public static string ToJson <T>(this FluentNet <T> net) where T : struct, IEquatable <T>, IFormattable
{
var data = net.GetData();
var json = JsonConvert.SerializeObject(data);
return(json);
}
private INet <double> BuildModel(GameModelConfig cfg)
{
_logger.LogInformation("Building the model.");
var model = FluentNet <double> .Create(cfg.InputWidth, cfg.InputHeight, cfg.ImageChannels)
.Conv(8, 8, 32).Stride(4).Pad(2)
.Pool(2, 2)
.Relu()
.Conv(4, 4, 64).Stride(2).Pad(2)
.Pool(2, 2)
.Relu()
.Conv(3, 3, 64).Stride(1).Pad(2)
.Pool(2, 2)
.Relu()
.FullyConn(512)
.Relu()
.FullyConn(cfg.Actions)
.Softmax(cfg.Actions)
.Build();
//create model file if not present
if (!File.Exists(modelPath))
{
File.WriteAllText(modelPath, model.ToJson());
}
_logger.LogInformation("Finished bilding the model.");
return(model);
}
public TrainingServiceProvider()
{
_framework = "keras";
_model = BuildKerasModel();
_cnn = BuildCNN();
_trainer = new SgdTrainer<double>(_cnn);
}
public void Load()
{
this.net = NetExtension.LoadNet($"{this.Prefix}net.dat");
FluentNet fluentNet = net as FluentNet;
this.inputWidth = fluentNet.InputLayers[0].InputWidth;
}
public void CreateTest()
{
var net = FluentNet.Create(10, 10, 2)
.Relu()
.FullyConn(10)
.Softmax(10)
.Build();
//net.Forward(new Volume(10, 10, 2));
}
private void MnistDemo()
{
Directory.CreateDirectory(mnistFolder);
string trainingLabelFilePath = Path.Combine(mnistFolder, trainingLabelFile);
string trainingImageFilePath = Path.Combine(mnistFolder, trainingImageFile);
string testingLabelFilePath = Path.Combine(mnistFolder, testingLabelFile);
string testingImageFilePath = Path.Combine(mnistFolder, testingImageFile);
// Download Mnist files if needed
Console.WriteLine("Downloading Mnist training files...");
DownloadFile(urlMnist + trainingLabelFile, trainingLabelFilePath);
DownloadFile(urlMnist + trainingImageFile, trainingImageFilePath);
Console.WriteLine("Downloading Mnist testing files...");
DownloadFile(urlMnist + testingLabelFile, testingLabelFilePath);
DownloadFile(urlMnist + testingImageFile, testingImageFilePath);
// Load data
Console.WriteLine("Loading the datasets...");
this.training = MnistReader.Load(trainingLabelFilePath, trainingImageFilePath);
this.testing = MnistReader.Load(testingLabelFilePath, testingImageFilePath);
if (this.training.Count == 0 || this.testing.Count == 0)
{
Console.WriteLine("Missing Mnist training/testing files.");
Console.ReadKey();
return;
}
// Create network
this.net = FluentNet.Create(24, 24, 1)
.Conv(5, 5, 8).Stride(1).Pad(2)
.Relu()
.Pool(2, 2).Stride(2)
.Conv(5, 5, 16).Stride(1).Pad(2)
.Relu()
.Pool(3, 3).Stride(3)
.FullyConn(10)
.Softmax(10)
.Build();
this.trainer = new AdadeltaTrainer(this.net)
{
BatchSize = 20,
L2Decay = 0.001,
};
Console.WriteLine("Convolutional neural network learning...[Press any key to stop]");
do
{
var sample = this.SampleTrainingInstance();
this.Step(sample);
} while (!Console.KeyAvailable);
}
private FluentNet<double> BuildCNN() =>
FluentNet<double>.Create(80, 60, 1)
.Conv(5, 5, 7)
.Relu()
