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38 lines
1.5 KiB
38 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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"""lr generator for ncf"""
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import math
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def _linear_warmup_learning_rate(current_step, warmup_steps, base_lr, init_lr):
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lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps)
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learning_rate = float(init_lr) + lr_inc * current_step
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return learning_rate
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def _cosine_learning_rate(current_step, base_lr, warmup_steps, decay_steps):
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base = float(current_step - warmup_steps) / float(decay_steps)
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learning_rate = (1 + math.cos(base * math.pi)) / 2 * base_lr
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return learning_rate
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def dynamic_lr(base_lr, total_steps, warmup_steps):
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"""dynamic learning rate generator"""
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lr = []
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for i in range(total_steps):
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if i < warmup_steps:
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lr.append(_linear_warmup_learning_rate(i, warmup_steps, base_lr, base_lr * 0.01))
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else:
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lr.append(_cosine_learning_rate(i, base_lr, warmup_steps, total_steps))
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return lr
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