File size: 2,492 Bytes
8207382 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | # 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import paddle
class L1Decay(object):
"""
L1 Weight Decay Regularization, which encourages the weights to be sparse.
Args:
factor(float): regularization coeff. Default:0.0.
"""
def __init__(self, factor=0.0):
super(L1Decay, self).__init__()
self.coeff = factor
def __call__(self):
reg = paddle.regularizer.L1Decay(self.coeff)
return reg
class L2Decay(object):
"""
L2 Weight Decay Regularization, which helps to prevent the model over-fitting.
Args:
factor(float): regularization coeff. Default:0.0.
"""
def __init__(self, factor=0.0):
super(L2Decay, self).__init__()
self.coeff = float(factor)
def __call__(self):
return self.coeff
class CosineL2Decay(object):
"""
L2 Weight Decay with cosine annealing schedule.
Anneals the weight decay coefficient from `factor` to `end_factor`
following a cosine curve over total training steps, with optional
linear warmup. Avoids over-regularizing small-capacity models.
Reference: EfficientNetV2 (Tan & Le, 2021) - "annealing the loss
incurred by weight decay regularization over the course of training".
Args:
factor(float): initial weight decay coefficient.
end_factor(float): final weight decay coefficient. Default: 0.0.
warmup_epoch(int|float): warmup epochs (same as lr warmup). Default: 0.
"""
def __init__(self, factor=5e-5, end_factor=0.0, warmup_epoch=0):
super(CosineL2Decay, self).__init__()
self.start_factor = float(factor)
self.end_factor = float(end_factor)
self.warmup_epoch = warmup_epoch
def __call__(self):
return self.start_factor
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