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learning_rate_scheduler.py
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learning_rate_scheduler.py
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# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import math
import warnings
from .. import unique_name
from ..framework import Variable
from ..data_feeder import check_type
__all__ = [
'NoamDecay', 'PiecewiseDecay', 'NaturalExpDecay', 'ExponentialDecay',
'InverseTimeDecay', 'PolynomialDecay', 'CosineDecay', 'LinearLrWarmup',
'ReduceLROnPlateau', 'StepDecay', 'MultiStepDecay', 'LambdaDecay'
]
class LearningRateDecay(object):
"""
Base class of learning rate decay
Define the common interface of an LearningRateDecay.
User should not use this class directly,
but need to use one of it's implementation.
"""
def __init__(self, begin=0, step=1, dtype='float32'):
self.step_num = begin
self.step_size = step
self.dtype = dtype
def __call__(self):
lr = self.step()
if isinstance(lr, float):
lr = self.create_lr_var(lr)
self.step_num += self.step_size
return lr
def create_lr_var(self, lr):
"""
convert lr from float to variable
Args:
lr: learning rate
Returns:
learning rate variable
"""
from .. import layers
lr = layers.create_global_var(
name=unique_name.generate("learning_rate"),
shape=[1],
value=float(lr),
dtype=self.dtype,
persistable=False)
return lr
# Note: If you want to change what optimizer.state_dict stores, just overwrite this functions,
# "self.step_num" will be stored by default.
def state_dict(self):
"""
Returns the state of the scheduler as a :class:`dict`.
It is a subset of self.__dict__ .
"""
self._state_keys()
state_dict = {}
for key in self.keys:
if key not in self.__dict__:
continue
value = self.__dict__[key]
if isinstance(value, Variable):
assert value.shape == [
1
], "shape of Variable in state_dict must be [1] {}".format(
value.shape)
value = value.numpy()[0]
state_dict[key] = value
return state_dict
def _state_keys(self):
"""
set the keys in self.__dict__ that are needed to be saved.
"""
self.keys = ['step_num']
def set_state_dict(self, state_dict):
"""
Loads the schedulers state.
"""
self._state_keys()
for key in self.keys:
if key in state_dict:
self.__dict__[key] = state_dict[key]
else:
raise RuntimeError(
"Please check whether state_dict is correct for optimizer. Can't find [ {} ] in state_dict".
format(key))
if len(state_dict) > len(self.keys):
warnings.warn(
"There are some unused values in state_dict. Maybe the optimizer have different 'LearningRateDecay' when invoking state_dict and set_dict"
)
# [aliases] Compatible with old method names
set_dict = set_state_dict
def step(self):
raise NotImplementedError()
class PiecewiseDecay(LearningRateDecay):
"""
:api_attr: imperative
Piecewise decay scheduler.
The algorithm can be described as the code below.
.. code-block:: text
boundaries = [10000, 20000]
values = [1.0, 0.5, 0.1]
if global_step < 10000:
learning_rate = 1.0
elif 10000 <= global_step < 20000:
learning_rate = 0.5
else:
learning_rate = 0.1
Parameters:
boundaries(list): A list of steps numbers. The type of element in the list is python int.
values(list): A list of learning rate values that will be picked during
different step boundaries. The type of element in the list is python float.
begin(int): The begin step to initialize the global_step in the description above.
step(int, optional): The step size used to calculate the new global_step in the description above.
The default value is 1.
dtype(str, optional): The data type used to create the learning rate variable. The data type can be set as
'float32', 'float64'. The default value is 'float32'.
Returns:
None.
Examples:
.. code-block:: python
import paddle.fluid as fluid
boundaries = [10000, 20000]
values = [1.0, 0.5, 0.1]
with fluid.dygraph.guard():
emb = fluid.dygraph.Embedding( [10, 10] )
optimizer = fluid.optimizer.SGD(
learning_rate=fluid.dygraph.PiecewiseDecay(boundaries, values, 0),
parameter_list = emb.parameters() )
"""
def __init__(self, boundaries, values, begin, step=1, dtype='float32'):
super(PiecewiseDecay, self).__init__(begin, step, dtype)
self.boundaries = boundaries
self.values = values
self.vars = []
for value in values:
self.vars.append(value)
def step(self):
for i in range(len(self.boundaries)):
if self.step_num < self.boundaries[i]:
return self.vars[i]
return self.create_lr_var(self.vars[len(self.values) - 1])
class NaturalExpDecay(LearningRateDecay):
r"""
:api_attr: imperative
Applies natural exponential decay to the initial learning rate.
