/// <summary> /// Clone the object. /// </summary> /// <returns>The cloned object.</returns> public object Clone() { ActivationStep result = new ActivationStep(Low, Center, High); return(result); }
private NEATPopulation Generate() { IMLDataSet trainingSet = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal); ICalculateScore score = new TrainingSetScore(trainingSet); // train the neural network ActivationStep step = new ActivationStep(); step.Center = 0.5; NEATTraining train = new NEATTraining( score, 2, 1, 10); return (NEATPopulation)train.Population; }
/// <summary> /// Program entry point. /// </summary> /// <param name="app">Holds arguments and other info.</param> public void Execute(IExampleInterface app) { IMLDataSet trainingSet = new BasicMLDataSet(XORInput, XORIdeal); var pop = new NEATPopulation(2, 1, 1000); ICalculateScore score = new TrainingSetScore(trainingSet); // train the neural network var step = new ActivationStep(); step.Center = 0.5; pop.OutputActivationFunction = step; var train = new NEATTraining(score, pop); EncogUtility.TrainToError(train, 0.01); var network = (NEATNetwork) train.Method; network.ClearContext(); // test the neural network Console.WriteLine(@"Neural Network Results:"); EncogUtility.Evaluate(network, trainingSet); }
private void trainNetworkBackprop() { // IMLTrain train = new Backpropagation(this.network, this.input,this.ideal, 0.000001, 0.1); IMLDataSet aset = new BasicMLDataSet(input, ideal); int epoch = 1; // train the neural network ICalculateScore score = new TrainingSetScore(aset); IMLTrain trainAlt = new NeuralSimulatedAnnealing(network, score, 10, 2, 100); IMLTrain trainMain = new Backpropagation(network, aset, 0.001, 0.0); StopTrainingStrategy stop = new StopTrainingStrategy(); var pop = new NEATPopulation(INPUT_SIZE, OUTPUT_SIZE, 1000); // train the neural network var step = new ActivationStep(); step.Center = 0.5; pop.OutputActivationFunction = step; var train = new NEATTraining(score, pop); trainMain.AddStrategy(new Greedy()); trainMain.AddStrategy(new HybridStrategy(trainAlt)); trainMain.AddStrategy(stop); trainMain.AddStrategy(new HybridStrategy(train)); network.ClearContext(); while (!stop.ShouldStop()) { trainMain.Iteration(); train.Iteration(); Console.WriteLine(@"Training " + @"Epoch #" + epoch + @" Error:" + trainMain.Error+ @" Genetic iteration:"+trainAlt.IterationNumber+ @"neat iteration:"+train.IterationNumber ); epoch++; } }
private NEATPopulation Generate() { IMLDataSet trainingSet = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal); ICalculateScore score = new TrainingSetScore(trainingSet); // train the neural network ActivationStep step = new ActivationStep(); step.Center = 0.5; IEvolutionaryAlgorithm train = NEATUtil.ConstructNEATTrainer( score, 2, 1, 10); return (NEATPopulation)train.Population; }
/// <summary> /// Clone the object. /// </summary> /// <returns>The cloned object.</returns> public object Clone() { var result = new ActivationStep(Low, Center, High); return result; }
private NEATNetwork Create() { IList<NEATNeuron> neurons = new List<NEATNeuron>(); IActivationFunction afSigmoid = new ActivationSigmoid(); IActivationFunction afStep = new ActivationStep(); // create the neurons NEATNeuron input1 = new NEATNeuron( NEATNeuronType.Input, 1, 0.1, 0.2, 0.3); NEATNeuron input2 = new NEATNeuron( NEATNeuronType.Input, 2, 0.1, 0.2, 0.3); NEATNeuron bias = new NEATNeuron( NEATNeuronType.Bias, 3, 0.1, 0.2, 0.3); NEATNeuron hidden1 = new NEATNeuron( NEATNeuronType.Hidden, 4, 0.1, 0.2, 0.3); NEATNeuron output = new NEATNeuron( NEATNeuronType.Output, 5, 0.1, 0.2, 0.3); // add the neurons neurons.Add(input1); neurons.Add(input2); neurons.Add(hidden1); neurons.Add(bias); neurons.Add(output); // connect everything Link(0.01, input1, hidden1, false); Link(0.01, input2, hidden1, false); Link(0.01, bias, hidden1, false); Link(0.01, hidden1, output, false); // create the network NEATNetwork result = new NEATNetwork(2, 1, neurons, afSigmoid, afStep, 3); return result; }
/// <summary> /// Builds and trains a neat network. /// </summary> /// <param name="aset">The IMLDataset.</param> /// <param name="inputcounts">The inputcounts.</param> /// <param name="outputcounts">The outputcounts.</param> /// <param name="populationsize">The populationsize.</param> /// <param name="ToErrorTraining">To error rate you want to train too.</param> /// <returns>a trained netnetwork.</returns> public static NEATNetwork BuildTrainNeatNetwork(IMLDataSet aset, int inputcounts, int outputcounts, int populationsize, double ToErrorTraining) { NEATPopulation pop = new NEATPopulation(inputcounts, outputcounts, populationsize); ICalculateScore score = new TrainingSetScore(aset); // train the neural network ActivationStep step = new ActivationStep(); step.Center = 0.5; pop.OutputActivationFunction = step; NEATTraining train = new NEATTraining(score, pop); EncogUtility.TrainToError(train, ToErrorTraining); NEATNetwork network = (NEATNetwork)train.Method; return network; }
void AddLayers(List<LayerConfig> gen) { foreach (var g in gen) { IActivationFunction act; if (g.ActivationType == 0) { act = new ActivationBiPolar(); } switch (g.ActivationType ) { case 0: act = new ActivationBiPolar(); break; case 1: act = new ActivationBipolarSteepenedSigmoid (); break; case 2: act = new ActivationClippedLinear(); break; case 3: act = new ActivationCompetitive(); break; case 4: act = new ActivationElliott(); break; case 5: act = new ActivationElliottSymmetric(); break; case 6: act = new ActivationGaussian(); break; case 7: act = new ActivationLinear(); break; case 8: act = new ActivationLOG(); break; case 9: act = new ActivationRamp(); break; case 10: act = new ActivationRamp(); break; case 11: act = new ActivationSigmoid(); break; case 12: act = new ActivationSIN(); break; case 13: act = new ActivationSoftMax(); break; case 14: act = new ActivationSteepenedSigmoid(); break; case 15: act = new ActivationStep(); break; case 16: act = new ActivationTANH(); break; default: act = new ActivationSoftMax(); break; } network.AddLayer(new BasicLayer(act, g.hasBias, g.neurons)); } }