void Create()
{
network = new NeuralNetwork.NeuralNetwork(input.Length, layers);
if (genetic)
learning = new GeneticLearningAlgorithm(network);
else
learning = new BackPropagationLearningAlgorithm(network);
network.randomizeAll();
network.LearningAlg = learning;
}
void Create()
{
network = new NeuralNetwork.NeuralNetwork(input.Length, layers);
if (genetic)
{
learning = new GeneticLearningAlgorithm(network);
}
else
{
learning = new BackPropagationLearningAlgorithm(network);
}
network.randomizeAll();
network.LearningAlg = learning;
}
/// <summary>
/// Create a new neural network
/// with "inputs" inputs and size of "layers"
/// layers of neurones.
/// The layer i is made with layers_desc[i] neurones.
/// The activation function of each neuron is set to n_act.
/// The lerning algorithm is set to default (Back Propagation).
/// </summary>
/// <param name="inputs">Number of inputs of the network</param>
/// <param name="layers_desc">Number of neurons for each layer of the network</param>
/// <param name="n_act">Activation function for each neuron in the network</param>
public NeuralNetwork(int inputs, int[] layers_desc, ActivationFunction n_act)
{
if (layers_desc.Length < 1)
{
throw new Exception("PERCEPTRON : cannot build perceptron, it must have at least 1 layer of neurone");
}
if (inputs < 1)
{
throw new Exception("PERCEPTRON : cannot build perceptron, it must have at least 1 input");
}
la = new BackPropagationLearningAlgorithm(this);
ni = inputs;
layers = new Layer[layers_desc.Length];
layers[0] = new Layer(layers_desc[0], ni);
for (int i = 1; i < layers_desc.Length; i++)
{
layers[i] = new Layer(layers_desc[i], layers_desc[i - 1], n_act);
}
}
/// <summary>
/// Create a new neural network
/// with "inputs" inputs and size of "layers"
/// layers of neurones.
/// The layer i is made with layers_desc[i] neurones.
/// The activation function of each neuron is set to default (Sigmoid with beta = 1).
/// The lerning algorithm is set to default (Back Propagation).
/// </summary>
/// <param name="inputs">Number of inputs of the network</param>
/// <param name="layers_desc">Number of neurons for each layer of the network</param>
public NeuralNetwork(int inputs, int[] layers_desc)
{
if (layers_desc.Length<1)
throw new Exception("PERCEPTRON : cannot build perceptron, it must have at least 1 layer of neurone");
if (inputs<1)
throw new Exception("PERCEPTRON : cannot build perceptron, it must have at least 1 input");
la = new BackPropagationLearningAlgorithm(this);
ni = inputs;
ActivationFunction n_act = new SigmoidActivationFunction();
layers = new Layer[layers_desc.Length];
layers[0] = new Layer(layers_desc[0], ni);
for(int i=1; i<layers_desc.Length; i++)
layers[i] = new Layer(layers_desc[i],layers_desc[i-1],n_act);
}