.Pool(2, 2)
.Conv(5, 5, 5)
.Relu()
.Pool(2, 2)
.FullyConn(40)
.Tanh()
.FullyConn(4)
.Softmax(4)
.Build();
public static string ToJSON(this FluentNet net)
{
//Serializes net to JSON
using (var ms = new MemoryStream())
{
var serializer = new DataContractJsonSerializer(typeof(FluentNet), new DataContractJsonSerializerSettings {
SerializeReadOnlyTypes = true, KnownTypes = new Type[] { typeof(Volume), typeof(VolumeWrapper) }
});
serializer.WriteObject(ms, net);
ms.Position = 0;
StreamReader sr = new StreamReader(ms);
return(sr.ReadToEnd());
}
}
public void MergeTest()
{
var branch1 = FluentNet.Create(10, 10, 2)
.Relu()
.FullyConn(10);
var branch2 = FluentNet.Create(10, 10, 2)
.Relu()
.FullyConn(20);
var net = FluentNet.Merge(branch1, branch2)
.FullyConn(5)
.Softmax(5)
.Build();
net.Forward(new[] { new Volume(10, 10, 2), new Volume(10, 10, 2) });
}
private void MnistDemo()
{
var datasets = new DataSets();
if (!datasets.Load(100))
{
return;
}
// Create network
this._net = FluentNet <double> .Create(24, 24, 1)
.Conv(5, 5, 8).Stride(1).Pad(2)
.Relu()
.Pool(2, 2).Stride(2)
.Conv(5, 5, 16).Stride(1).Pad(2)
.Relu()
.Pool(3, 3).Stride(3)
.FullyConn(10)
.Softmax(10)
.Build();
this._trainer = new SgdTrainer <double>(this._net)
{
LearningRate = 0.01,
BatchSize = 20,
L2Decay = 0.001,
Momentum = 0.9
};
Console.WriteLine("Convolutional neural network learning...[Press any key to stop]");
do
{
var trainSample = datasets.Train.NextBatch(this._trainer.BatchSize);
Train(trainSample.Item1, trainSample.Item2, trainSample.Item3);
var testSample = datasets.Test.NextBatch(this._trainer.BatchSize);
Test(testSample.Item1, testSample.Item3, this._testAccWindow);
Console.WriteLine("Loss: {0} Train accuracy: {1}% Test accuracy: {2}%", this._trainer.Loss,
Math.Round(this._trainAccWindow.Items.Average() * 100.0, 2),
Math.Round(this._testAccWindow.Items.Average() * 100.0, 2));
Console.WriteLine("Example seen: {0} Fwd: {1}ms Bckw: {2}ms", this._stepCount,
Math.Round(this._trainer.ForwardTimeMs, 2),
Math.Round(this._trainer.BackwardTimeMs, 2));
} while (!Console.KeyAvailable);
}
public void FluentNetSerialization()
{
// Fluent version
var net = FluentNet <double> .Create(24, 24, 1)
.Conv(5, 5, 8).Stride(1).Pad(2)
.Relu()
.Pool(2, 2).Stride(2)
.Conv(5, 5, 16).Stride(1).Pad(2)
.Relu()
.Pool(3, 3).Stride(3)
.FullyConn(10)
.Softmax(10)
.Build();
var json = net.ToJson();
var deserialized = SerializationExtensions.FromJson <double>(json);
Assert.AreEqual(9, deserialized.Layers.Count);
}
public MainWindow()
{
InitializeComponent();
Init();
InitPalette();
StartNes();
var net = FluentNet <double> .Create(WIDTH, HEIGHT, 4)
.Conv(5, 5, 8).Stride(1).Pad(2)
.Relu()
.Pool(2, 2).Stride(2)
.Conv(5, 5, 16).Stride(1).Pad(2)
.Relu()
.Pool(3, 3).Stride(3)
.FullyConn(10)
.Softmax(10)
.Build();
}
public void FluentBinaryNetSerializerTest()
{
var net = FluentNet.Create(5, 5, 3)
.Conv(2, 2, 16)
.FullyConn(3)
.Softmax(3)
.Build();
// Serialize (binary)
using (var ms = new MemoryStream())
{
net.SaveBinary(ms);
ms.Position = 0;
// Deserialize (binary)
