private INeuralModel InitNeuralModel()
{
var model = new NeuralModelBase();
model.defaultWeightInitializer = () => GARandomManager.NextFloat(-1f, 1f);
model.WeightConstraints = new Tuple <float, float>(-10, 10);
var bias = model.AddBiasNeuron();
var layers = new List <Neuron[]>()
{
model.AddInputNeurons(inputs).ToArray(),
model.AddNeurons(
new Neuron(-1, ActivationFunctions.Gaussian)
{
//ValueModifiers = new[] { Dropout.DropoutFunc(0.06f) },
},
count: inputs
).ToArray(),
model.AddOutputNeurons(
inputs,
ActivationFunctions.Sigmoid
).ToArray(),
};
model.ConnectLayers(layers);
model.ConnectBias(bias, layers.Skip(1));
return(model);
}
private INeuralModel Init4InputsNeuralModel()
{
var model = new NeuralModelBase();
model.defaultWeightInitializer = () => GARandomManager.NextFloat(-1, 1);;
model.WeightConstraints = new Tuple <float, float>(-50f, 50f);
var layers = new List <Neuron[]>()
{
model.AddInputNeurons(CartPoleAgent.nbOfInputs).ToArray(),
model.AddOutputNeurons(
1,
ActivationFunctions.TanH
).ToArray(),
};
model.ConnectLayers(layers);
var outputNeuron = layers.Last().Last();
model.AddConnection(outputNeuron.InnovationNb, outputNeuron.InnovationNb);
return(model);
}
protected override INeuralModel InitNeuralModel()
{
var model = new NeuralModelBase();
model.defaultWeightInitializer = () => GARandomManager.NextFloat(-1, 1);
model.WeightConstraints = weightConstraints.ToTuple();
var layers = new List <Neuron[]>()
{
// Inputs
model.AddInputNeurons(
agentPrefab.GetComponent <AgentProxy>().nbOfInputs
).ToArray(),
model.AddNeurons(
new Neuron(-1, ActivationFunctions.TanH),
count: agentPrefab.GetComponent <AgentProxy>().nbOfInputs * 4
).ToArray(),
// Outputs
model.AddOutputNeurons(
agentPrefab.GetComponent <AgentProxy>().nbOfOutputs,
ActivationFunctions.Sigmoid
).ToArray(),
};
model.ConnectLayers(layers);
var outputs = layers.Last();
//model.ConnectNeurons(outputs, outputs);
model.ConnectLayers(new[] { outputs, outputs });
return(model);
}
private INeuralModel InitModel()
{
var model = new NeuralModelBase();
model.defaultWeightInitializer = () => GARandomManager.NextFloat(-1, 1);
model.WeightConstraints = new Tuple <float, float>(-5, 5);
var bias = model.AddBiasNeuron();
var layers = new[]
{
model.AddInputNeurons(1).ToArray(),
model.AddNeurons(
sampleNeuron: new Neuron(-1, ActivationFunctions.Gaussian),
count: 1
).ToArray(),
model.AddOutputNeurons(1, ActivationFunctions.Sigmoid).ToArray()
};
model.ConnectBias(bias, layers.Skip(1));
model.ConnectLayers(layers);
// Adding RNN
//foreach (var neuron in layers[1])
//{
// var mem = model.AddNeurons(
// sampleNeuron: new MemoryNeuron(-1, neuron.InnovationNb),
// count: 1);
// model.AddConnection(mem[0].InnovationNb, neuron.InnovationNb);
//}
// Addin LSTM
var input = layers.First().First();
var output = layers[1].First();
var a = new[] { 1, 2, 3 };
a.Where(x => x == 0)
.GroupBy(x => x)
.ToArray();
model.AddLSTM(out var lstmIn, out var lstmOut, biasNeuron: bias);
model.AddConnection(input, lstmIn);
model.AddConnection(lstmOut, output);
return(model);
}
private INeuralModel Init2InputsNeuralModel()
{
var model = new NeuralModelBase();
model.defaultWeightInitializer = () => GARandomManager.NextFloat(-1, 1);;
