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126 lines
4.4 KiB
126 lines
4.4 KiB
# Copyright (c) 2016 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 layers
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from framework import Variable
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__all__ = ['exponential_decay', 'natural_exp_decay', 'inverse_time_decay']
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"""
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When training a model, it's often useful to decay the
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learning rate during training process, this is called
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learning_rate_decay. There are many strategies to do
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this, this module will provide some classical method.
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User can also implement their own learning_rate_decay
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strategy according to this module.
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"""
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def exponential_decay(learning_rate,
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global_step,
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decay_steps,
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decay_rate,
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staircase=False):
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"""Applies exponential decay to the learning rate.
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```python
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decayed_learning_rate = learning_rate *
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decay_rate ^ (global_step / decay_steps)
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```
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Args:
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learning_rate: A scalar float32 value or a Variable. This
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will be the initial learning rate during training
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global_step: A Variable that record the training step.
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decay_steps: A Python `int32` number.
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decay_rate: A Python `float` number.
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staircase: Boolean. If set true, decay the learning rate every decay_steps.
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Returns:
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The decayed learning rate
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"""
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if not isinstance(global_step, Variable):
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raise ValueError("global_step is required for exponential_decay.")
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# update learning_rate
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div_res = global_step / decay_steps
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if staircase:
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div_res = layers.floor(x=div_res)
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return learning_rate * (decay_rate**div_res)
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def natural_exp_decay(learning_rate,
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global_step,
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decay_steps,
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decay_rate,
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staircase=False):
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"""Applies natural exponential decay to the initial learning rate.
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```python
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if not staircase:
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decayed_learning_rate = learning_rate * exp(- decay_rate * (global_step / decay_steps))
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else:
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decayed_learning_rate = learning_rate * exp(- decay_rate * (global_step / decay_steps))
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```
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Args:
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learning_rate: A scalar float32 value or a Variable. This
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will be the initial learning rate during training
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global_step: A Variable that record the training step.
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decay_steps: A Python `int32` number.
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decay_rate: A Python `float` number.
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staircase: Boolean. If set true, decay the learning rate every decay_steps.
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Returns:
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The decayed learning rate
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"""
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if not isinstance(global_step, Variable):
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raise ValueError("global_step is required for natural_exp_decay.")
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div_res = global_step / decay_steps
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if staircase:
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div_res = layers.floor(x=div_res)
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return learning_rate * layers.exp(x=(-1 * decay_rate * div_res))
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def inverse_time_decay(learning_rate,
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global_step,
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decay_steps,
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decay_rate,
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staircase=False):
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"""Applies inverse time decay to the initial learning rate.
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```python
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if staircase:
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decayed_learning_rate = learning_rate / (1 + decay_rate * floor(global_step / decay_step))
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else
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decayed_learning_rate = learning_rate / (1 + decay_rate * global_step / decay_step)
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```
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Args:
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learning_rate: A scalar float32 value or a Variable. This
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will be the initial learning rate during training
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global_step: A Variable that record the training step.
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decay_steps: A Python `int32` number.
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decay_rate: A Python `float` number.
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staircase: Boolean. If set true, decay the learning rate every decay_steps.
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Returns:
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The decayed learning rate
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"""
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if not isinstance(global_step, Variable):
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raise ValueError("global_step is required for inverse_time_decay.")
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div_res = global_step / decay_steps
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if staircase:
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div_res = layers.floor(x=div_res)
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return learning_rate / (1 + decay_rate * div_res)
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