FluentNet deserialized = SerializationExtensions.LoadBinary(ms) as FluentNet;
Assert.IsNotNull(deserialized);
Assert.AreEqual(net.InputLayers.Count, deserialized.InputLayers.Count);
// TODO: improve test
}
}
public static void Run()
{
const string NetName = "net.dat";
var random = new Random();
var entryContainer = new EntryContainer();
#region Load Net
var convInputWith = 11; // Will extract 11x11 area
if (convInputWith % 2 == 0)
{
throw new ArgumentException("convInputWith must be odd");
}
// Load IA or initialize new network if not found - Direction choice
INet singleNet = NetExtension.LoadOrCreateNet(NetName, () =>
{
var net = FluentNet.Create(convInputWith, convInputWith, 3)
.Conv(3, 3, 16).Stride(2)
.Tanh()
.Conv(2, 2, 16)
.Tanh()
.FullyConn(100)
.Relu()
.FullyConn(5)
.Softmax(5).Build();
return(net);
});
#endregion
#region Load data
var hltFiles = Directory.EnumerateFiles(@"..\..\..\games\2609\", "*.hlt").ToList(); // erdman games downloaded with HltDownloader
int total = hltFiles.Count;
Console.WriteLine($"Loading {total} games...");
foreach (var file in hltFiles)
{
Console.WriteLine(total--);
HltReader reader = new HltReader(file);
var playerId = -1;
var playerToCopy = reader.PlayerNames.FirstOrDefault(o => o.StartsWith("erdman"));
if (playerToCopy == null)
{
Console.WriteLine("Player not found");
continue;
}
playerId = reader.PlayerNames.IndexOf(playerToCopy) + 1;
var width = reader.Width;
var height = reader.Height;
int lastmoveCount = 1;
for (var frame = 0; frame < reader.FrameCount - 1; frame++)
{
var currentFrame = reader.GetFrame(frame);
var map = currentFrame.map;
var moves = currentFrame.moves;
var helper = new Helper(map, (ushort)playerId);
bool foundInFrame = false;
int moveCount = 0;
// moves
for (ushort x = 0; x < width; x++)
{
for (ushort y = 0; y < height; y++)
{
if (map[x, y].Owner == playerId)
{
foundInFrame = true;
moveCount++;
if (random.NextDouble() < 1.0 / lastmoveCount)
{
var convVolume = map.GetVolume(convInputWith, playerId, x, y); // Input
var direction = moves[y][x]; // Output
var entry1 = new Entry(new[] { convVolume }, direction, x, y, frame, file.GetHashCode());
entryContainer.Add(entry1);
// Data augmentation
var entry2 = new Entry(new[] { convVolume.Flip(VolumeUtilities.FlipMode.LeftRight) }, (int)Helper.FlipLeftRight((Direction)direction), x, y, frame, file.GetHashCode());
entryContainer.Add(entry2);
var entry3 = new Entry(new[] { convVolume.Flip(VolumeUtilities.FlipMode.UpDown) }, (int)Helper.FlipUpDown((Direction)direction), x, y, frame, file.GetHashCode());
entryContainer.Add(entry3);
var entry4 = new Entry(new[] { convVolume.Flip(VolumeUtilities.FlipMode.Both) }, (int)Helper.FlipBothWay((Direction)direction), x, y, frame, file.GetHashCode());
entryContainer.Add(entry4);
}
}
}
}
lastmoveCount = moveCount;
if (!foundInFrame)
{
// player has died
break;
}
}
}
var length = entryContainer.Shuffle();
Console.WriteLine(entryContainer.Summary);
#endregion
#region Training
var trainer = new AdamTrainer(singleNet)
{
