public void Run()
{
// create training set (logical XOR function)
TrainingSet trainingSet = new TrainingSet(2, 1);
trainingSet.Add(new SupervisedTrainingElement(new double[] { 0, 0 }, new double[] { 0 }));
trainingSet.Add(new SupervisedTrainingElement(new double[] { 0, 1 }, new double[] { 1 }));
trainingSet.Add(new SupervisedTrainingElement(new double[] { 1, 0 }, new double[] { 1 }));
trainingSet.Add(new SupervisedTrainingElement(new double[] { 1, 1 }, new double[] { 0 }));
// create multi layer perceptron
MultiLayerPerceptron myMlPerceptron = new MultiLayerPerceptron(TransferFunctionType.TANH, 2, 3, 1);
// learn the training set
Console.WriteLine("Training neural network...");
myMlPerceptron.LearnInSameThread(trainingSet);
// test perceptron
Console.WriteLine("Testing trained neural network");
TestNeuralNetwork(myMlPerceptron, trainingSet);
// save trained neural network
myMlPerceptron.Save("myMlPerceptron.nnet");
// load saved neural network
NeuralNetwork loadedMlPerceptron = NeuralNetwork.Load("myMlPerceptron.nnet");
// test loaded neural network
//Console.WriteLine("Testing loaded neural network");
//testNeuralNetwork(loadedMlPerceptron, trainingSet);
}
/// <summary>
/// Creates and returns a new instance of Multi Layer Perceptron
/// </summary>
/// <param name="layersStr">space separated number of neurons in layers</param>
/// <param name="transferFunctionType">transfer function type for neurons</param>
/// <param name="learningRule">instance of Multi Layer Perceptron</param>
/// <param name="useBias"></param>
/// <param name="connectIO"></param>
/// <returns></returns>
public static MultiLayerPerceptron CreateMLPerceptron(String layersStr, TransferFunctionType transferFunctionType, Type learningRule, bool useBias, bool connectIO)
{
IList<int> layerSizes = VectorParser.ParseInteger(layersStr);
NeuronProperties neuronProperties = new NeuronProperties(transferFunctionType, useBias);
MultiLayerPerceptron nnet = new MultiLayerPerceptron(layerSizes, neuronProperties);
// set learning rule
if (learningRule.Name.Equals(typeof(BackPropagation).Name))
{
nnet.LearningRule = new BackPropagation();
}
else if (learningRule.Name.Equals(typeof(MomentumBackpropagation).Name))
{
nnet.LearningRule = new MomentumBackpropagation();
}
else if (learningRule.Name.Equals(typeof(DynamicBackPropagation).Name))
{
nnet.LearningRule = new DynamicBackPropagation();
}
// connect io
if (connectIO)
{
nnet.ConnectInputsToOutputs();
}
return nnet;
}
/// <summary>
/// Creates and returns a new instance of Multi Layer Perceptron
/// </summary>
/// <param name="layersStr">space separated number of neurons in layers</param>
/// <param name="transferFunctionType">transfer function type for neurons</param>
/// <returns>instance of Multi Layer Perceptron</returns>
public static MultiLayerPerceptron CreateMLPerceptron(String layersStr, TransferFunctionType transferFunctionType)
{
IList<int> layerSizes = VectorParser.ParseInteger(layersStr);
MultiLayerPerceptron nnet = new MultiLayerPerceptron(layerSizes,
transferFunctionType);
return nnet;
}
public void Run()
{
NeuralNetwork network = new MultiLayerPerceptron(TransferFunctionType.SIGMOID, WINDOW_SIZE, 10, 1);
/*if( !FlatNetworkPlugin.flattenNeuralNetworkNetwork(network) )
{
Console.WriteLine("Failed to flatten network.");
}*/
NormalizeSunspots(0.1, 0.9);
network.LearningRule.AddObserver(this);
TrainingSet training = GenerateTraining();
network.LearnInSameThread(training);
Predict(network);
//FlatNetworkPlugin.shutdown();
}