/// <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(params uint[] topology) { this.Topology = topology; //Calculate overall weight count WeightCount = 0; for (int i = 0; i < topology.Length - 1; i++) { WeightCount += (int)((topology[i] + 1) * topology[i + 1]); // + 1 for bias node } UnityEngine.Debug.Log("weight count = " + WeightCount); //Initialise layers Layers = new NeuralLayer[topology.Length - 1]; for (int i = 0; i < Layers.Length; i++) { Layers[i] = new NeuralLayer(topology[i], topology[i + 1]); } }
/// <summary> /// Copies this NeuralLayer including its weights. /// </summary> /// <returns>A deep copy of this NeuralLayer</returns> public NeuralLayer DeepCopy() { //Copy weights double[,] copiedWeights = new double[this.Weights.GetLength(0), this.Weights.GetLength(1)]; for (int x = 0; x < this.Weights.GetLength(0); x++) { for (int y = 0; y < this.Weights.GetLength(1); y++) { copiedWeights[x, y] = this.Weights[x, y]; } } //Create copy NeuralLayer newLayer = new NeuralLayer(this.NeuronCount, this.OutputCount); newLayer.Weights = copiedWeights; newLayer.NeuronActivationFunction = this.NeuronActivationFunction; return(newLayer); }