private float TrainNetwork(INetworkRunContext context, IEnumerable <float> inputs, float target)
{
context.Set(inputs.Take(context.InputCount));
context.Target[0] = target;
neuralNetwork.Train(context);
neuralNetwork.Update();
return(context.TotalError);
}
public NeuralNetworkFilter(string name) : base(name, "TRAIN", "OUT", "ERR")
{
IActivationFunction activationFunction = new NeuralNetwork.Nodes.Activations.Tanh();
neuralNetwork.SetInputs(Globals.SPECTRUMRES * HISTORY_SIZE);
neuralNetwork.AddLayer(activationFunction, 4);
neuralNetwork.AddLayer(activationFunction, 1);
//neuralNetwork.LearningRate = 0.02f;
neuralNetwork.Momentum = 0.25f;
runContext = neuralNetwork.GetNewContext();
trainingContext = neuralNetwork.GetNewContext();
}
