public static void Run()
{
//目標とするフィルタを作成（実践であればココは不明な値となる）
Deconvolution2D decon_core = new Deconvolution2D(1, 1, 15, 1, 7, gpuEnable: true)
{
Weight = { Data = MakeOneCore() }
};
Deconvolution2D model = new Deconvolution2D(1, 1, 15, 1, 7, gpuEnable: true);
SGD optimizer = new SGD(learningRate: 0.00005); //大きいと発散する
model.SetOptimizer(optimizer);
MeanSquaredError meanSquaredError = new MeanSquaredError();
//移植元では同じ教育画像で教育しているが、より実践に近い学習に変更
for (int i = 0; i < 11; i++)
{
//ランダムに点が打たれた画像を生成
NdArray img_p = getRandomImage();
//目標とするフィルタで学習用の画像を出力
NdArray[] img_core = decon_core.Forward(img_p);
//未学習のフィルタで画像を出力
NdArray[] img_y = model.Forward(img_p);
Real loss = meanSquaredError.Evaluate(img_y, img_core);
model.Backward(img_y);
model.Update();
Console.WriteLine("epoch" + i + " : " + loss);
}
}
public static void Run()
{
// Create a target filter (In case of practice, here is the unknown value)
Deconvolution2D decon_core = new Deconvolution2D(true, 1, 1, 15, 1, 7, gpuEnable: true)
{
Weight = { Data = MakeOneCore() }
};
Deconvolution2D model = new Deconvolution2D(true, 1, 1, 15, 1, 7, gpuEnable: true);
SGD optimizer = new SGD(learningRate: 0.00005); // diverge if big
model.SetOptimizer(optimizer);
MeanSquaredError meanSquaredError = new MeanSquaredError();
// I am educating with the same educational image at the transplanting source, but changing to learning closer to practice
for (int i = 0; i < 11; i++)
{
// Generate an image with randomly struck points
NdArray img_p = getRandomImage();
// Output a learning image with a target filter
NdArray[] img_core = decon_core.Forward(true, img_p);
// Output an image with an unlearned filter
NdArray[] img_y = model.Forward(true, img_p);
Real loss = meanSquaredError.Evaluate(img_y, img_core);
model.Backward(true, img_y);
model.Update();
RILogManager.Default?.SendDebug("epoch" + i + " : " + loss);
}
}
public static void Run()
{
//Create a target filter (If it is practical, here is an unknown value)
Deconvolution2D decon_core = new Deconvolution2D(1, 1, 15, 1, 7, gpuEnable: true)
{
Weight = { Data = MakeOneCore() }
};
Deconvolution2D model = new Deconvolution2D(1, 1, 15, 1, 7, gpuEnable: true);
SGD optimizer = new SGD(learningRate: 0.00005); //When it is big, it diverges.
model.SetOptimizer(optimizer);
MeanSquaredError meanSquaredError = new MeanSquaredError();
//At the transplant source, we are educating with the same educational image, but changing to learning closer to practice
for (int i = 0; i < 11; i++)
{
//Generate random dotted images
NdArray img_p = getRandomImage();
//Output a learning image with a target filter
NdArray[] img_core = decon_core.Forward(img_p);
//Output an image with an unlearned filter
NdArray[] img_y = model.Forward(img_p);
Real loss = meanSquaredError.Evaluate(img_y, img_core);
model.Backward(img_y);
model.Update();
Console.WriteLine("epoch" + i + " : " + loss);
}
}
public Test13WinForm()
{
InitializeComponent();
ClientSize = new Size(128 * 4, 128 * 4);
//目標とするフィルタを作成（実践であればココは不明な値となる）
decon_core = new Deconvolution2D(1, 1, 15, 1, 7, gpuEnable: true)
{
Weight = { Data = MakeOneCore() }
};
model = new Deconvolution2D(1, 1, 15, 1, 7, gpuEnable: true);
optimizer = new SGD(learningRate: 0.01); //大きいと発散する
model.SetOptimizer(optimizer);
}
public Test13WinForm()
{
InitializeComponent();
ClientSize = new Size(128 * 4, 128 * 4);
// Create a target filter (In case of practice, here is the unknown value)
decon_core = new Deconvolution2D(true, 1, 1, 15, 1, 7, gpuEnable: true)
{
Weight = { Data = MakeOneCore() }
};
model = new Deconvolution2D(true, 1, 1, 15, 1, 7, gpuEnable: true);
optimizer = new SGD(learningRate: 0.01); // diverge if big
model.SetOptimizer(optimizer);
}