Since the gradient of the error becomes shallower as the descent nears
convergence, this will naturally shrink the updates into the error
function's minimum. However, too large of a step size will lead to the
descent diverging and too small of a step size will lead to an extremely
long descent. Unfortunately, choosing a good step size is a matter of
trial and error.
public function testStep() { $schedule = new Fixed(1.0); $schedule->update([1.0]); static::assertEquals(1.0, $schedule->step(0)); }