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