public static void Gate(Neuron from, Neuron to, Neuron gate, double weight)
{
var l = new NeuronLink { From = from, To = to, Weight = weight, Gate = gate };
from.OutProject.Add(l);
to.InProject.Add(l);
if (gate != Neuron.NoGate)
{
gate.OutGate.Add(l);
}
}
public static void Disconnect(Neuron from, Neuron to)
{
from.OutLinks = from.OutLinks.Where(l => l.To != to).ToArray();
to.InLinks = to.InLinks.Where(l => l.From != from).ToArray();
}
public static void Connect(Neuron from, Neuron to)
{
var l = new NeuronLink() { From = from, To = to, Weights = new double[from.Size, to.Size] };
from.OutLinks = from.OutLinks.Union(new[] { l }).ToArray();
to.InLinks = to.InLinks.Union(new[] { l }).ToArray();
}
private static Network Build(int fanIn, int inputs, params int[] layerSizes)
{
var layers = new List<Neuron[]>();
var layer = new List<Neuron>();
for (int i = 0; i < inputs; ++i)
{
layer.Add(new Neuron("L0:" + i));
}
layers.Add(layer.ToArray());
var layerIndex = 0;
foreach (var size in layerSizes)
{
++layerIndex;
layer.Clear();
for (int i = 0; i < size; ++i)
{
var n = new Neuron("L" + layerIndex + ":" + i);
foreach (int j in Lib.Math.NChooseM(layers.Last().Length, fanIn, Hyperparams.Rand))
{
Project(layers.Last()[j], n, Hyperparams.NewLinkWeight());
}
layer.Add(n);
}
layers.Add(layer.ToArray());
}
var res = new Network { Layers = layers.ToArray() };
res.SetNeuronLayers();
return res;
}
public void RemoveNeuron(Neuron n)
{
if (n.Name == Neuron.BiasNeuron.Name || n.Name == Neuron.NoGate.Name) return;
Console.WriteLine(" ### Removed neuron (" + n.Name + ") - Fan: " + n.FanIn() + "x" + n.FanOut() + " ###");
n.InProject.Union(n.OutProject).Union(n.OutGate).Union(n.OutRemember).ToList().ForEach(RemoveLink);
RemoveLink(n.SelfLink);
for (int i = 0; i < Layers.Length; ++i)
{
Layers[i] = Layers[i].Except(new[] { n }).ToArray();
}
Layers = Layers.Where(l => l.Length > 0).ToArray();
SetNeuronLayers();
}
public static Network NewLSTM(int fanIn, bool isLastLayerRegular, int inputs, params int[] layerSizes)
{
var layers = new List<Neuron[]>();
layers.Add(Enumerable.Range(0, inputs).Select(i => new Neuron("L0:" + i)).ToArray());
//var prevSize = layers.Last().Length;
var layerIndex = 0;
foreach (var size in layerSizes.Take(isLastLayerRegular ? layerSizes.Length - 1 : layerSizes.Length))
{
++layerIndex;
var inGates = new List<Neuron>();
var rememberGates = new List<Neuron>();
var memories = new List<Neuron>();
var outGates = new List<Neuron>();
var results = new List<Neuron>();
for (int i = 0; i < size; ++i)
{
var inGate = new Neuron("InGate L" + layerIndex + ":" + i);
var rememberGate = new Neuron("RememberGate L" + layerIndex + ":" + i);
var memoryCell = new Neuron("MemoryCell L" + layerIndex + ":" + i);
var outGate = new Neuron("OutGate L" + layerIndex + ":" + i);
var resultNode = new Neuron("ResultNode L" + layerIndex + ":" + i);
var available = layers.Last().ToList();
available.OrderBy(n => n.FanOut());
var fanSum = available.Sum(x => x.FanOut());
for (int j = 0; j < fanIn && available.Any(); ++j)
{
var avgFan = (double)fanSum / available.Count;
var until = available.FindLastIndex(x => x.FanOut() <= avgFan);
var inputNode = available[Hyperparams.Rand.Next(until + 1)];
available.Remove(inputNode);
fanSum -= inputNode.FanOut();
Project(inputNode, resultNode, Hyperparams.NewLinkWeight());
Project(inputNode, inGate, Hyperparams.NewLinkWeight());
Project(inputNode, rememberGate, Hyperparams.NewLinkWeight());
Project(inputNode, outGate, Hyperparams.NewLinkWeight());
Gate(inputNode, memoryCell, inGate, Hyperparams.NewLinkWeight());
}
Gate(memoryCell, resultNode, outGate, Hyperparams.NewLinkWeight());
Remember(memoryCell, rememberGate);
//Peepholes
//Project(memoryCell, inGate, Hyperparams.NewLinkWeight());
//Project(memoryCell, rememberGate, Hyperparams.NewLinkWeight());
