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