BatchSize = 1024, LearningRate = 0.1, Beta1 = 0.9, Beta2 = 0.99, Eps = 1e-8
};
var trainingScheme = new TrainingScheme(singleNet, trainer, entryContainer, "single");
bool save = true;
double lastValidationAcc = 0.0;
do
{
for (int i = 0; i < 1000; i++)
{
if (i > 5)
{
trainer.L2Decay = 0.001;
}
Console.WriteLine($"Epoch #{i + 1}");
if (i % 15 == 0)
{
trainer.LearningRate = Math.Max(trainer.LearningRate / 10.0, 0.00001);
}
trainingScheme.RunEpoch();
#region Save Nets
if (save)
{
// Save if validation accuracy has improved
if (trainingScheme.ValidationAccuracy > lastValidationAcc)
{
lastValidationAcc = trainingScheme.ValidationAccuracy;
singleNet.SaveNet(NetName);
}
}
#endregion
if (Console.KeyAvailable)
{
break;
}
}
} while (!Console.KeyAvailable);
#endregion
}
public static void Run()
{
var random = new Random(RandomUtilities.Seed);
int normalInputWidth = 19;
int earlyInputWidth = 19;
int strongInputWidth = 19;
string NetName = $"net.dat";
string NetName_early = $"net_early.dat";
string NetName_strong = $"net_strong.dat";
var entryContainer = new EntryContainer();
var entryContainer_early = new EntryContainer();
var entryContainer_strong = new EntryContainer();
#region Load Net
INet singleNet = NetExtension.LoadOrCreateNet(NetName, () =>
{
var net = FluentNet.Create(normalInputWidth, normalInputWidth, 3)
.Conv(5, 5, 16).Stride(5).Pad(2)
.Tanh()
.Conv(3, 3, 16).Stride(1).Pad(1)
.Tanh()
.FullyConn(100)
.Relu()
.FullyConn(5)
.Softmax(5).Build();
return(net);
});
INet singleNet_early = NetExtension.LoadOrCreateNet(NetName_early, () =>
{
var net = FluentNet.Create(earlyInputWidth, earlyInputWidth, 3)
.Conv(5, 5, 16).Stride(5).Pad(2)
.Tanh()
.Conv(3, 3, 16).Stride(1).Pad(1)
.Tanh()
.FullyConn(100)
.Relu()
.FullyConn(5)
.Softmax(5).Build();
return(net);
});
INet singleNet_strong = NetExtension.LoadOrCreateNet(NetName_strong, () =>
{
var net = FluentNet.Create(strongInputWidth, strongInputWidth, 3)
.Conv(5, 5, 16).Stride(5).Pad(2)
.Tanh()
.Conv(3, 3, 16).Stride(1).Pad(1)
.Tanh()
.FullyConn(100)
.Relu()
.FullyConn(5)
.Softmax(5).Build();
return(net);
});
#endregion
#region Load data
var hltFiles = Directory.EnumerateFiles(@"..\..\..\games\2609\", "*.hlt").ToList(); // erdman games downloaded with HltDownloader
int total = hltFiles.Count;
Console.WriteLine($"Loading {total} games...");
foreach (var file in hltFiles)
{
Console.WriteLine(total--);
HltReader reader = new HltReader(file);
var playerId = -1;
var playerToCopy = reader.PlayerNames.FirstOrDefault(o => o.StartsWith("erdman"));
if (playerToCopy != null)
{
playerId = reader.PlayerNames.IndexOf(playerToCopy) + 1;
}
if (playerId != -1)
{
var width = reader.Width;
var height = reader.Height;
int lastmoveCount = 1;
for (var frame = 0; frame < reader.FrameCount - 1; frame++)
{
bool earlyGame = lastmoveCount < 25;
var currentFrame = reader.GetFrame(frame);
var map = currentFrame.map;
var moves = currentFrame.moves;
var helper = new Helper(map, (ushort)playerId);
bool foundInFrame = false;
int moveCount = 0;
// moves
for (ushort x = 0; x < width; x++)
{
