/// <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 learn.
/// </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="activationFunc">Activation function for each neuron in the network</param>
/// <param name="learnAlg">Learning algorithm to be used by the neural network</param>
public Network(int inputs, int[] layers_desc, IActivationFunction activationFunc, LearningAlgorithm learnAlg)
{
if (layers_desc.Length < 1)
{
throw new Exception("PERCEPTRON : cannot build perceptron, it must have at least 1 layer of neurons");
}
if (inputs < 1)
{
throw new Exception("PERCEPTRON : cannot build perceptron, it must have at least 1 input");
}
learningAlg = learnAlg;
numInputs = inputs;
layers = new Layer[layers_desc.Length];
layers[0] = new Layer(layers_desc[0], numInputs);
for (int i = 1; i < layers_desc.Length; i++)
{
layers[i] = new Layer(layers_desc[i], layers_desc[i - 1], activationFunc);
}
}
/// <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 Network(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");
}
learningAlg = new BackPropagationLearningAlgorithm(this);
numInputs = inputs;
IActivationFunction n_act = new SigmoidActivation();
layers = new Layer[layers_desc.Length];
layers[0] = new Layer(layers_desc[0], numInputs);
for (int i = 1; i < layers_desc.Length; i++)
{
layers[i] = new Layer(layers_desc[i], layers_desc[i - 1], n_act);
}
}
