//Constructs a layer of neurons and initializes them with the number of weights and the activation function to be uses.
public NeuronLayer(int Neurons, int WeightsPerNeuron, Neuron.ActivationFunction ActivationFunction)
{
int idx = 0;
Neuron n = null;
for (idx = 0; idx <= Neurons - 1; idx++) {
n = new Neuron(WeightsPerNeuron, ActivationFunction);
List.Add(n);
}
mWeightsPerNeuron = WeightsPerNeuron;
}
//Creates a neural network.
//Inputs: The number of neurons in the input layer
//Outputs: The number of neurons in the output layer
//HiddenLayers: The number of Hidden layers in the network. This can be 0 or greater.
//NeuronsPerHiddenLayer: The number of neurons in each hidden layer.
//InputWeightSize: The number of weights in the input layer. The weights size for each layer above is the number of neurons in the previous layer.
//ActivationFunction: The activation function to use in the neuron.
public NeuralNetwork(int Inputs, int Outputs, int HiddenLayers, int NeuronsPerHiddenLayer, int InputWeightSize, Neuron.ActivationFunction Activation)
{
mInputLayer = new NeuronLayer(Inputs, InputWeightSize + 1, Activation);
mLayers = new ArrayList();
int idx = 0;
int LastLayerSize = Inputs;
for (idx = 0; idx <= HiddenLayers - 1; idx++) {
mLayers.Add(new NeuronLayer(NeuronsPerHiddenLayer, LastLayerSize + 1, Activation));
LastLayerSize = NeuronsPerHiddenLayer;
}
mOutputLayer = new NeuronLayer(Outputs, LastLayerSize + 1, Activation);
}