public static NeuralNetwork GetBrain()
{
if (_brain == null)
{
NeuralNetworkConfig nnConfig = new NeuralNetworkConfig();
nnConfig.AddLayer(LayerType.Input, 5); //Vision inouts
nnConfig.AddLayer(LayerType.Intermediate, 12, ActivationType.LeakyRelu);
nnConfig.AddLayer(LayerType.Output, 5, ActivationType.LeakyRelu); //Movement decisions.
_brain = new NeuralNetwork(nnConfig, 0.02f);
for (int i = 0; i < 10000; i++)
{
//Left side detection, go right.
_brain.BackPropagate(TrainingScenerio(1, 0, 0, 0, 0), TrainingDecision(1, 1, 1, 1, 0));
_brain.BackPropagate(TrainingScenerio(0, 1, 0, 0, 0), TrainingDecision(1, 1, 1, 1, 0));
_brain.BackPropagate(TrainingScenerio(1, 1, 0, 0, 0), TrainingDecision(1, 1, 1, 1, 0));
//Right side detection, go left.
_brain.BackPropagate(TrainingScenerio(0, 0, 0, 0, 1), TrainingDecision(1, 0, 1, 1, 0));
_brain.BackPropagate(TrainingScenerio(0, 0, 0, 1, 0), TrainingDecision(1, 0, 1, 1, 0));
_brain.BackPropagate(TrainingScenerio(0, 0, 0, 1, 1), TrainingDecision(1, 0, 1, 1, 0));
//Front side detection, so left or right.
_brain.BackPropagate(TrainingScenerio(0, 0, 1, 0, 0), TrainingDecision(1, 0, 1, 1, 0));
_brain.BackPropagate(TrainingScenerio(0, 1, 1, 1, 0), TrainingDecision(1, 1, 1, 1, 0));
_brain.BackPropagate(TrainingScenerio(1, 1, 1, 1, 1), TrainingDecision(1, 1, 1, 1, 0));
//No objects dection, speed up and cruise.
_brain.BackPropagate(TrainingScenerio(0, 0, 0, 0, 0), TrainingDecision(0.4f, 0.4f, 0.4f, 0.9f, 0.9f));
}
}
return _brain.Clone();
}
private static void Start()
{
NeuralNetworkConfig nnConfig = new NeuralNetworkConfig();
nnConfig.AddLayer(LayerType.Input, 3);
nnConfig.AddLayer(LayerType.Intermediate, 5, ActivationType.LeakyRelu);
nnConfig.AddLayer(LayerType.Output, 1, ActivationType.LeakyRelu);
nn = new NeuralNetwork(nnConfig, 0.01f);
nn.Load("C:\\network.txt");
//Train the network
for (int i = 0; i < 20000; i++)
{
nn.BackPropagate(new float[] { 0, 0, 0 }, new float[] { 0 });
nn.BackPropagate(new float[] { 1, 0, 0 }, new float[] { 1 });
nn.BackPropagate(new float[] { 0, 1, 0 }, new float[] { 1 });
nn.BackPropagate(new float[] { 0, 0, 1 }, new float[] { 1 });
nn.BackPropagate(new float[] { 0, 0, 1 }, new float[] { 0 });
nn.BackPropagate(new float[] { 0, 0, 1 }, new float[] { 1 });
nn.BackPropagate(new float[] { 0, 0, 1 }, new float[] { 1 });
nn.BackPropagate(new float[] { 0, 0, 1 }, new float[] { 0 });
nn.BackPropagate(new float[] { 1, 1, 0 }, new float[] { 1 });
nn.BackPropagate(new float[] { 0, 1, 1 }, new float[] { 1 });
nn.BackPropagate(new float[] { 1, 0, 1 }, new float[] { 1 });
nn.BackPropagate(new float[] { 1, 1, 1 }, new float[] { 1 });
}
Console.WriteLine($"Cost: {nn.cost:0.########}");
nn.Save("C:\\network.txt");
VerboseFeedForward(nn, new float[] { 0, 0, 0 });
VerboseFeedForward(nn, new float[] { 1, 0, 0 });
VerboseFeedForward(nn, new float[] { 0, 1, 0 });
VerboseFeedForward(nn, new float[] { 0, 0, 1 });
VerboseFeedForward(nn, new float[] { 1, 1, 0 });
VerboseFeedForward(nn, new float[] { 0, 1, 1 });
VerboseFeedForward(nn, new float[] { 1, 0, 1 });
VerboseFeedForward(nn, new float[] { 1, 1, 1 });
//We want the gate to simulate 3 input or gate (A or B or C)
// 0 0 0 => 0
// 1 0 0 => 1
// 0 1 0 => 1
// 0 0 1 => 1
// 1 1 0 => 1
// 0 1 1 => 1
// 1 0 1 => 1
// 1 1 1 => 1
}