Source code for mxnet.lr_scheduler

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"""Scheduling learning rate."""
import logging

[docs]class LRScheduler(object): """Base class of a learning rate scheduler. A scheduler returns a new learning rate based on the number of updates that have been performed. Parameters ---------- base_lr : float, optional The initial learning rate. """ def __init__(self, base_lr=0.01): self.base_lr = base_lr
[docs] def __call__(self, num_update): """Return a new learning rate. The ``num_update`` is the upper bound of the number of updates applied to every weight. Assume the optimizer has updated *i*-th weight by *k_i* times, namely ``optimizer.update(i, weight_i)`` is called by *k_i* times. Then:: num_update = max([k_i for all i]) Parameters ---------- num_update: int the maximal number of updates applied to a weight. """ raise NotImplementedError("must override this")
[docs]class FactorScheduler(LRScheduler): """Reduce the learning rate by a factor for every *n* steps. It returns a new learning rate by:: base_lr * pow(factor, floor(num_update/step)) Parameters ---------- step : int Changes the learning rate for every n updates. factor : float, optional The factor to change the learning rate. stop_factor_lr : float, optional Stop updating the learning rate if it is less than this value. """ def __init__(self, step, factor=1, stop_factor_lr=1e-8): super(FactorScheduler, self).__init__() if step < 1: raise ValueError("Schedule step must be greater or equal than 1 round") if factor > 1.0: raise ValueError("Factor must be no more than 1 to make lr reduce") self.step = step self.factor = factor self.stop_factor_lr = stop_factor_lr self.count = 0 def __call__(self, num_update): # NOTE: use while rather than if (for continuing training via load_epoch) while num_update > self.count + self.step: self.count += self.step self.base_lr *= self.factor if self.base_lr < self.stop_factor_lr: self.base_lr = self.stop_factor_lr logging.info("Update[%d]: now learning rate arrived at %0.5e, will not " "change in the future", num_update, self.base_lr) else: logging.info("Update[%d]: Change learning rate to %0.5e", num_update, self.base_lr) return self.base_lr
[docs]class MultiFactorScheduler(LRScheduler): """Reduce the learning rate by given a list of steps. Assume there exists *k* such that:: step[k] <= num_update and num_update < step[k+1] Then calculate the new learning rate by:: base_lr * pow(factor, k+1) Parameters ---------- step: list of int The list of steps to schedule a change factor: float The factor to change the learning rate. """ def __init__(self, step, factor=1): super(MultiFactorScheduler, self).__init__() assert isinstance(step, list) and len(step) >= 1 for i, _step in enumerate(step): if i != 0 and step[i] <= step[i-1]: raise ValueError("Schedule step must be an increasing integer list") if _step < 1: raise ValueError("Schedule step must be greater or equal than 1 round") if factor > 1.0: raise ValueError("Factor must be no more than 1 to make lr reduce") self.step = step self.cur_step_ind = 0 self.factor = factor self.count = 0 def __call__(self, num_update): # NOTE: use while rather than if (for continuing training via load_epoch) while self.cur_step_ind <= len(self.step)-1: if num_update > self.step[self.cur_step_ind]: self.count = self.step[self.cur_step_ind] self.cur_step_ind += 1 self.base_lr *= self.factor logging.info("Update[%d]: Change learning rate to %0.5e", num_update, self.base_lr) else: return self.base_lr return self.base_lr