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242 lines
8.3 KiB
242 lines
8.3 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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from initializer import init_on_cpu
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__all__ = [
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'exponential_decay', 'natural_exp_decay', 'inverse_time_decay',
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'polynomial_decay', 'piecewise_decay'
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]
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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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with init_on_cpu():
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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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decayed_lr = learning_rate * (decay_rate**div_res)
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return decayed_lr
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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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with init_on_cpu():
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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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decayed_lr = learning_rate * layers.exp(x=(-1 * decay_rate * div_res))
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return decayed_lr
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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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with init_on_cpu():
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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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decayed_lr = learning_rate / (1 + decay_rate * div_res)
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return decayed_lr
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def polynomial_decay(learning_rate,
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global_step,
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decay_steps,
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end_learning_rate=0.0001,
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power=1.0,
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cycle=False):
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"""Applies polynomial decay to the initial learning rate.
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```python
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if cycle:
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decay_steps = decay_steps * ceil(global_step / decay_steps)
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else:
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global_step = min(global_step, decay_steps)
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decayed_learning_rate = (learning_rate - end_learning_rate) *
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(1 - global_step / decay_steps) ^ power +
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end_learning_rate
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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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end_learning_rate: A Python `float` number.
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power: A Python `float` number
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cycle: 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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with init_on_cpu():
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if cycle:
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div_res = layers.ceil(x=(global_step / decay_steps))
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zero_var = layers.fill_constant(
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shape=[1], dtype='float32', value=0.0)
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one_var = layers.fill_constant(
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shape=[1], dtype='float32', value=1.0)
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with layers.Switch() as switch:
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with switch.case(global_step == zero_var):
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layers.assign(input=one_var, output=div_res)
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decay_steps = decay_steps * div_res
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else:
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decay_steps_var = layers.fill_constant(
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shape=[1], dtype='float32', value=float(decay_steps))
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global_step = layers.elementwise_min(
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x=global_step, y=decay_steps_var)
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decayed_lr = (learning_rate - end_learning_rate) * \
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((1 - global_step / decay_steps) ** power) + end_learning_rate
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return decayed_lr
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def piecewise_decay(global_step, boundaries, values):
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"""Applies piecewise decay to the initial learning rate.
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```python
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boundaries = [10000, 20000]
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values = [1.0, 0.5, 0.1]
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if step < 10000:
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learning_rate = 1.0
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elif step >= 10000 and step < 20000:
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learning_rate = 0.5
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else:
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learning_rate = 0.1
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```
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"""
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if len(values) - len(boundaries) != 1:
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raise ValueError("len(values) - len(boundaries) should be 1")
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if not isinstance(global_step, Variable):
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raise ValueError("global_step is required for piecewise_decay.")
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with init_on_cpu():
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lr = layers.create_global_var(
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shape=[1],
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value=0.0,
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dtype='float32',
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persistable=True,
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name="learning_rate")
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with layers.Switch() as switch:
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for i in range(len(boundaries)):
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boundary_val = layers.fill_constant(
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shape=[1], dtype='float32', value=float(boundaries[i]))
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value_var = layers.fill_constant(
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shape=[1], dtype='float32', value=float(values[i]))
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with switch.case(global_step < boundary_val):
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layers.assign(value_var, lr)
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last_value_var = layers.fill_constant(
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shape=[1],
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dtype='float32',
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value=float(values[len(values) - 1]))
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with switch.default():
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layers.assign(last_value_var, lr)
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return lr
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