public static void Run(string[] args)
{
// -clusterid $(Cluster) -processid $(Process) -brain Breakout.brain -factor 0.5
int clusterId = 0;
int processId = 0;
double discountFactor = 0.6;
string breakoutBrainFilePath = "";
OptionSet options = new OptionSet()
.Add("clusterid=", v => clusterId = Int32.Parse(v))
.Add("processid=", v => processId = Int32.Parse(v))
.Add("factor=", v => discountFactor = Double.Parse(v, CultureInfo.InvariantCulture))
.Add("brain=", v => breakoutBrainFilePath = Path.GetFullPath(v));
try
{
options.Parse(Environment.GetCommandLineArgs().Skip(1));
}
catch (OptionException e)
{
MyLog.ERROR.WriteLine(e.Message);
}
MyProjectRunner runner = new MyProjectRunner(MyLogLevel.DEBUG);
StringBuilder result = new StringBuilder();
runner.OpenProject(breakoutBrainFilePath);
runner.DumpNodes();
runner.SaveOnStop(23, true);
for (int i = 0; i < 5; ++i)
{
runner.RunAndPause(1000, 100);
float[] data = runner.GetValues(23, "Bias");
MyLog.DEBUG.WriteLine(data[0]);
MyLog.DEBUG.WriteLine(data[1]);
result.AppendFormat("{0}: {1}, {2}", i, data[0], data[1]);
runner.Set(23, typeof(MyQLearningTask), "DiscountFactor", discountFactor);
runner.RunAndPause(1000, 300);
data = runner.GetValues(23, "Bias");
MyLog.DEBUG.WriteLine(data[0]);
MyLog.DEBUG.WriteLine(data[1]);
result.AppendFormat(" --- {0}, {1}", data[0], data[1]).AppendLine();
runner.Reset();
}
string resultFilePath = @"res." + clusterId.ToString() + "." + processId.ToString() + ".txt";
File.WriteAllText(resultFilePath, result.ToString());
string brainzFilePath = @"state." + clusterId.ToString() + "." + processId.ToString() + ".brainz";
runner.SaveProject(brainzFilePath);
runner.Shutdown();
return;
}
public void CreatesAndRunsMNIST()
{
using (var runner = new MyProjectRunner())
{
MyProject project = runner.CreateProject(typeof(MyMNISTWorld), "MNIST");
MyWorld world = project.World;
var neuralGroup = project.CreateNode<MyNeuralNetworkGroup>();
project.Network.AddChild(neuralGroup);
var hiddenLayer = project.CreateNode<MyHiddenLayer>();
neuralGroup.AddChild(hiddenLayer);
var outputLayer = project.CreateNode<MyOutputLayer>();
neuralGroup.AddChild(outputLayer);
var accumulator = project.CreateNode<MyAccumulator>();
neuralGroup.AddChild(accumulator);
// Connect the nodes.
project.Connect(project.Network.GroupInputNodes[0], neuralGroup, 0, 0);
project.Connect(project.Network.GroupInputNodes[1], neuralGroup, 0, 1);
project.Connect(neuralGroup.GroupInputNodes[0], hiddenLayer, 0, 0);
project.Connect(neuralGroup.GroupInputNodes[1], outputLayer, 0, 1);
project.Connect(hiddenLayer, outputLayer, 0, 0);
project.Connect(outputLayer, accumulator, 1, 0);
// Setup the nodes.
MyTask sendMnistData = world.GetTaskByPropertyName("SendTrainingMNISTData");
Assert.NotNull(sendMnistData);
sendMnistData.GetType().GetProperty("RandomEnumerate").SetValue(sendMnistData, true);
sendMnistData.GetType().GetProperty("ExpositionTime").SetValue(sendMnistData, 1);
world.GetType().GetProperty("Binary").SetValue(world, true);
hiddenLayer.Neurons = 40;
accumulator.ApproachValue.ApproachMethod = MyAccumulator.MyApproachValueTask.SequenceType.Momentum;
accumulator.ApproachValue.Delta = 0.1f;
accumulator.ApproachValue.Target = 0;
accumulator.ApproachValue.Factor = 0.9f;
// Enable tasks.
project.World.EnableDefaultTasks();
neuralGroup.EnableDefaultTasks();
neuralGroup.RMS.Enabled = true;
hiddenLayer.EnableDefaultTasks();
outputLayer.EnableDefaultTasks();
accumulator.EnableDefaultTasks();
accumulator.ApproachValue.Enabled = true;
// Run the simulation.
runner.RunAndPause(100);
float error = runner.GetValues(accumulator.Id)[0];
Assert.True(error < 0.5f);
//runner.SaveProject(@"c:\foobar.brain");
}
}
