# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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 paddle
from paddle import nn
scheduler_classes = dict(
ReduceOnPlateau=paddle.optimizer.lr.ReduceOnPlateau,
lambda_decay=paddle.optimizer.lr.LambdaDecay,
step_decay=paddle.optimizer.lr.StepDecay,
multistep_decay=paddle.optimizer.lr.MultiStepDecay,
exponential_decay=paddle.optimizer.lr.ExponentialDecay,
CosineAnnealingDecay=paddle.optimizer.lr.CosineAnnealingDecay, )
optim_classes = dict(
adadelta=paddle.optimizer.Adadelta,
adagrad=paddle.optimizer.Adagrad,
adam=paddle.optimizer.Adam,
adamax=paddle.optimizer.Adamax,
adamw=paddle.optimizer.AdamW,
lamb=paddle.optimizer.Lamb,
momentum=paddle.optimizer.Momentum,
rmsprop=paddle.optimizer.RMSProp,
sgd=paddle.optimizer.SGD, )
[docs]def build_optimizers(
model: nn.Layer,
optim='adadelta',
max_grad_norm=None,
learning_rate=0.01,
weight_decay=None,
epsilon=1.0e-6, ) -> paddle.optimizer:
optim_class = optim_classes.get(optim)
if optim_class is None:
raise ValueError(f"must be one of {list(optim_classes)}: {optim}")
else:
grad_clip = None
if max_grad_norm:
grad_clip = paddle.nn.ClipGradByGlobalNorm(max_grad_norm)
optim_dict = {}
optim_dict['parameters'] = model.parameters()
optim_dict['learning_rate'] = learning_rate
optim_dict['grad_clip'] = grad_clip
optim_dict['weight_decay'] = weight_decay
if optim_class not in {'momentum', 'sgd'}:
optim_dict['epsilon'] = epsilon
optimizers = optim_class(**optim_dict)
return optimizers