public void 合流のある計算グラフでもpythonと同じBakcwardになる()
{
var chain = new helper.models.VerySmallChain();
var optimizer = new chainer.optimizers.Adam();
var input1 = new Variable(builder.DenseOfArray(new float[, ] {
{ 4, 3, 2 }
}));
var input2 = new Variable(builder.DenseOfArray(new float[, ] {
{ 10, 11, 12 }
}));
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 = chainer.functions.MeanSquaredError.ForwardStatic(
chain.Forward(input1),
target
) + chainer.functions.MeanSquaredError.ForwardStatic(
chain.Forward(input1),
target
) + chainer.functions.MeanSquaredError.ForwardStatic(
chain.Forward(input2),
target
);
Helper.AssertMatrixAlmostEqual(
loss.Value,
builder.DenseOfArray(new float[, ] {
{ 29811.0f }
}),
delta: 0.01f
);
loss.Backward();
Helper.AssertMatrixAlmostEqual(
chain.fc._Params["W"].Grad,
builder.DenseOfArray(new float[, ] {
{ -3556f, -3346f, -3136f }
}),
delta: 0.01f
);
}
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
);
}
