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79 lines
2.4 KiB
Python
79 lines
2.4 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import paddle
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class L1Decay(object):
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"""
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L1 Weight Decay Regularization, which encourages the weights to be sparse.
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Args:
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factor(float): regularization coeff. Default:0.0.
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"""
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def __init__(self, factor=0.0):
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super(L1Decay, self).__init__()
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self.coeff = factor
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def __call__(self):
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reg = paddle.regularizer.L1Decay(self.coeff)
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return reg
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class L2Decay(object):
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"""
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L2 Weight Decay Regularization, which helps to prevent the model over-fitting.
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Args:
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factor(float): regularization coeff. Default:0.0.
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"""
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def __init__(self, factor=0.0):
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super(L2Decay, self).__init__()
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self.coeff = float(factor)
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def __call__(self):
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return self.coeff
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class CosineL2Decay(object):
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"""
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L2 Weight Decay with cosine annealing schedule.
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Anneals the weight decay coefficient from `factor` to `end_factor`
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following a cosine curve over total training steps, with optional
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linear warmup. Avoids over-regularizing small-capacity models.
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Reference: EfficientNetV2 (Tan & Le, 2021) - "annealing the loss
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incurred by weight decay regularization over the course of training".
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Args:
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factor(float): initial weight decay coefficient.
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end_factor(float): final weight decay coefficient. Default: 0.0.
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warmup_epoch(int|float): warmup epochs (same as lr warmup). Default: 0.
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"""
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def __init__(self, factor=5e-5, end_factor=0.0, warmup_epoch=0):
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super(CosineL2Decay, self).__init__()
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self.start_factor = float(factor)
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self.end_factor = float(end_factor)
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self.warmup_epoch = warmup_epoch
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def __call__(self):
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return self.start_factor
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