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 static void PrintGraph(Network ntwk)
{
var ns = ntwk.AllNeurons().ToList();
var ls = ntwk.AllLinks().ToList();
var maxWeight = ls.Max(l => Math.Abs(l.Weight));
var s = "CREATE ";
var ss = new List<string>();
ns.RemoveAll(n => n.Name == Neuron.NoGate.Name);
ns.RemoveAll(n => n.Name == Neuron.BiasNeuron.Name);
foreach (var n in ns)
{
var lbl = n.OutGate.Any() ? "GateNeuron" : n.OutRemember.Any() ? "RememberNeuron" : n.SelfLink.Weight != 0.0 ? "MemoryNeuron" : "Neuron";
var biasL = n.InProject.FirstOrDefault(l => l.From == Neuron.BiasNeuron);
var bias = null == biasL ? 0.0 : biasL.Weight;
ss.Add("(n" + ns.IndexOf(n) + ":" + lbl + " {Name:'" + n.Name + "', State:'" + n.State + "', Bias:'" + bias + "', Activation:'" + n.Activation + "', Gate:'" + n.GateActivation + "'})");
if (n.SelfLink.Gate.Name != Neuron.NoGate.Name)
{
ls.Add(n.SelfLink);
}
}
foreach (var l in ls)
{
if (l.From.Name == Neuron.BiasNeuron.Name) continue;
var lbl = "";
if (Math.Abs(l.Weight) / maxWeight > 6.4) { lbl = "Link7"; }
else if (Math.Abs(l.Weight) / maxWeight > 3.2) { lbl = "Link6"; }
else if (Math.Abs(l.Weight) / maxWeight > 1.6) { lbl = "Link5"; }
else if (Math.Abs(l.Weight) / maxWeight > 0.8) { lbl = "Link4"; }
else if (Math.Abs(l.Weight) / maxWeight > 0.4) { lbl = "Link3"; }
else if (Math.Abs(l.Weight) / maxWeight > 0.2) { lbl = "Link2"; }
else if (Math.Abs(l.Weight) / maxWeight > 0.1) { lbl = "Link1"; }
else { lbl = "Link0"; }
if (l.Weight < 0) { lbl = "Neg" + lbl; }
if (l.Gate.Name == Neuron.NoGate.Name)
{
ss.Add("(n" + ns.IndexOf(l.From) + ")-[:" + lbl + " {Weight:'" + l.Weight + "'}]->(n" + ns.IndexOf(l.To) + ")");
}
else if (l.To == l.From)
{
ss.Add("(n" + ns.IndexOf(l.Gate) + ")-[:RememberLink {Weight:'" + l.Weight + "'}]->(n" + ns.IndexOf(l.To) + ")");
}
else
{
ss.Add("(g" + ls.IndexOf(l) + ":Gate)");
ss.Add("(n" + ns.IndexOf(l.From) + ")-[:Link]->(g" + ls.IndexOf(l) + ")");
ss.Add("(n" + ns.IndexOf(l.Gate) + ")-[:GateLink]->(g" + ls.IndexOf(l) + ")");
ss.Add("(g" + ls.IndexOf(l) + ")-[:" + lbl + " {Weight:'" + l.Weight + "'}]->(n" + ns.IndexOf(l.To) + ")");
}
}
s += string.Join(", \n", ss);
/*
* create
* (x0:Neuron {State:'1.2345457568', Activation:'0.957983745', Gate:'0.34857638576345'}),
* (x1:Neuron {State:'1.2345457568', Activation:'0.957983745', Gate:'0.34857638576345'}),
* (y0:Link {weight:'-0.82345457568'}),
* (x0)-[:Flow]->(y0)-[:Flow]->(x1)
*
*/
using (var driver = GraphDatabase.Driver("bolt://localhost", AuthTokens.Basic("neo4j", "Aa1234567")))
using (var session = driver.Session())
{
session.Run(s);
//var result = session.Run("MATCH (a:Person) WHERE a.name = 'Arthur' RETURN a.name AS name, a.title AS title");
//foreach (var record in result)
//{
// Console.WriteLine($"{record["title"].As<string>()} {record["name"].As<string>()}");
//}
}
}
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;
}