The algorithm can be described as following.
.. math::
decayed\_learning\_rate = learning\_rate * e^{y}
If staircase is set to False, then:
.. math::
y = - decay\_rate * \\frac{global\_step}{decay\_steps}
If staircase is set to True, then:
.. math::
y = - decay\_rate * math.floor(\\frac{global\_step}{decay\_steps})
Parameters:
learning_rate(Variable|float): The initial learning rate. If the type
is Variable, it's a tensor with shape [1], the data type can be
float32 or float64. It also can be set to python int number.
decay_steps(int): The decay step size. It determines the decay cycle.
decay_rate(int): The decay rate.
staircase(bool, optional): If set to True, decay the learning rate at discrete intervals. The
default value is False.
begin(int, optional): The begin step. The initial value of global_step described above. The default value is 0.
step(int, optional): The step size used to calculate the new global_step in the description above.
The default value is 1.
dtype(str, optional): The data type used to create the learning rate variable. The data type can be set as
'float32', 'float64'. The default value is 'float32'.
Returns:
None.
Examples:
.. code-block:: python
import paddle.fluid as fluid
base_lr = 0.1
with fluid.dygraph.guard():
emb = fluid.dygraph.Embedding([10, 10])
sgd_optimizer = fluid.optimizer.SGD(
learning_rate=fluid.dygraph.NaturalExpDecay(
learning_rate=base_lr,
decay_steps=10000,
decay_rate=0.5,
staircase=True),
parameter_list=emb.parameters())
"""
def __init__(self,
learning_rate,
decay_steps,
decay_rate,
staircase=False,
begin=0,
step=1,
dtype='float32'):
super(NaturalExpDecay, self).__init__(begin, step, dtype)
self.learning_rate = learning_rate
self.decay_steps = decay_steps
self.decay_rate = decay_rate
self.staircase = staircase
def step(self):
from .. import layers
div_res = self.create_lr_var(self.step_num / self.decay_steps)
if self.staircase:
div_res = layers.floor(div_res)
decayed_lr = self.learning_rate * layers.exp(-1 * self.decay_rate *
div_res)
return decayed_lr
class ExponentialDecay(LearningRateDecay):
r"""
:api_attr: imperative
Applies exponential decay to the learning rate.
The algorithm can be described as following.
.. math::
decayed\_learning\_rate = learning\_rate * decay\_rate ^ y
If staircase is set to False, then:
.. math::
y = \\frac{global\_step}{decay\_steps}
If staircase is set to True, then:
.. math::
y = math.floor(\\frac{global\_step}{decay\_steps})
Parameters:
learning_rate(Variable|float): The initial learning rate. If the type
is Variable, it's a tensor with shape [1], the data type can be
float32 or float64. It also can be set to python int number.
decay_steps(int): The decay step size. It determines the decay cycle.
decay_rate(float): The decay rate.
staircase(bool, optional): If set to True, decay the learning rate at discrete intervals. The
default value is False.
begin(int, optional): The begin step. The initial value of global_step described above. The default value is 0.
step(int, optional): The step size used to calculate the new global_step in the description above.
The default value is 1.
dtype(str, optional): The data type used to create the learning rate variable. The data type can be set as
'float32', 'float64'. The default value is 'float32'.
Returns:
None.
Examples:
.. code-block:: python
import paddle.fluid as fluid
base_lr = 0.1
with fluid.dygraph.guard():
sgd_optimizer = fluid.optimizer.SGD(
learning_rate=fluid.dygraph.ExponentialDecay(
learning_rate=base_lr,
decay_steps=10000,
decay_rate=0.5,
staircase=True))
"""
def __init__(self,
learning_rate,
decay_steps,
decay_rate,
staircase=False,
begin=0,
step=1,
dtype='float32'):
super(ExponentialDecay, self).__init__(begin, step, dtype)
self.learning_rate = learning_rate
self.decay_steps = decay_steps
self.decay_rate = decay_rate
self.staircase = staircase
def step(self):
from .. import layers
div_res = self.create_lr_var(self.step_num / self.decay_steps)
if self.staircase:
div_res = layers.floor(div_res)
decayed_lr = self.learning_rate * (self.decay_rate**div_res)
return decayed_lr
class InverseTimeDecay(LearningRateDecay):
r"""
:api_attr: imperative
Applies inverse time decay to the initial learning rate.
