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[FEATURE] Add "Reduce LR On Plateau" scheduler #897
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Job PR-897/1 is complete. |
szhengac
reviewed
Aug 27, 2019
| else: # mode == 'max': | ||
| self.mode_worse = -np.Inf | ||
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| self.is_better = partial(self._cmp, mode, threshold_mode, threshold) |
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Why do you want to use partial? mode, threshold_mode, threshold are simply class variables, so you can simply use them by adding self. to the prefix.
| def in_cooldown(self): | ||
| return self.cooldown_counter > 0 | ||
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| def _cmp(self, mode, threshold_mode, threshold, a, best): |
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The current design does not look very scalable. That means it requires hard coding if we would like add some changes/schedules.
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Thanks for the comments.
I can consider more flexible class desine.
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@haven-jeon gentle ping |
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Description
The LR scheduler that reduce learning rate when a metric has stopped improving. #887
Modified code from https://pytorch.org/docs/stable/optim.html#torch.optim.lr_scheduler.ReduceLROnPlateau
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