for (ushort y = 0; y < height; y++)
{
if (map[x, y].Owner == playerId)
{
bool strong = map[x, y].Strength > 200;
foundInFrame = true;
moveCount++;
if ((earlyGame && random.NextDouble() < 1.5 / lastmoveCount) || (strong && random.NextDouble() < 1.5 / lastmoveCount) || random.NextDouble() < 1.0 / lastmoveCount)
{
var w = normalInputWidth;
var container = entryContainer;
if (earlyGame)
{
w = earlyInputWidth;
container = entryContainer_early;
}
else if (strong)
{
w = strongInputWidth;
container = entryContainer_strong;
}
var convVolume = map.GetVolume(w, playerId, x, y);
var direction = moves[y][x];
var entry1 = new Entry(new[] { convVolume }, direction, x, y, frame, file.GetHashCode());
container.Add(entry1);
var entry2 = new Entry(new[] { convVolume.Flip(VolumeUtilities.FlipMode.LeftRight) }, (int)Helper.FlipLeftRight((Direction)direction), x, y, frame, file.GetHashCode());
container.Add(entry2);
var entry3 = new Entry(new[] { convVolume.Flip(VolumeUtilities.FlipMode.UpDown) }, (int)Helper.FlipUpDown((Direction)direction), x, y, frame, file.GetHashCode());
container.Add(entry3);
var entry4 = new Entry(new[] { convVolume.Flip(VolumeUtilities.FlipMode.Both) }, (int)Helper.FlipBothWay((Direction)direction), x, y, frame, file.GetHashCode());
container.Add(entry4);
}
}
}
}
lastmoveCount = moveCount;
if (!foundInFrame)
{
// player has died
break;
}
}
}
else
{
Console.WriteLine("not found");
}
}
var length = entryContainer.Shuffle();
Console.WriteLine("normal: " + entryContainer.Summary);
length = entryContainer_early.Shuffle();
Console.WriteLine("early: " + entryContainer_early.Summary);
length = entryContainer_strong.Shuffle();
Console.WriteLine("strong " + entryContainer_strong.Summary);
#endregion
#region Training
var trainer = new AdamTrainer(singleNet)
{
BatchSize = 1024, LearningRate = 0.01, Beta1 = 0.9, Beta2 = 0.99, Eps = 1e-8
};
var trainingScheme = new TrainingScheme(singleNet, trainer, entryContainer, "single");
var trainer_early = new AdamTrainer(singleNet_early)
{
BatchSize = 1024, LearningRate = 0.01, Beta1 = 0.9, Beta2 = 0.99, Eps = 1e-8
};
var trainingScheme_early = new TrainingScheme(singleNet_early, trainer_early, entryContainer_early, "single_early");
var trainer_strong = new AdamTrainer(singleNet_strong)
{
BatchSize = 1024, LearningRate = 0.01, Beta1 = 0.9, Beta2 = 0.99, Eps = 1e-8
};
var trainingScheme_strong = new TrainingScheme(singleNet_strong, trainer_strong, entryContainer_strong, "single_strong");
bool save = true;
double lastValidationAcc = 0.0;
double lastValidationAcc_early = 0.0;
double lastValidationAcc_strong = 0.0;
double lastTrainAcc = 0.0;
double lastTrainAcc_early = 0.0;
double lastTrainAcc_strong = 0.0;
do
{
var normal = Task.Factory.StartNew(() =>
{
for (int i = 0; i < 50; i++)
{
if (i > 5)
{
trainer.L2Decay = 0.05;
}
Console.WriteLine($"[normal] Epoch #{i + 1}");
if (i % 50 == 0)
{
trainer.LearningRate = Math.Max(trainer.LearningRate / 5.0, 0.00001);
}
trainingScheme.RunEpoch();
#region Save Nets
if (save)
{
if (trainingScheme.ValidationAccuracy > lastValidationAcc)
