public Simulation CreateSimulation()
{
var activationFunction = CreateActivationFunction();
var neuron = CreateNeuron(activationFunction);
INeuralNetwork neuralNetwork = null;
if (Network == "NeuralNetwork")
{
var weightInitializer = new WeightInitializer(Weights.Min, Weights.Max);
neuralNetwork = new NeuralNetwork(neuron, Inputs, Outputs, weightInitializer, HiddenNeurons);
}
else if (Network == "SOMNetwork")
{
neuralNetwork = new SOMNetwork(neuron, Inputs, Outputs, MaxEpoch);
}
var simulation = new Simulation(neuralNetwork, true);
simulation.ValidationData = ValidationData;
simulation.ImagesDisturbanceProbability = ImagesDisturbanceProbability;
simulation.ImageDisturbanceMaxDifference = ImageDisturbanceMaxDifference;
simulation.MaxEpoch = MaxEpoch;
simulation.Config = this;
return(simulation);
}
public NeuralModelBase()
{
synapseInnovNbTracker = new SynapseInnovNbTracker();
Neurons = new Dictionary <InnovationNumber, Neuron>();
Synapses = new Dictionary <Synapse, WeightInitializer>();
this.defaultWeightInitializer = () => GARandomManager.NextFloat(-1f, 1f);
}
public Synapse AddConnection(
Neuron startNeuron,
Neuron endNeuron,
WeightInitializer weightInitializer = null)
{
return(AddConnection(
startNeuron.InnovationNb,
endNeuron.InnovationNb,
weightInitializer
));
}
public void ConnectBias(
BiasNeuron bias,
IEnumerable <IEnumerable <Neuron> > layers,
WeightInitializer weightInitializer = null)
{
var biasLayer = new[] { bias };
foreach (var layer in layers)
{
ConnectNeurons(biasLayer, layer, weightInitializer).ToArray();
}
}
public void ConnectLayers(
IEnumerable <IEnumerable <Neuron> > layers,
WeightInitializer weightInitializer = null)
{
var prevLayer = layers.First();
foreach (var layer in layers.Skip(1))
{
ConnectNeurons(prevLayer, layer, weightInitializer).ToArray();
prevLayer = layer;
}
}
public IEnumerable <Synapse> ConnectNeurons(
IEnumerable <Neuron> group1,
IEnumerable <Neuron> group2,
WeightInitializer weightInitializer = null)
{
foreach (var neuron1 in group1)
{
foreach (var neuron2 in group2)
{
yield return(AddConnection(
neuron1.InnovationNb,
neuron2.InnovationNb,
weightInitializer ?? defaultWeightInitializer));
}
}
}
public Synapse AddConnection(
InnovationNumber startNeuron,
InnovationNumber endNeuron,
WeightInitializer weightInitializer = null)
{
if (!Neurons.ContainsKey(startNeuron) || !Neurons.ContainsKey(endNeuron))
{
throw new Exception("The given neurons are not yer registered.");
}
var innov = synapseInnovNbTracker.GetHystoricalMark(startNeuron, endNeuron);
var result = new Synapse(innov, 0, startNeuron, endNeuron)
{
weightConstraints = this.WeightConstraints
};
if (weightInitializer == null)
{
weightInitializer = defaultWeightInitializer;
}
Synapses.Add(result, weightInitializer);
return(result);
}
public static void AddLSTM(
this NeuralModelBase model,
out Neuron input,
out Neuron output,
BiasNeuron biasNeuron = null,
WeightInitializer weightInitializer = null,
string groupName = "LSTM")
{
var concatNeur = model.AddNeuron(
sampleNeuron: new Neuron(-1, null)
);
// Multiply Gate
var sigmoid1 = model.AddNeuron(
sampleNeuron: new Neuron(-1, ActivationFunctions.Sigmoid)
);
model.AddConnection(concatNeur, sigmoid1, weightInitializer)
.isTransferConnection = true;
var multiplyGate = model.AddNeuron(
sampleNeuron: new Neuron(-1, null)
{
ValueCollector = new MultValueCollector()
}
);
model.AddConnection(sigmoid1, multiplyGate, weightInitializer);
// Addition gate
var sigmoid2 = model.AddNeuron(
sampleNeuron: new Neuron(-1, ActivationFunctions.Sigmoid)
);
model.AddConnection(concatNeur, sigmoid2, weightInitializer)
.isTransferConnection = true;
var tanh = model.AddNeuron(
sampleNeuron: new Neuron(-1, ActivationFunctions.TanH)
);
model.AddConnection(concatNeur, tanh, weightInitializer)
.isTransferConnection = true;
var sigmoidAndTanhMultGate = model.AddNeuron(
sampleNeuron: new Neuron(-1, null)
{
ValueCollector = new MultValueCollector()
}
);
model.AddConnection(sigmoid2, sigmoidAndTanhMultGate, weightInitializer);
model.AddConnection(tanh, sigmoidAndTanhMultGate, weightInitializer);
var additionGate = model.AddNeuron(new Neuron(-1, null));
model.AddConnection(multiplyGate, additionGate, weightInitializer)
.isTransferConnection = true;
model.AddConnection(sigmoidAndTanhMultGate, additionGate, weightInitializer)
.isTransferConnection = true;
// Tanh gate
var sigmoid3 = model.AddNeuron(
sampleNeuron: new Neuron(-1, ActivationFunctions.Sigmoid)
);
model.AddConnection(concatNeur, sigmoid3, weightInitializer)
.isTransferConnection = true;
var finalMult = model.AddNeuron(
sampleNeuron: new Neuron(-1, null)
{
ValueCollector = new MultValueCollector()
}
);
var tanhGate = model.AddNeuron(
sampleNeuron: new Neuron(-1, ActivationFunctions.TanH)
);
model.AddConnection(additionGate, tanhGate, weightInitializer)
.isTransferConnection = true;
model.AddConnection(sigmoid3, finalMult, weightInitializer);
model.AddConnection(tanhGate, finalMult, weightInitializer)
.isTransferConnection = true;
// Adding memory neurons
var finalMultMem = model.AddNeuron(
sampleNeuron: new MemoryNeuron(-1, finalMult.InnovationNb)
);
model.AddConnection(finalMultMem, concatNeur, weightInitializer)
.isTransferConnection = true;
var cellStateMem = model.AddNeuron(
sampleNeuron: new MemoryNeuron(-1, additionGate.InnovationNb)
);
model.AddConnection(cellStateMem, multiplyGate, weightInitializer)
.isTransferConnection = true;
// Connecting bias
if (biasNeuron != null)
{
model.AddConnection(biasNeuron, sigmoid1);
model.AddConnection(biasNeuron, sigmoid2);
model.AddConnection(biasNeuron, tanh);
model.AddConnection(biasNeuron, sigmoid3);
}
// Assign neuron group
concatNeur.group = groupName;
sigmoid1.group = groupName;
sigmoid2.group = groupName;
sigmoid3.group = groupName;
tanh.group = groupName;
additionGate.group = groupName;
multiplyGate.group = groupName;
tanhGate.group = groupName;
sigmoidAndTanhMultGate.group = groupName;
finalMult.group = groupName;
cellStateMem.group = groupName;
finalMultMem.group = groupName;
// Assigning out's
input = concatNeur;
output = finalMult;
}
public void SetUp()
{
_weightSetterMock = new Mock <IWeightSetter>();
_network = new NetworkBuilder().Setup();
_weightInitializer = new WeightInitializer(_network.GetNetwork(), weightSetter: _weightSetterMock.Object);
}