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47 lines
1.6 KiB
47 lines
1.6 KiB
3 years ago
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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 paddle
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optim_classes = dict(
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adadelta=paddle.optimizer.Adadelta,
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adagrad=paddle.optimizer.Adagrad,
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adam=paddle.optimizer.Adam,
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adamax=paddle.optimizer.Adamax,
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adamw=paddle.optimizer.AdamW,
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lamb=paddle.optimizer.Lamb,
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momentum=paddle.optimizer.Momentum,
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rmsprop=paddle.optimizer.RMSProp,
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sgd=paddle.optimizer.SGD, )
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def build_optimizers(model: paddle.nn.Layer,
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optim='adadelta',
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max_grad_norm=None,
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learning_rate=0.01) -> paddle.optimizer:
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optim_class = optim_classes.get(optim)
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if optim_class is None:
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raise ValueError(f"must be one of {list(optim_classes)}: {optim}")
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else:
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grad_clip = None
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if max_grad_norm:
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grad_clip = paddle.nn.ClipGradByGlobalNorm(max_grad_norm)
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optim = optim_class(
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parameters=model.parameters(),
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learning_rate=learning_rate,
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grad_clip=grad_clip)
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optimizers = optim
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return optimizers
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