The algorithm can be described as following.
If staircase is set to False, then:
.. math::
decayed\_learning\_rate = \\frac{learning\_rate}{1 + decay\_rate * \\frac{global\_step}{decay\_step}}
If staircase is set to True, then:
.. math::
decayed\_learning\_rate = \\frac{learning\_rate}{1 + decay\_rate * math.floor(\\frac{global\_step}{decay\_step})}
Parameters:
learning_rate(Variable|float): The initial learning rate. If the type
is Variable, it's a tensor with shape [1], the data type can be
float32 or float64. It also can be set to python int number.
decay_steps(int): The decay step size. It determines the decay cycle.
decay_rate(float): The decay rate.
staircase(bool, optional): If set to True, decay the learning rate at discrete intervals. The
default value is False.
begin(int, optional): The begin step. The initial value of global_step described above. The default value is 0.
step(int, optional): The step size used to calculate the new global_step in the description above.
The default value is 1.
dtype(str, optional): The data type used to create the learning rate variable. The data type can be
'float32', 'float64'. The default value is 'float32'.
Returns:
None.
Examples:
.. code-block:: python
import paddle.fluid as fluid
base_lr = 0.1
with fluid.dygraph.guard():
emb = fluid.dygraph.Embedding([10, 10])
sgd_optimizer = fluid.optimizer.SGD(
learning_rate=fluid.dygraph.InverseTimeDecay(
learning_rate=base_lr,
decay_steps=10000,
decay_rate=0.5,
staircase=True),
parameter_list = emb.parameters())
"""
def __init__(self,
learning_rate,
decay_steps,
decay_rate,
staircase=False,
begin=0,
step=1,
dtype='float32'):
super(InverseTimeDecay, self).__init__(begin, step, dtype)
self.learning_rate = learning_rate
self.decay_steps = decay_steps
self.decay_rate = decay_rate
self.staircase = staircase
def step(self):
from .. import layers
div_res = self.create_lr_var(self.step_num / self.decay_steps)
if self.staircase:
div_res = layers.floor(div_res)
decayed_lr = self.learning_rate / (1 + self.decay_rate * div_res)
return decayed_lr
class PolynomialDecay(LearningRateDecay):
r"""
:api_attr: imperative
Applies polynomial decay to the initial learning rate.
The algorithm can be described as following.
If cycle is set to True, then:
.. math::
decay\_steps & = decay\_steps * math.ceil(\\frac{global\_step}{decay\_steps})
decayed\_learning\_rate & = (learning\_rate-end\_learning\_rate)*(1-\\frac{global\_step}{decay\_steps})^{power}+end\_learning\_rate
If cycle is set to False, then:
.. math::
global\_step & = min(global\_step, decay\_steps)
decayed\_learning\_rate & = (learning\_rate-end\_learning\_rate)*(1-\\frac{global\_step}{decay\_steps})^{power}+end\_learning\_rate
Parameters:
learning_rate(Variable|float): The initial learning rate. If the type
is Variable, it's a tensor with shape [1], the data type can be
float32 or float64. It also can be set to python int number.
decay_steps(int): The decay step size. It determines the decay cycle.
end_learning_rate(float, optional): The minimum final learning rate. The default value is 0.0001.
power(float, optional): Power of polynomial. The default value is 1.0.
cycle(bool, optional): If set true, decay the learning rate every decay_steps. The default value is False.
begin(int, optional): The begin step. The initial value of global_step described above. The default value is 0.
step(int, optional): The step size used to calculate the new global_step in the description above.
The default value is 1.
dtype(str, optional): The data type used to create the learning rate variable. The data type can be set as
'float32', 'float64'. The default value is 'float32'.
Returns:
None.
