/// <summary>
/// Copies this NeuralLayer including its weights.
/// </summary>
/// <returns>A deep copy of this NeuralLayer</returns>
public NeuralLayer DeepCopy()
{
//Copy weights
var copiedWeights = new float[Weights.GetLength(0), Weights.GetLength(1)];
for (var x = 0; x < Weights.GetLength(0); x++)
{
for (var y = 0; y < Weights.GetLength(1); y++)
{
copiedWeights[x, y] = Weights[x, y];
}
}
//Create copy
var newLayer = new NeuralLayer(NeuronCount, OutputCount);
newLayer.Weights = copiedWeights;
newLayer.NeuronActivationFunctionType = NeuronActivationFunctionType;
return(newLayer);
}
/// <summary>
/// Initialises a new fully connected feedforward neural network with given topology.
/// </summary>
/// <param name="topology">
/// An array of unsigned integers representing the node count of each layer from input to output
/// layer.
/// </param>
public NeuralNetwork(ActivationFunctionType activationFunction, params uint[] topology)
{
Topology = topology;
//Calculate overall weight count
WeightCount = 0;
for (var i = 0; i < topology.Length - 1; i++)
{
WeightCount += (int)((topology[i] + 1) * topology[i + 1]); // + 1 for bias node
}
//Initialise layers
Layers = new NeuralLayer[topology.Length - 1];
for (var i = 0; i < Layers.Length; i++)
{
Layers[i] = new NeuralLayer(topology[i], topology[i + 1])
{
NeuronActivationFunctionType = activationFunction
}
}
;
}
