make a BalancingLearner strategy that compares the total loss rather than loss improvement
Closes #103 (closed)
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In fact it does work as a drop-in replacement. It also handles #104 (closed) better than the current balancinglearner, although it won't solve the underlying "issue".
Further I am not entirely sure how I should measure performance. Maybe we could have like a graph: I can think of 2 factors that may be important: the number of cores available and the number of learners you pass into the balancinglearner. As I can't modify the number of cores, I think this can be simulated by just using multiple processes.
We could either time by just timing the total elapsed time, although this gives no indication on the accuracy of the measurement, I suppose it does give an estimate.
BTW: my personal measurements indicate that:
BalancingLearnerV2 is somewhat faster that V1 in combination with Learner1D.
BalancingLearnerV2 is quite a bit slower than V1 in combination with Learner2D.
BalancingLearnerV2 is many times faster than V1 in combination with LearnerND. (this is of course due to the
with restore(self)
which is extremely slow.)I will however check if I can increase the performance of BalancingLearnerV2 with the Learner2D. I know it can be done when we would accept a slight error, yet preferably we wouldn't of course.
Would it be possible to add a test for that?
added tests and fixed it in !113 (merged)
Edited by Jorn Hoofwijkmentioned in issue #60 (closed)
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@Jorn I've changed your code a bit and implemented the
V2
as aBalancingLearner
strategy
that can be set. What do you think?added 1 commit
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assigned to @jbweston
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mentioned in issue #125 (closed)