NeuralNetwork

Open neuron network with extensible architecture and status logging.

Basic network principles

1. Network type is Feedforward
2. Use sygmoid activation function
3. Use backpropagation type for training
4. Start Neuron newtwork parameters define in App.config
5. Start synapses values are random number. Values range define in App.config.

WARNINGS:

First layer is ONLY input neurons

Last layer is ONLY output neurons

In other case neuron network result will be uncorrect or network will be crash.

Config parameters

Parameter	type
SPEED	double value
MOMENT	double value
INPUT_NEURONS_COUNT	integer value
HIDDEN_LAYER_COUNT	integer value
HIDDEN_NEURONS_ON_LAYERS_COUNT	string with format "1,2,3"
OUT_NEURONS_COUNT	integer value
USAGE_BIAS	bool value "true" or "false"
MAX_SYNAPS_WEIGHT	double value
MIN_SYNAPS_WEIGHT	double value

Quick start

usage example

private void MainActivity_Load(object sender, EventArgs e)
{
    /**
    * Use sample 
    * */
    NetworkInfo info = new NetworkInfo(NetworkInfo.CONSOLE_OUTPUT);

    NeuronNetwork network = new NeuronNetwork();

    info.displayInfo(info.getCurrentNetworkState(network));

    TrainingController controller = new TrainingController();
    controller.trainNetwork(ref network, new string[] { "trainingSet.txt" });

    double[] rez = network.execute(new double[] { 0, 0 });

    info.displayInfo(info.getCurrentNetworkState(network));
}

Author

@lindlind and @vovaksenov99