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257 lines
8.5 KiB
257 lines
8.5 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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"""
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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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import control_flow
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import nn
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import ops
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import tensor
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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', 'noam_decay'
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]
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def _decay_step_counter(begin=0):
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# the first global step is zero in learning rate decay
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global_step = nn.autoincreased_step_counter(
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counter_name='@LR_DECAY_COUNTER@', begin=begin, step=1)
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global_step = tensor.cast(global_step, 'float32')
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return global_step
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def noam_decay(d_model, warmup_steps):
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"""
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Noam decay method. The numpy implementation of noam decay as follows.
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>>> import numpy as np
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>>> lr_value = np.power(d_model, -0.5) * np.min([
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>>> np.power(current_steps, -0.5),
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>>> np.power(warmup_steps, -1.5) * current_steps])
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Please reference `attention is all you need
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<https://arxiv.org/pdf/1706.03762.pdf>`_.
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Args:
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d_model(Variable): The dimensionality of input and output of model.
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warmup_steps(Variable): A super parameter.
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Returns:
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The decayed learning rate.
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"""
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global_step = _decay_step_counter(1)
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with init_on_cpu():
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a = global_step**-0.5
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b = (warmup_steps**-1.5) * global_step
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lr_value = (d_model**-0.5) * ops.elementwise_min(a, b)
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return lr_value
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def exponential_decay(learning_rate, decay_steps, decay_rate, 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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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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global_step = _decay_step_counter()
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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 = ops.floor(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, decay_steps, decay_rate, staircase=False):
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"""Applies natural exponential decay to the initial learning rate.
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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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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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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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global_step = _decay_step_counter()
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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 = ops.floor(div_res)
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decayed_lr = learning_rate * ops.exp(-1 * decay_rate * div_res)
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return decayed_lr
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def inverse_time_decay(learning_rate, decay_steps, decay_rate, staircase=False):
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"""Applies inverse time decay to the initial learning rate.
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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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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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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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global_step = _decay_step_counter()
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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 = ops.floor(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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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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"""
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**polynomial_decay**
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Applies polynomial decay to the initial learning rate.
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.. code-block::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 + end_learning_rate
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Args:
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learning_rate(Variable|float32): A scalar float32 value or a Variable. This
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will be the initial learning rate during training
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decay_steps(int32): A Python `int32` number.
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end_learning_rate(float): A Python `float` number.
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power(float): A Python `float` number
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cycle(bool, Default False): 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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global_step = _decay_step_counter()
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with init_on_cpu():
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if cycle:
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div_res = ops.ceil(global_step / decay_steps)
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zero_var = tensor.fill_constant(
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shape=[1], dtype='float32', value=0.0)
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one_var = tensor.fill_constant(
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shape=[1], dtype='float32', value=1.0)
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with control_flow.Switch() as switch:
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with switch.case(global_step == zero_var):
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tensor.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 = tensor.fill_constant(
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shape=[1], dtype='float32', value=float(decay_steps))
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global_step = ops.elementwise_min(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(boundaries, values):
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"""Applies piecewise decay to the initial learning rate.
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>>> boundaries = [10000, 20000]
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>>> values = [1.0, 0.5, 0.1]
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>>>
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>>> if step < 10000:
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>>> learning_rate = 1.0
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>>> elif 10000 <= 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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if len(values) - len(boundaries) != 1:
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raise ValueError("len(values) - len(boundaries) should be 1")
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global_step = _decay_step_counter()
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with init_on_cpu():
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lr = tensor.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 control_flow.Switch() as switch:
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for i in range(len(boundaries)):
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boundary_val = tensor.fill_constant(
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shape=[1], dtype='float32', value=float(boundaries[i]))
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value_var = tensor.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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tensor.assign(value_var, lr)
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last_value_var = tensor.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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tensor.assign(last_value_var, lr)
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return lr
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