public void LinkのParameterがoptimizerで更新される()
{
var optimizer = new optimizers.SGD(lr: 0.001f);
var link = new SimpleLink();
optimizer.Setup(link);
var loss = MeanSquaredError.ForwardStatic(
link.Forward(new Variable(Matrix <float> .Build.DenseOfArray(new float[, ] {
{ 1, 1, 1 }
}).Transpose())),
new Variable(Matrix <float> .Build.DenseOfArray(new float[, ] {
{ 1, 2, 3 }
}).Transpose())
);
var before = link.constParam.Value;
optimizer.ZeroGrads();
loss.Backward();
optimizer.Update();
var after = link.constParam.Value;
Helper.AssertMatrixNotAlmostEqual(before, after, delta: 0);
}
public void Iterationを回すと最適値になる()
{
var optimizer = new optimizers.SGD(lr: 0.05f);
var link = new SimpleLink();
optimizer.Setup(link);
var loss = MeanSquaredError.ForwardStatic(
link.Forward(new Variable(Matrix <float> .Build.DenseOfArray(new float[, ] {
{ 1, 1, 1 }
}).Transpose())),
new Variable(Matrix <float> .Build.DenseOfArray(new float[, ] {
{ 1, 2, 3 }
}).Transpose())
);
Assert.Greater(loss.Value[0, 0], 0.1f);
var converge = false;
for (int i = 0; i < 100; i++)
{
var lossEach = MeanSquaredError.ForwardStatic(
link.Forward(new Variable(Matrix <float> .Build.DenseOfArray(new float[, ] {
{ 1, 1, 1 }
}).Transpose())),
new Variable(Matrix <float> .Build.DenseOfArray(new float[, ] {
{ 1, 2, 3 }
}).Transpose())
);
if (lossEach.Value[0, 0] < 0.1f)
{
converge = true;
break;
}
optimizer.ZeroGrads();
lossEach.Backward();
optimizer.Update();
}
Assert.True(converge);
}