Examples:
.. code-block:: python
import paddle.fluid as fluid
start_lr = 0.01
total_step = 5000
end_lr = 0
with fluid.dygraph.guard():
emb = fluid.dygraph.Embedding( [10, 10])
optimizer = fluid.optimizer.SGD(
learning_rate = fluid.dygraph.PolynomialDecay(
start_lr, total_step, end_lr, power=1.0),
parameter_list = emb.parameters())
"""
def __init__(self,
learning_rate,
decay_steps,
end_learning_rate=0.0001,
power=1.0,
cycle=False,
begin=0,
step=1,
dtype='float32'):
super(PolynomialDecay, self).__init__(begin, step, dtype)
self.learning_rate = learning_rate
self.decay_steps = decay_steps
self.end_learning_rate = end_learning_rate
self.power = power
self.cycle = cycle
def step(self):
from .. import layers
tmp_step_num = self.step_num
tmp_decay_steps = self.decay_steps
if self.cycle:
div_res = layers.ceil(
self.create_lr_var(tmp_step_num / float(self.decay_steps)))
if tmp_step_num == 0:
div_res = self.create_lr_var(1.0)
tmp_decay_steps = self.decay_steps * div_res
else:
tmp_step_num = self.create_lr_var(tmp_step_num
if tmp_step_num < self.decay_steps
else self.decay_steps)
decayed_lr = (self.learning_rate - self.end_learning_rate) * \
((1 - tmp_step_num / tmp_decay_steps) ** self.power) + self.end_learning_rate
return decayed_lr
class CosineDecay(LearningRateDecay):
r"""
:api_attr: imperative
Applies cosine decay to the learning rate.
The algorithm can be described as following.
.. math::
decayed\_learning\_rate = learning\_rate * 0.5 * (math.cos(global\_step * \\frac{math.pi}{step\_each\_epoch} ) + 1)
Parameters:
learning_rate(Variable|float): The initial learning rate. If the type
is Variable, it's a tensor with shape [1], the data type can be
float32 or float64. It also can be set to python int number.
step_each_epoch(int): The number of steps in an epoch.
epochs(int): The number of epochs.
begin(int, optional): The begin step. The initial value of global_step described above. The default value is 0.
step(int, optional): The step size used to calculate the new global_step in the description above.
The default value is 1.
dtype(str, optional): The data type used to create the learning rate variable. The data type can be set as
'float32', 'float64'. The default value is 'float32'.
Returns:
None.
Examples:
.. code-block:: python
base_lr = 0.1
with fluid.dygraph.guard():
optimizer = fluid.optimizer.SGD(
learning_rate = fluid.dygraph.CosineDecay(
base_lr, 10000, 120) )
"""
def __init__(self,
learning_rate,
step_each_epoch,
epochs,
begin=0,
step=1,
dtype='float32'):
super(CosineDecay, self).__init__(begin, step, dtype)
self.learning_rate = learning_rate
self.step_each_epoch = step_each_epoch
self.epochs = epochs
def step(self):
from .. import layers
cur_epoch = layers.floor(
self.create_lr_var(self.step_num / self.step_each_epoch))
decayed_lr = self.learning_rate * 0.5 * (
layers.cos(cur_epoch * math.pi / self.epochs) + 1)
return decayed_lr
class NoamDecay(LearningRateDecay):
r"""
:api_attr: imperative
Applies Noam decay to the initial learning rate.
The algorithm can be described as following.
.. math::
decayed\_learning\_rate = learning\_rate * d_{model}^{-0.5} * min(global\_step^{-0.5}, global\_step * warmup\_steps^{-1.5})
Please reference `attention is all you need <https://arxiv.org/pdf/1706.03762.pdf>`_
Parameters:
d$_{model}$(Variable|int): The dimensionality of input and output feature vector of model. If type is Variable,
it's a tensor with shape [1] and the data type can be int32 or int64. The type can also be python int.
warmup_steps(Variable|int): The number of warmup steps. A super parameter. If type is Variable,
it's a tensor with shape [1] and the data type can be int32 or int64. The type can also be python int.
begin(int, optional): The begin step. The initial value of global_step described above. The default value is 0.
step(int, optional): The step size used to calculate the new global_step in the description above.
The default value is 1.
dtype(str, optional): The data type used to create the learning rate variable. The data type can be set as
'float32', 'float64'. The default value is 'float32'.
learning_rate(Variable|float|int): The initial learning rate. If the type
is Variable, it's a tensor with shape [1], the data type can be
float32 or float64. It also can be set to python int number. Default 1.0
Returns:
None.
