public void TestForward()
{
var parameterGenerator = new ActionParameterGenerator(() => 0.1, () => 1.0);
var neuronFactor = new NeuronFactory(parameterGenerator);
var synapseFactory = new SynapseFactory(parameterGenerator);
var n = NetworkFactory.CreateMultilayerPerceptron(new[] { 2, 2, 1 }, ActivationFunction.Sigmoid,
ActivationFunction.Identity, null, neuronFactor, synapseFactory);
var x = new Vector(3, 5);
Assert.IsTrue(Math.Abs(n.Compute(x)[0] - 2.0993931) < 0.001);
}
public void TestBack()
{
var parameterGenerator = new ActionParameterGenerator(() => 0.1, () => 1.0);
var neuronFactor = new NeuronFactory(parameterGenerator);
var synapseFactory = new SynapseFactory(parameterGenerator);
var network = NetworkFactory.CreateMultilayerPerceptron(new[] { 2, 2, 1, 1 }, ActivationFunction.Sigmoid,
ActivationFunction.Identity, null, neuronFactor, synapseFactory);
var networkTrainer = new NetworkTrainer(network);
var x = new Vector(3, 5);
network.Compute(x);
networkTrainer.Back(42.0);
var n = network.OutputLayer[0].Inputs[0].Source;
Console.WriteLine(n.InputDerivative);
Console.WriteLine(n.OutputDerivative);
Console.WriteLine(n.InputDerivativeCount);
Assert.IsTrue(Math.Abs(-3.987764 - n.InputDerivative) < 0.01);
Assert.IsTrue(Math.Abs(-41.009155 - n.OutputDerivative) < 0.01);
Assert.AreEqual(1, n.InputDerivativeCount);
}
public void TestTrainXor()
{
var pg = new PositiveUniformParameterGenerator();
var neuronFactor = new NeuronFactory(pg);
var synapseFactory = new SynapseFactory(pg);
var n = NetworkFactory.CreateMultilayerPerceptron(new[] { 2, 2, 1 }, ActivationFunction.Sigmoid,
ActivationFunction.Identity, null, neuronFactor, synapseFactory);
var trainer = new NetworkTrainer(n, 0.9, 0.1);
var examples = new Matrix(new double[, ] {
{ 0, 0 }, { 0, 1 }, { 1, 0 }, { 1, 1 }
});
var labels = new Vector(0, 1, 1, 0);
trainer.Train(examples, labels, 1000);
for (var i = 0; i < labels.Length; i++)
{
var x = examples.GetRow(i);
var y = labels[i];
Console.WriteLine("Actual: {0}, Result: {1}", y, n.Compute(x));
Assert.IsTrue(Math.Abs(y - n.Compute(x)[0]) < 0.01);
}
}
/// <summary>
/// Create a multilayer perceptron
/// </summary>
/// <param name="layerSizes">Number of neurons per layer</param>
/// <param name="activationFunction">Activation function for every hidden node</param>
/// <param name="outputActivationFunction">Activation function for last layer</param>
/// <param name="regularizationFunction">Regularization function</param>
/// <param name="neuronFactory">Neuron factory used to create neurons</param>
/// <param name="synapseFactory">Synapse factory used to create synapses</param>
/// <returns>New network</returns>
public static Network CreateMultilayerPerceptron(int[] layerSizes, IActivationFunction activationFunction,
IActivationFunction outputActivationFunction, IRegularizationFunction regularizationFunction,
NeuronFactory neuronFactory, SynapseFactory synapseFactory)
{
var prevLayer = new Neuron[layerSizes[0]];
// create input layer
var inputNeurons = new Neuron[layerSizes[0]];
for (var i = 0; i < inputNeurons.Length; i++)
{
var n = neuronFactory.CreateNeuron(activationFunction);
inputNeurons[i] = n;
prevLayer[i] = n;
}
// create hidden layers
for (var i = 1; i < layerSizes.Length - 1; i++)
{
var layer = new Neuron[layerSizes[i]];
for (var j = 0; j < layer.Length; j++)
{
var n = neuronFactory.CreateNeuron(activationFunction);
layer[j] = n;
for (var k = 0; k < prevLayer.Length; k++)
{
var pn = prevLayer[k];
synapseFactory.Link(pn, n, regularizationFunction);
}
}
prevLayer = layer;
}
// create output layer
var outputNeurons = new Neuron[layerSizes[layerSizes.Length - 1]];
for (var i = 0; i < outputNeurons.Length; i++)
{
var outputNeuron = neuronFactory.CreateOutputNeuron(outputActivationFunction);
outputNeurons[i] = outputNeuron;
for (var j = 0; j < prevLayer.Length; j++)
{
var pn = prevLayer[j];
synapseFactory.Link(pn, outputNeuron, regularizationFunction);
}
}
return(new Network {
InputLayer = inputNeurons, OutputLayer = outputNeurons
});
}
static void Main(string[] args)
{
NeuralNetworkConfigurationSettings networkConfig = new NeuralNetworkConfigurationSettings
{
NumInputNeurons = 1,
NumOutputNeurons = 1,
NumHiddenLayers = 2,
NumHiddenNeurons = 3,
SummationFunction = new SimpleSummation(),
ActivationFunction = new TanhActivationFunction()
};
GenerationConfigurationSettings generationSettings = new GenerationConfigurationSettings
{
UseMultithreading = true,
GenerationPopulation = 500
};
EvolutionConfigurationSettings evolutionSettings = new EvolutionConfigurationSettings
{
NormalMutationRate = 0.05,
HighMutationRate = 0.5,
GenerationsPerEpoch = 10,
NumEpochs = 1000,
NumTopEvalsToReport = 10
};
MutationConfigurationSettings mutationSettings = new MutationConfigurationSettings
{
MutateAxonActivationFunction = true,
MutateNumberOfHiddenLayers = true,
MutateNumberOfHiddenNeuronsInLayer = true,
MutateSomaBiasFunction = true,
MutateSomaSummationFunction = true,
MutateSynapseWeights = true
};
var random = new RandomWeightInitializer(new Random());
INeuralNetworkFactory factory = NeuralNetworkFactory.GetInstance(SomaFactory.GetInstance(networkConfig.SummationFunction), AxonFactory.GetInstance(networkConfig.ActivationFunction), SynapseFactory.GetInstance(new RandomWeightInitializer(new Random()), AxonFactory.GetInstance(networkConfig.ActivationFunction)), SynapseFactory.GetInstance(new ConstantWeightInitializer(1.0), AxonFactory.GetInstance(new IdentityActivationFunction())), random);
IBreeder breeder = BreederFactory.GetInstance(factory, random).Create();
IMutator mutator = MutatorFactory.GetInstance(factory, random).Create(mutationSettings);
IEvalWorkingSet history = EvalWorkingSetFactory.GetInstance().Create(50);
IEvaluatableFactory evaluatableFactory = new GameEvaluationFactory();
IStorageProxy proxy = new NodeJSProxy(1, "http://localhost:3000", "123456789");
IEpochAction action = new BestPerformerUpdater(proxy);
var GAFactory = GeneticAlgorithmFactory.GetInstance(evaluatableFactory);
IGeneticAlgorithm evolver = GAFactory.Create(networkConfig, generationSettings, evolutionSettings, factory, breeder, mutator, history, evaluatableFactory, action);
evolver.RunSimulation();
}
