public static void InitNG(this IEnumerable <IGradientLayerInfo> layerInfos, NeuronNet neuronNet, Random rnd)
{
if (neuronNet.Count < 2)
{
return;
}
int n = neuronNet[0].Inputs.Length, p = 0;
for (int index = 0; index < (neuronNet.Count - 1); index++)
{
p += neuronNet[index].Outputs.Length;
}
double beta = 0.7 * Math.Pow(p, 1.0 / n);
foreach (var info in layerInfos)
{
var layer = info.Layer;
foreach (var neuron in layer)
{
neuron.Bias = (2.0 * rnd.NextDouble() - 1.0) * beta;
var mod = Math.Sqrt(neuron.Weights.Sum(x => x * x));
for (int i = 0; i < neuron.Weights.Length; i++)
{
neuron.Weights[i] = beta * neuron.Weights[i] / mod;
}
}
}
}
public RPropLearning(NeuronNet network)
{
Network = network;
// reverse order
infos = new List <LayerInfo>();
foreach (var layer in network)
{
infos.Insert(0, new LayerInfo(layer));
}
}