{
lastValidationAcc = trainingScheme.ValidationAccuracy;
lastTrainAcc = trainingScheme.TrainAccuracy;
singleNet.SaveNet(NetName);
}
}
#endregion
if (Console.KeyAvailable)
{
break;
}
}
});
var early = Task.Factory.StartNew(() =>
{
for (int i = 0; i < 50; i++)
{
if (i > 5)
{
trainer_early.L2Decay = 0.05;
}
Console.WriteLine($"[early] Epoch #{i + 1}");
if (i % 50 == 0)
{
trainer_early.LearningRate = Math.Max(trainer_early.LearningRate / 5.0, 0.00001);
}
trainingScheme_early.RunEpoch();
#region Save Nets
if (save)
{
if (trainingScheme_early.ValidationAccuracy > lastValidationAcc_early)
{
lastValidationAcc_early = trainingScheme_early.ValidationAccuracy;
lastTrainAcc_early = trainingScheme_early.TrainAccuracy;
singleNet_early.SaveNet(NetName_early);
}
}
#endregion
if (Console.KeyAvailable)
{
break;
}
}
});
var strong = Task.Factory.StartNew(() =>
{
for (int i = 0; i < 50; i++)
{
if (i > 5)
{
trainer_strong.L2Decay = 0.05;
}
Console.WriteLine($"[strong] Epoch #{i + 1}");
if (i % 50 == 0)
{
trainer_strong.LearningRate = Math.Max(trainer_strong.LearningRate / 5.0, 0.00001);
}
trainingScheme_strong.RunEpoch();
#region Save Nets
if (save)
{
if (trainingScheme_strong.ValidationAccuracy > lastValidationAcc_strong)
{
lastValidationAcc_strong = trainingScheme_strong.ValidationAccuracy;
lastTrainAcc_strong = trainingScheme_strong.TrainAccuracy;
singleNet_strong.SaveNet(NetName_strong);
}
}
#endregion
if (Console.KeyAvailable)
{
break;
}
}
});
Task.WaitAll(new[] { normal, strong, early });
}while (!Console.KeyAvailable);
#endregion
}
public IEnumerable <Move> GetMoves()
{
var moves = new List <Move>();
try
{
var chrono = System.Diagnostics.Stopwatch.StartNew();
bool earlyGame = lastmoveCount < 15;
int moveCount = 0;
for (ushort x = 0; x < this.map.Width; x++)
{
for (ushort y = 0; y < this.map.Height; y++)
{
if (this.map[x, y].Owner == this.myId)
{
Direction move = Direction.Still;
// Check if we are taking too much time
if (lastElapsed > 850)
{
degradedMode = true;
}
if (this.map[x, y].Strength > 1)
{
// Evaluate network if
// - not in degraded mode
// OR degraded mode and (frame + x + y) % 4 == 0. Used to evaluate only 25% of the tiles
// OR strength is 255
if (!degradedMode || ((frame + x + y) % 4 == 0) || this.map[x, y].Strength == 255)
{
// Choose which network to use
FluentNet net = singleNet as FluentNet;
if (earlyGame)
{
net = singleNet_early as FluentNet;;
}
else if (map[x, y].Strength > 200)
{
net = singleNet_strong as FluentNet;;
}
// Create input
int inputWidth = net.InputLayers[0].InputWidth;
var volume = this.map.GetVolume(inputWidth, this.myId, x, y);
net.Forward(volume);
// Get prediction
move = (Direction)net.GetPrediction();
}
}
moves.Add(new Move
{
Location = new Location {
X = x, Y = y
},
Direction = move
});
if (chrono.Elapsed.TotalMilliseconds > 900)
{
return(moves);
}
moveCount++;
}
}
}
this.lastmoveCount = moveCount;
this.frame++;
lastElapsed = chrono.Elapsed.TotalMilliseconds;
}
catch (Exception ex)
{
Console.WriteLine(ex);
throw;
}
return(moves);
}
