public Train Backpropagate(NeuronalNetwork neuronalNetwork,
double[][] input, double[][] ideal, double learningRate, double momentum)
{
this.neuronalNetwork = neuronalNetwork;
this.input = input;
this.ideal = ideal;
this.learningRate = learningRate;
this.momentum = momentum;
return(new Train(this));
}
public FeedforwardNeuronalNetwork(OneHiddenLayerNeuronCounter neuronCounter, int evolutions = 1000, double learningRate = 0.5,
NeuronalNetworkMode neuronalNetworkMode = NeuronalNetworkMode.Standard)
{
this.mode = NeuronalCounterMode.OneHiddenLayer;
this.neuronalNetworkMode = neuronalNetworkMode;
this.neuronCounter = neuronCounter;
this.evolutions = evolutions;
this.learningRate = learningRate;
this.inputValuesCount = neuronCounter.InputNeuronCount;
this.outputValuesCount = neuronCounter.OutputNeuronCount;
neuronalNetwork = NeuronalNetworkModeFactory.CreateInstance(neuronalNetworkMode);
this.neuronalNetwork.NeuronalNetworkMode = neuronalNetworkMode;
this.neuronalNetwork.AddInputLayer(new FeedforwardLayer(activationFunction, neuronCounter.InputNeuronCount, 0));
this.neuronalNetwork.AddHiddenLayer(new FeedforwardLayer(activationFunction, neuronCounter.FirstLayerHiddenNeuronCount, 1));
this.neuronalNetwork.AddOutputLayer(new FeedforwardLayer(activationFunction, neuronCounter.OutputNeuronCount, 2));
this.neuronalNetwork.LearningRate = learningRate;
this.neuronalNetwork.Evolutions = evolutions;
this.neuronalNetwork.RandomFillWeightMatrix();
}