model.WeightConstraints = new Tuple <float, float>(-5f, 5f);
var bias = model.AddBiasNeuron();
var layers = new List <Neuron[]>()
{
model.AddInputNeurons(CartPoleAgent.nbOfInputs).ToArray(),
model.AddOutputNeurons(
1,
ActivationFunctions.TanH
).ToArray(),
};
model.ConnectLayers(layers);
var outputNeuron = layers.Last().Last();
//var memNeurons = model.AddNeurons(
// sampleNeuron: new MemoryNeuron(-1, outputNeuron.InnovationNb),
// count: 1
//).ToArray();
// RNN
//var innerMemNeurons = model.AddNeurons(
// sampleNeuron: new Neuron(-1, ActivationFunctions.TanH),
// count: 1).ToArray();
//model.ConnectLayers(new[] { memNeurons, innerMemNeurons });
//model.ConnectLayers(new[] { layers[0], innerMemNeurons, new[] { outputNeuron } });
// LSTM
Neuron lstmIn, lstmOut;
model.AddLSTM(out lstmIn, out lstmOut, biasNeuron: bias);
//model.ConnectNeurons(memNeurons, new[] { lstmIn }).ToArray();
model.ConnectNeurons(layers[0], new[] { lstmIn }).ToArray();
model.ConnectNeurons(new[] { lstmOut }, layers.Last()).ToArray();
return(model);
}
private INeuralModel InitModel()
{
var model = new NeuralModelBase();
model.defaultWeightInitializer = () => GARandomManager.NextFloat(-3, 3);
model.WeightConstraints = new Tuple <float, float>(-20, 20);
var bias = model.AddBiasNeuron();
var layers = new[]
{
model.AddInputNeurons(2).ToArray(),
model.AddNeurons(
sampleNeuron: new Neuron(-1, ActivationFunctions.Gaussian),
count: 1
).ToArray(),
model.AddOutputNeurons(1, ActivationFunctions.Sigmoid).ToArray()
};
model.ConnectBias(bias, layers.Skip(1));
model.ConnectLayers(layers);
return(model);
}
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;
}
protected override INeuralModel InitNeuralModel()
{
var model = new NeuralModelBase();
model.defaultWeightInitializer = () => GARandomManager.NextFloat(-1, 1);
model.WeightConstraints = new Tuple <float, float>(
weightConstraints.x,
weightConstraints.y
);
var bias = model.AddBiasNeuron();
var layers = new List <Neuron[]>()
{
// Inputs
model.AddInputNeurons(
agentPrefab.GetComponent <AgentProxy>().nbOfInputs
).ToArray(),
//model.AddNeurons(
// new Neuron(-1, ActivationFunctions.TanH),
// count: 4
//).ToArray(),
// Outputs
model.AddOutputNeurons(
agentPrefab.GetComponent <AgentProxy>().nbOfOutputs,
ActivationFunctions.Sigmoid
).ToArray(),
};
model.ConnectLayers(layers);
model.ConnectBias(bias, layers.Skip(1));
//var memoryNeurons = new List<Neuron>();
//foreach (var neuron in layers.Last())
//{
// var mem = model.AddNeurons(
// sampleNeuron: new MemoryNeuron(-1, neuron.InnovationNb),
// count: 1
// );
// memoryNeurons.Add(mem[0]);
//}
//var memoryProcLayer1 = model.AddNeurons(
// new Neuron(-1, ActivationFunctions.TanH),
// count: 4
//).ToArray();
//var memoryProcLayer2 = model.AddNeurons(
// new Neuron(-1, ActivationFunctions.TanH),
// count: 4
// ).ToArray();
//model.ConnectLayers(
// new Neuron[][]
// {
// memoryNeurons.ToArray(),
// memoryProcLayer1,
// memoryProcLayer2,
// layers.Last()
// }
//);
Neuron lstmIn, lstmOut;
model.AddLSTM(out lstmIn, out lstmOut, biasNeuron: bias);
model.ConnectNeurons(layers[0], new[] { lstmIn }).ToArray();
model.ConnectNeurons(new[] { lstmOut }, layers.Last()).ToArray();
return(model);
}
