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- # copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- import math
- from paddle.optimizer.lr import LRScheduler
- class CyclicalCosineDecay(LRScheduler):
- def __init__(self,
- learning_rate,
- T_max,
- cycle=1,
- last_epoch=-1,
- eta_min=0.0,
- verbose=False):
- """
- Cyclical cosine learning rate decay
- A learning rate which can be referred in https://arxiv.org/pdf/2012.12645.pdf
- Args:
- learning rate(float): learning rate
- T_max(int): maximum epoch num
- cycle(int): period of the cosine decay
- last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
- eta_min(float): minimum learning rate during training
- verbose(bool): whether to print learning rate for each epoch
- """
- super(CyclicalCosineDecay, self).__init__(learning_rate, last_epoch,
- verbose)
- self.cycle = cycle
- self.eta_min = eta_min
- def get_lr(self):
- if self.last_epoch == 0:
- return self.base_lr
- reletive_epoch = self.last_epoch % self.cycle
- lr = self.eta_min + 0.5 * (self.base_lr - self.eta_min) * \
- (1 + math.cos(math.pi * reletive_epoch / self.cycle))
- return lr
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