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- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # 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.
- # ============================================================================
- """learning rate generator"""
- import numpy as np
-
-
- def get_lr(current_step, lr_max, total_epochs, steps_per_epoch):
- """
- generate learning rate array
-
- Args:
- current_step(int): current steps of the training
- lr_max(float): max learning rate
- total_epochs(int): total epoch of training
- steps_per_epoch(int): steps of one epoch
-
- Returns:
- np.array, learning rate array
- """
- lr_each_step = []
- total_steps = steps_per_epoch * total_epochs
- decay_epoch_index = [0.8 * total_steps]
- for i in range(total_steps):
- if i < decay_epoch_index[0]:
- lr = lr_max
- else:
- lr = lr_max * 0.1
- lr_each_step.append(lr)
- lr_each_step = np.array(lr_each_step).astype(np.float32)
- learning_rate = lr_each_step[current_step:]
-
- return learning_rate
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