public NeuralLayer(int numNeurons, int inputs, NeuralNetwork net)
{
Neurons = new Neuron[numNeurons];
NumInputs = inputs;
for(int i = 0; i < numNeurons; i++)
{
Neurons[i] = new Neuron(inputs, net);
}
//Neurons[0].Debug = true;
}
public void SetNetwork(NeuralNetwork net)
{
Network = net;
}
public OutputNeuralLayer(int outputs, int inputs, NeuralNetwork net)
: base(outputs, inputs, net)
{
Neurons[0].Debug = false;
}
public Neuron(int inputs, NeuralNetwork net)
{
Network = net;
Weights = new double[inputs];
deltas = new double[inputs];
for (int i = 0; i < inputs; i++)
{
Weights[i] = NeuralNetwork.Rand.NextDouble()*4-2;
}
Bias = NeuralNetwork.Rand.NextDouble()*4-2;
}
public override void SetNetwork(NeuralNetwork net)
{
return;
}
public virtual void SetNetwork(NeuralNetwork net)
{
Neurons.ForEach(n => n.SetNetwork(net));
}
public InputNeuralLayer(int inputs, NeuralNetwork net)
: base(0, inputs, net)
{
}
private void runner(object state)
{
int id = (int)state;
do
{
NeuralNetwork.NetworkParameters param;
lock (ParametersEnumerator)
{
if (!ParametersEnumerator.MoveNext())
{
// Done
return;
}
param = ParametersEnumerator.Current;
Console.WriteLine(string.Format("Id {4} Starting an optimization run : L={0} N={1} D={2} R={3}", param.Layers, param.NeuronsPerLayers, param.MaxDepth, param.TrainingRate, id));
}
NeuralNetwork network = new NeuralNetwork(param);
var results = network.TrainBatch(TrainingSet.Item1.ToList(), TrainingSet.Item2.ToList(), s => Console.WriteLine("Id "+ id + " " + s));
// Check against bests
lock (bestRuns)
{
Console.WriteLine(string.Format("Optimisation for {0} is done in {1}s. Results => MSE={2:.00}% // Success={5:.00} ({3}/{4})", id, results.duration.TotalSeconds, results.mse*100, results.success, results.count, ((double)results.success/results.count)*100));
if (bestRuns.Count < bestRuns.Capacity)
{
bestRuns.Add(Tuple.Create(network, results, param));
}
else
{
for(int i = 0; i < bestRuns.Count; i++)
{
if (bestRuns[i].Item2.success < results.success)
{
bestRuns[i] = Tuple.Create(network, results, param);
}
}
}
break;
}
} while (true);
}
static void Main(string[] args)
{
if (args.Length < 2)
{
Environment.Exit(1);
return;
}
var test = extractPlayers(args[1]);
//Console.Out.WriteLine(allPlayers.Count);
var serializer = new XmlSerializer(typeof(SerializeNetwork));
if (args[0].EndsWith(".xml"))
{
// Training is a xml backup
var load = File.OpenRead(args[0]);
var deserialized = (SerializeNetwork)serializer.Deserialize(load);
load.Close();
network = deserialized.network;
mean = deserialized.means;
stdev = deserialized.stdev;
network.Checker = checker;
network.SetNetwork();
}
else
{
var training = extractPlayers(args[0]);
network = Train(training);
}
Test(test);
var save = File.Create("network.xml");
serializer.Serialize(save, new SerializeNetwork { network = network, means = mean, stdev = stdev });
save.Close();
while(Console.ReadLine() != "stop");
}
public void SetActualNetworkFromNetworkDetails()
{
this.actualNetwork = ucNetworkDetails.GetNetwork();
}
