }
                }
            }
            while (!done);
        }
Exemplo n.º 4
0
 private static void WriteResult(LearningEpoch epoch)
 {
     Console.WriteLine("{0}: Current: {1}/{2} Validation: {3}/{4}",
                 epoch.CurrentIteration.ToString("0000"),
                 epoch.BestResult.MSE.ToString("0.000000"),
                 epoch.CurrentResult.MSE.ToString("0.000000"),
                 epoch.BestValidationResult.MSE.ToString("0.000000"),
                 epoch.CurrentValidationResult.MSE.ToString("0.000000"));
 }