public override void DoWork(string[] args)
{
var folder = args[1];
var model = ModelIO.Load(folder);
DoWork(model);
ModelIO.Save(model);
}
public override void DoWork(string[] args)
{
if (args.Length < 3)
{
throw (new ArgumentException(String.Format("Insufficient args")));
}
var folder = args[1];
AssemblerMode mode;
if (!Enum.TryParse(args[2], true, out mode))
{
throw (new ArgumentException(String.Format("Unknown mode: {0}", args[2])));
}
var print = mode == AssemblerMode.Print;
var model = ModelIO.Load(folder);
DoWork(model, print);
// ModelIO.Save(model);
}
static void Main(string[] args)
{
var path = ModelIO.DebugSubdir("debug\\20");
Directory.CreateDirectory(path);
File.Delete(path + "\\montage.v3");
var model = ModelIO.Load(path);
for (int i = 0; i < 1000; i++)
{
model.SetChunkMode(i * 3000, (i % 2 == 0)?Mode.Face:Mode.Screen, false);
}
for (int i = 0; i < 1000; i++)
{
model.Montage.Intervals.Add(new Interval {
StartTime = i * 3000, EndTime = i * 3000 + 500, HasVoice = false
});
model.Montage.Intervals.Add(new Interval {
StartTime = i * 3000 + 500, EndTime = i * 3000 + 3000, HasVoice = true
});
}
ModelIO.Save(model);
}
public static void Run()
{
//訓練回数
const int learningCount = 10000;
//訓練データ
Real[][] trainData =
{
new Real[] { 0, 0 },
new Real[] { 1, 0 },
new Real[] { 0, 1 },
new Real[] { 1, 1 }
};
//訓練データラベル
Real[][] trainLabel =
{
new Real[] { 0 },
new Real[] { 1 },
new Real[] { 1 },
new Real[] { 0 }
};
//ネットワークの構成は FunctionStack に書き連ねる
FunctionStack nn = new FunctionStack(
new Linear(2, 2, name: "l1 Linear"),
new Sigmoid(name: "l1 Sigmoid"),
new Linear(2, 2, name: "l2 Linear")
);
//optimizerを宣言
nn.SetOptimizer(new MomentumSGD());
//訓練ループ
Console.WriteLine("Training...");
for (int i = 0; i < learningCount; i++)
{
for (int j = 0; j < trainData.Length; j++)
{
//訓練実行時にロス関数を記述
Trainer.Train(nn, trainData[j], trainLabel[j], new SoftmaxCrossEntropy());
}
}
//訓練結果を表示
Console.WriteLine("Test Start...");
foreach (Real[] input in trainData)
{
NdArray result = nn.Predict(input)[0];
int resultIndex = Array.IndexOf(result.Data, result.Data.Max());
Console.WriteLine(input[0] + " xor " + input[1] + " = " + resultIndex + " " + result);
}
//学習の終わったネットワークを保存
ModelIO.Save(nn, "test.nn");
//学習の終わったネットワークを読み込み
Function testnn = ModelIO.Load("test.nn");
Console.WriteLine("Test Start...");
foreach (Real[] input in trainData)
{
NdArray result = testnn.Predict(input)[0];
int resultIndex = Array.IndexOf(result.Data, result.Data.Max());
Console.WriteLine(input[0] + " xor " + input[1] + " = " + resultIndex + " " + result);
}
}
public static void Run()
{
const int learningCount = 10000;
Real[][] trainData =
{
new Real[] { 0, 0 },
new Real[] { 1, 0 },
new Real[] { 0, 1 },
new Real[] { 1, 1 }
};
//Training data label
Real[][] trainLabel =
{
new Real[] { 0 },
new Real[] { 1 },
new Real[] { 1 },
new Real[] { 0 }
};
//Network configuration is written in FunctionStack
FunctionStack nn = new FunctionStack(
new Linear(2, 2, name: "l1 Linear"),
new Sigmoid(name: "l1 Sigmoid"),
new Linear(2, 2, name: "l2 Linear")
);
//optimizer
nn.SetOptimizer(new MomentumSGD());
Console.WriteLine("Training...");
for (int i = 0; i < learningCount; i++)
{
for (int j = 0; j < trainData.Length; j++)
{
//Describe the loss function at training execution
Trainer.Train(nn, trainData[j], trainLabel[j], new SoftmaxCrossEntropy());
}
}
//Show training results
Console.WriteLine("Test Start...");
foreach (Real[] input in trainData)
{
NdArray result = nn.Predict(input)[0];
int resultIndex = Array.IndexOf(result.Data, result.Data.Max());
Console.WriteLine(input[0] + " xor " + input[1] + " = " + resultIndex + " " + result);
}
//Save network after learning
ModelIO.Save(nn, "test.nn");
//Load the network after learning
FunctionStack testnn = ModelIO.Load("test.nn");
Console.WriteLine("Test Start...");
foreach (Real[] input in trainData)
{
NdArray result = testnn.Predict(input)[0];
int resultIndex = Array.IndexOf(result.Data, result.Data.Max());
Console.WriteLine(input[0] + " xor " + input[1] + " = " + resultIndex + " " + result);
}
}
public static void Run()
{
const int learningCount = 10000;
Real[][] trainData =
{
new Real[] { 0, 0 },
new Real[] { 1, 0 },
new Real[] { 0, 1 },
new Real[] { 1, 1 }
};
Real[][] trainLabel =
{
new Real[] { 0 },
new Real[] { 1 },
new Real[] { 1 },
new Real[] { 0 }
};
bool verbose = true;
FunctionStack nn = new FunctionStack("Test1",
new Linear(verbose, 2, 2, name: "l1 Linear"),
new Sigmoid(name: "l1 Sigmoid"),
new Linear(verbose, 2, 2, name: "l2 Linear"));
nn.SetOptimizer(new MomentumSGD());
Info("Training...");
for (int i = 0; i < learningCount; i++)
{
for (int j = 0; j < trainData.Length; j++)
{
Trainer.Train(nn, trainData[j], trainLabel[j], new SoftmaxCrossEntropy());
}
}
Info("Test Start...");
foreach (Real[] input in trainData)
{
NdArray result = nn.Predict(true, input)?[0];
int resultIndex = Array.IndexOf(result?.Data, result.Data.Max());
Info($"{input[0]} xor {input[1]} = {resultIndex} {result}");
}
Info("Saving Model...");
ModelIO.Save(nn, "test.nn");
Info("Loading Model...");
FunctionStack testnn = ModelIO.Load("test.nn");
Info(testnn.Describe());
Info("Test Start...");
foreach (Real[] input in trainData)
{
NdArray result = testnn?.Predict(true, input)?[0];
int resultIndex = Array.IndexOf(result?.Data, result?.Data.Max());
Info($"{input[0]} xor {input[1]} = {resultIndex} {result}");
}
}
