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PaddleSpeech/parakeet/training/optimizer.py

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1.6 KiB

# 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
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, )
def build_optimizers(model: paddle.nn.Layer,
optim='adadelta',
max_grad_norm=None,
learning_rate=0.01) -> 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 = optim_class(
parameters=model.parameters(),
learning_rate=learning_rate,
grad_clip=grad_clip)
optimizers = optim
return optimizers