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);
}
public void chainer_pythonと同じ値になる()
{
var chain = new VerySmallChain();
var optimizer = new chainer.optimizers.Adam();
var input = new Variable(builder.DenseOfArray(new float[, ] {
{ 4, 3, 2 }
}));
var target = new Variable(builder.DenseOfArray(new float[, ] {
{ 100 }
}));
optimizer.Setup(chain);
Helper.AssertMatrixAlmostEqual(chain.fc._Params["W"].Value, builder.DenseOfArray(new float[, ] {
{ -1, 0, 1 }
}));
Helper.AssertMatrixAlmostEqual(chain.fc._Params["b"].Value, builder.DenseOfArray(new float[, ] {
{ 1 }
}));
var loss = MeanSquaredError.ForwardStatic(
chain.Forward(input),
target
);
Helper.AssertMatrixAlmostEqual(
loss.Value,
builder.DenseOfArray(new float[, ] {
{ 10201 }
}),
delta: 0.01f
);
optimizer.ZeroGrads();
loss.Backward();
optimizer.Update();
loss = MeanSquaredError.ForwardStatic(
chain.Forward(input),
target
);
Helper.AssertMatrixAlmostEqual(
loss.Value,
builder.DenseOfArray(new float[, ] {
{ 10198.9794921875f }
}),
delta: 0.01f
);
optimizer.ZeroGrads();
loss.Backward();
optimizer.Update();
loss = MeanSquaredError.ForwardStatic(
chain.Forward(input),
target
);
Helper.AssertMatrixAlmostEqual(
loss.Value,
builder.DenseOfArray(new float[, ] {
{ 10196.9609375f }
}),
delta: 0.01f
);
for (int i = 0; i < 100; i++)
{
loss = MeanSquaredError.ForwardStatic(
chain.Forward(input),
target
);
optimizer.ZeroGrads();
loss.Backward();
optimizer.Update();
}
loss = MeanSquaredError.ForwardStatic(
chain.Forward(input),
target
);
Helper.AssertMatrixAlmostEqual(
loss.Value,
builder.DenseOfArray(new float[, ] {
{ 9996.3515625f }
}),
delta: 0.01f
);
}
