internal ITrainingSession ExtractSession(string serialized, double eval)
{
var networkGenes = JsonConvert.DeserializeObject <NeuralNetworkGene>(serialized);
var network = NeuralNetworkFactory.GetInstance().Create(networkGenes);
var session = new FakeTrainingSession(network, eval);
return(session);
}
public INeuralNetwork LoadNeuralNetwork(string filename)
{
var jsonGenes = File.ReadAllText(_directory + filename);
var networkGene = JsonConvert.DeserializeObject <NeuralNetworkGene>(jsonGenes);
var network = NeuralNetworkFactory.GetInstance().Create(networkGene);
return(network);
}
public static IGeneticAlgorithmFactory GetInstance(IEvaluatableFactory evaluatableFactory)
{
var networkFactory = NeuralNetworkFactory.GetInstance();
var workingSetFactory = EvalWorkingSetFactory.GetInstance();
var random = new Random();
var breederFactory = BreederFactory.GetInstance(networkFactory, new RandomWeightInitializer(random));
var mutatorFactory = MutatorFactory.GetInstance(networkFactory, new RandomWeightInitializer(random));
return(new GeneticAlgorithmFactory(networkFactory, workingSetFactory, evaluatableFactory, breederFactory, mutatorFactory));
}
public async Task <ITrainingSession> GetBestSessionAsync()
{
var result = await ParseCloud.CallFunctionAsync <ParseObject>("bestNetwork", new Dictionary <string, object> {
{ "networkVersion", _networkVersion }
});
var networkGenes = JsonConvert.DeserializeObject <NeuralNetworkGene>((string)result["jsonNetwork"]);
var network = NeuralNetworkFactory.GetInstance().Create(networkGenes);
var session = new FakeTrainingSession(network, (double)result["eval"]);
return(session);
}
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();
}
public INeuralNetworkFactory BuildBackpropagationNetworkFactory(IWeightInitializer weightInitializer,
ISomaFactory somaFactory,
IActivationFunctionDerivative activationFunctionDerivative,
IActivationFunction inputActivationFunction,
INeuronFactory neuronFactory)
{
var axonFactory = BackpropagationAxonFactory.GetInstance(activationFunctionDerivative);
var hiddenSynapseFactory = DecoratedSynapseFactory.GetInstance(weightInitializer,
AxonFactory.GetInstance(activationFunctionDerivative));
var inputSynapseFactory = DecoratedSynapseFactory.GetInstance(new ConstantWeightInitializer(1.0),
AxonFactory.GetInstance(inputActivationFunction));
var decoratedNeuronFactory = BackpropagationNeuronFactory.GetInstance(neuronFactory);
INeuralNetworkFactory factory = NeuralNetworkFactory.GetInstance(somaFactory, axonFactory,
hiddenSynapseFactory, inputSynapseFactory, weightInitializer, decoratedNeuronFactory);
var backPropNetworkFactory = new BackpropagationNetworkFactoryDecorator(factory);
return(backPropNetworkFactory);
}
public ITrainingSession GetBestSession()
{
using (var client = new HttpClient())
{
// Replace this with the API key for the web service
client.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", _apiKey);
var networkEndpoint = _baseUrl + "/network/" + _networkVersion;
var response = client.GetAsync(networkEndpoint).Result;
var responseObj = response.Content.ReadAsAsync <NodeJsMessage>().Result;
if (responseObj == null)
{
return(null);
}
var networkGenes = JsonConvert.DeserializeObject <NeuralNetworkGene>(responseObj.networkGenes);
var network = NeuralNetworkFactory.GetInstance().Create(networkGenes);
var session = new FakeTrainingSession(network, responseObj.eval);
return(session);
}
}