/// <summary>
/// Connect networks with 0th entry as input
/// </summary>
/// <param name="nets"></param>
public NeuralNetwork(params NeuralNetwork[] nets)
{
int layerCount = 1;
for (int i = 0; i < nets.Length; i++)
{
layerCount += (nets[i].LayerCount - 1);
}
this.layers = new int[layerCount];
this.function = new IActivationFunction[layerCount];
this.lossFunc = nets[0].LossFunction;
this.Optimizer = nets[0].Optimizer;
this.Weights = new Matrix[layerCount];
this.Biases = new Vector[layerCount];
int idx = 1;
for (int i = 0; i < nets.Length; i++)
{
for (int j = 1; j < nets[i].LayerCount; j++)
{
Weights[idx] = nets[i].Weights[j];
Biases[idx] = nets[i].Biases[j];
layers[idx] = nets[i].Layers[j];
function[idx] = nets[i].ActivationFunctions[j];
idx++;
}
}
this.Solver = new GradientSolver(this);
}
public NeuralNetwork(int[] layers, IActivationFunction[] function, ILossFunction loss, IOptimizer optimizer)
{
this.layers = layers;
this.function = function;
this.lossFunc = loss;
this.Optimizer = optimizer;
this.Weights = new Matrix[layers.Length];
this.Biases = new Vector[layers.Length];
int w = layers[0];
Random rng = new Random(0);
for (int i = 1; i < Weights.Length; i++)
{
int h = layers[i];
Weights[i] = new Matrix(w, h);
Biases[i] = new Vector(h);
for (int j = 0; j < h; j++)
{
Biases[i][j] = (float)rng.NextGaussian();// 1e-10f;
for (int k = 0; k < w; k++)
{
Weights[i][k, j] = (float)rng.NextGaussian(0, System.Math.Pow(w, -0.5f));// * 1e-10f;
}
}
w = h;
}
this.Solver = new GradientSolver(this);
}
