| Name |
Description |
| Any |
Checks for when any of a given list of stopping criteria is met. |
| CoefficientEquality |
Checks for when the coefficients themselves have completely stopped changing. |
| DescentSpy |
StoppingCriteria decorator that will call some callback on each iteration
before passing on to the delegated criteria. Useful for debugging gradient
descent convergence. |
| GradientNorm |
Stops when the normal of the gradient vector drops below some specified eta. |
| MaxIterations |
Place a cap on the total number of iterations through the descent. It won't give
actual convergence, but is good if "good enough" can be expected to be reached
after a given number of times through the data. Note that each iteration does
not necessarily correspond to a complete epoch of the data. Check your descent
method for how many records are processed per iteration. Pairs well with a
criteria that checks for actual convergence inside of an `Any` object to stop
early if convergence has occurred. |
| MaxTime |
Run the descent for a certain amount of time before stopping. Goes well with
some criteria that checks for convergence within an instance of `Any` to cap
the amount of time that a descent can run. Nice to make sure that a descent
doesn't become runaway on a production server. |
| NthIteration |
Decorator that calls the underlying StoppingCriteria only after every N
iterations. This is for when the stopping criteria is expensive to
calculate, such as one that involves computing the cost of the entire
data-set given the current set of weights. |
| SteppedCriteria |
Decorates another StoppingCriteria and passes it the gradient as updated by the provided
Schedule object. This way, you can test your stopping criteria not against the current
gradient, but against the gradient as it is for coefficient updates. Useful for
Stochastic gradient descent and MiniBatch gradient descent with small batches, where the
actual gradient never quite settles down, despite the descent itself converging. |