//Project(memoryCell, outGate, Hyperparams.NewLinkWeight());
inGates.Add(inGate);
outGates.Add(outGate);
rememberGates.Add(rememberGate);
memories.Add(memoryCell);
results.Add(resultNode);
}
//foreach(var m1 in memories)
//{
// foreach(var m2 in memories)
// {
// if (m1 == m2) continue;
// Project(m1, m2, Hyperparams.NewLinkWeight());
// }
//}
layers.Add(inGates.Union(rememberGates).Union(outGates).ToArray());
layers.Add(memories.ToArray());
layers.Add(results.ToArray());
}
if (isLastLayerRegular)
{
++layerIndex;
var layer = new List<Neuron>();
for (int i = 0; i < layerSizes.Last(); ++i)
{
var n = new Neuron("OutL" + layerIndex + ":" + i);
foreach (int j in Lib.Math.NChooseM(layers.Last().Length, fanIn, Hyperparams.Rand))
{
Project(layers.Last()[j], n, Hyperparams.NewLinkWeight());
}
layer.Add(n);
}
layers.Add(layer.ToArray());
}
var res = new Network { Layers = layers.ToArray() };
res.SetNeuronLayers();
return res;
}
public void NewRandomNeuron(bool withSelfLink, int fanIn = int.MaxValue)
{
var candidInputs = AllNeurons().ToList();
candidInputs.RemoveAll(n => n.Name == Neuron.BiasNeuron.Name);
candidInputs.RemoveAll(n => n.Name == Neuron.NoGate.Name);
candidInputs.RemoveAll(n => n.Layer == Layers.Length - 1);
candidInputs.RemoveAll(n => n.SelfLink.Gate.Name == Neuron.NoGate.Name);
candidInputs.RemoveAll(n => n.OutProject.Count == 0);
fanIn = Math.Min(candidInputs.Count, fanIn);
var projIn = new List<Neuron>();
candidInputs = candidInputs.OrderBy(n => n.FanOut()).ToList();
var fanSum = candidInputs.Sum(x => x.FanOut());
for (int j = 0; j < fanIn && candidInputs.Any(); ++j)
{
var avgFan = (double)fanSum / candidInputs.Count;
var until = candidInputs.FindLastIndex(x => x.FanOut() <= avgFan);
var inputNode = candidInputs[Hyperparams.Rand.Next(until + 1)];
candidInputs.Remove(inputNode);
fanSum -= inputNode.FanOut();
projIn.Add(inputNode);
}
var baseLayer = projIn.Max(n => n.Layer) + 1;
var newNeurons = new List<Neuron>();
if (withSelfLink)
{
var inGate = new Neuron("InGate L" + baseLayer + ":R") { Layer = baseLayer };
var rememberGate = new Neuron("RememberGate L" + baseLayer + ":R") { Layer = baseLayer };
var memoryCell = new Neuron("MemoryCell L" + (baseLayer + 1) + ":R") { Layer = baseLayer + 1 };
var outGate = new Neuron("OutGate L" + baseLayer + ":R") { Layer = baseLayer };
var resultNode = new Neuron("ResultNode L" + (baseLayer + 2) + ":R") { Layer = baseLayer + 2 };
foreach(var inputNode in projIn)
{
Project(inputNode, resultNode, Hyperparams.NewLinkWeight());
Project(inputNode, inGate, Hyperparams.NewLinkWeight());
Project(inputNode, rememberGate, Hyperparams.NewLinkWeight());
Project(inputNode, outGate, Hyperparams.NewLinkWeight());
Gate(inputNode, memoryCell, inGate, Hyperparams.NewLinkWeight());
}
Gate(memoryCell, resultNode, outGate, Hyperparams.NewLinkWeight());
Remember(memoryCell, rememberGate);
newNeurons.Add(inGate);
newNeurons.Add(outGate);
newNeurons.Add(rememberGate);
newNeurons.Add(memoryCell);
newNeurons.Add(resultNode);
}
else
{
var newNeur = new Neuron("R" + candidInputs.Count) { Layer = baseLayer };
foreach (var pin in projIn)
{
Project(pin, newNeur, Hyperparams.NewLinkWeight());
}
newNeurons.Add(newNeur);
}
foreach (var n in newNeurons)
{
foreach (var pout in Layers.Last())
{
Project(n, pout, Hyperparams.NewLinkWeight());
}
if (n.Layer == Layers.Length - 1)
{
var ll = Layers.Take(Layers.Length - 1).ToList();
ll.Add(new Neuron[] { n });
ll.Add(Layers.Last());
Layers = ll.ToArray();
}
else
{
Layers[n.Layer] = Layers[n.Layer].Union(new Neuron[] { n }).ToArray();
}
}
SetNeuronLayers();
}
public static void Remember(Neuron target, Neuron gate)
{
var l = target.SelfLink;
l.Gate = gate;
l.Weight = 1;
gate.OutRemember.Add(l);
}
public static void Project(Neuron from, Neuron to, double weight)
{
Gate(from, to, Neuron.NoGate, weight);
}
