public void ReferenceTest()
{
int length = 27;
{
float limit = 0.5f;
float[] xval = (new float[length]).Select((_, idx) => 0.1f * ((float)idx * 3 - length)).ToArray();
float norm = xval.Select((v) => v * v).Average();
float rate = (float)Math.Sqrt(limit / Math.Max(norm, limit));
float[] yval = xval.Select((v) => v * rate).ToArray();
Tensor x_tensor = new Tensor(Shape.Vector(length), xval);
ParameterField x = x_tensor;
(Flow flow, _) = Flow.Optimize(x);
flow.Execute();
x.AddUpdater(new GradRMSClipping(x, limit));
x.Update();
AssertError.Tolerance(yval, x.GradTensor.State, 1e-7f, 1e-5f);
float post_norm = x.GradTensor.State.Select((v) => v * v).Average();
Assert.AreEqual(limit, post_norm, 1e-5f);
CollectionAssert.AreEqual(xval, x.ValueTensor.State);
}
{
float limit = 1e+7f;
float[] xval = (new float[length]).Select((_, idx) => 0.1f * ((float)idx * 3 - length)).ToArray();
Tensor x_tensor = new Tensor(Shape.Vector(length), xval);
ParameterField x = x_tensor;
(Flow flow, _) = Flow.Optimize(x);
flow.Execute();
x.AddUpdater(new GradRMSClipping(x, limit));
x.Update();
AssertError.Tolerance(xval, x.GradTensor.State, 1e-7f, 1e-5f);
CollectionAssert.AreEqual(xval, x.ValueTensor.State);
}
}
public void ReferenceTest()
{
int length = 27;
float decay = 0.25f;
float[] xval = (new float[length]).Select((_, idx) => 0.1f * ((float)idx * 3 - length)).ToArray();
float[] yval = xval.Select((v) => v * (1 - decay)).ToArray();
Tensor x_tensor = new Tensor(Shape.Vector(length), xval);
ParameterField x = x_tensor;
(Flow flow, _) = Flow.Optimize(x);
x.AddUpdater(new Ridge(x, decay));
x.Update();
AssertError.Tolerance(yval, x_tensor.State, 1e-7f, 1e-5f);
}
public void ReferenceTest()
{
int length = 27;
float limit = 1;
float[] xval = (new float[length]).Select((_, idx) => 0.1f * ((float)idx * 3 - length)).ToArray();
float[] yval = xval.Select((v) => Math.Min(Math.Max(v, -limit), limit)).ToArray();
Tensor x_tensor = new Tensor(Shape.Vector(length), xval);
ParameterField x = x_tensor;
(Flow flow, _) = Flow.Optimize(x);
flow.Execute();
x.AddUpdater(new GradClipping(x, limit));
x.Update();
AssertError.Tolerance(yval, x.GradTensor.State, 1e-7f, 1e-5f);
CollectionAssert.AreEqual(xval, x.ValueTensor.State);
}
public void ReferenceTest()
{
int ch = 7, w = 3, h = 5, length = ch * w * h;
float decay = 0.25f;
float[] xval = (new float[length]).Select((_, idx) => 0.1f * ((float)idx * 3 * (idx % 5) - length)).ToArray();
float[] yval = xval.Select((v) => Math.Sign(v) * Math.Max(Math.Abs(v) - decay, 0)).ToArray();
Tensor x_tensor = new Tensor(Shape.Map2D(ch, w, h), xval);
int[] axes = new int[] { Axis.Map2D.Width, Axis.Map2D.Height };
float[] pre_variance = Variance(x_tensor, axes).State;
float[] pre_mean = Average(x_tensor, axes).State;
ParameterField x = x_tensor;
(Flow flow, _) = Flow.Optimize(x);
x.AddUpdater(new ZeroMeanShift(x, axes));
x.Update();
float[] post_variance = Variance(x_tensor, axes).State;
float[] post_mean = Average(x_tensor, axes).State;
AssertError.Tolerance(pre_variance, post_variance, 1e-7f, 1e-5f);
foreach (var v in pre_mean)
{
Assert.AreNotEqual(0, v, 1e-5f);
}
foreach (var v in post_mean)
{
Assert.AreEqual(0, v, 1e-5f);
}
}