Examples:
.. code-block:: python
import paddle.fluid as fluid
warmup_steps = 100
learning_rate = 0.01
with fluid.dygraph.guard():
emb = fluid.dygraph.Embedding([10, 10])
optimizer = fluid.optimizer.SGD(
learning_rate = fluid.dygraph.NoamDecay(
1/(warmup_steps *(learning_rate ** 2)),
warmup_steps),
parameter_list = emb.parameters())
"""
def __init__(self,
d_model,
warmup_steps,
begin=1,
step=1,
dtype='float32',
learning_rate=1.0):
super(NoamDecay, self).__init__(begin, step, dtype)
self.learning_rate = learning_rate
self.d_model = d_model
self.warmup_steps = warmup_steps
def step(self):
from .. import layers
a = self.create_lr_var(self.step_num**-0.5)
b = self.create_lr_var((self.warmup_steps**-1.5) * self.step_num)
lr_value = self.learning_rate * (self.d_model
**-0.5) * layers.elementwise_min(a, b)
return lr_value
class LinearLrWarmup(LearningRateDecay):
"""
:api_attr: imperative
This operator use the linear learning rate warm up strategy to adjust the learning rate preliminarily before the normal learning rate scheduling.
For more information, please refer to `Bag of Tricks for Image Classification with Convolutional Neural Networks <https://arxiv.org/abs/1812.01187>`_
When global_step < warmup_steps, learning rate is updated as:
.. code-block:: text
linear_step = end_lr - start_lr
lr = start_lr + linear_step * (global_step / warmup_steps)
where start_lr is the initial learning rate, and end_lr is the final learning rate;
When global_step >= warmup_steps, learning rate is updated as:
.. code-block:: text
lr = learning_rate
where lr is the learning_rate after warm-up.
Args:
learning_rate (Variable|float): Learning_rate after warm-up, it could be 1D-Tensor or single value with the data type of float32.
warmup_steps (int): Steps for warm up.
start_lr (float): Initial learning rate of warm up.
end_lr (float): Final learning rate of warm up.
begin(int, optional): The begin step. The initial value of global_step described above. The default value is 0.
step(int, optional): The step size used to calculate the new global_step in the description above.
The default value is 1.
dtype(str, optional): The data type used to create the learning rate variable. The data type can be set as
'float32', 'float64'. The default value is 'float32'.
Returns:
Variable: Warm-up learning rate with the same data type as learning_rate.
Examples:
.. code-block:: python
import paddle.fluid as fluid
learning_rate = 0.1
warmup_steps = 50
start_lr = 0
end_lr = 0.1
with fluid.dygraph.guard():
lr_decay = fluid.dygraph.LinearLrWarmup( learning_rate, warmup_steps, start_lr, end_lr)
"""
def __init__(self,
learning_rate,
warmup_steps,
start_lr,
end_lr,
begin=1,
step=1,
dtype='float32'):
super(LinearLrWarmup, self).__init__(begin, step, dtype)
type_check = isinstance(learning_rate, float) or isinstance(
learning_rate, int) or isinstance(learning_rate, LearningRateDecay)
if not type_check:
raise TypeError(
"the type of learning_rate should be [int, float or LearningRateDecay], the current type is {}".
format(learning_rate))
self.learning_rate = learning_rate
self.warmup_steps = warmup_steps
self.start_lr = start_lr
assert end_lr > start_lr, "end_lr {} must be greater than start_lr {}".format(
end_lr, start_lr)
self.lr_ratio_before_warmup = (
float(end_lr) - float(start_lr)) / float(warmup_steps)
def step(self):
base_lr = self.learning_rate
if isinstance(self.learning_rate, LearningRateDecay):
base_lr = base_lr()
from .. import layers
if self.step_num < self.warmup_steps:
return self.lr_ratio_before_warmup * self.step_num + self.start_lr
else:
return base_lr
class ReduceLROnPlateau(LearningRateDecay):
"""
:api_attr: imperative
Reduce learning rate when ``loss`` has stopped descending. Models often benefit from reducing the learning rate
by 2 to 10 times once model performance has no longer improvement.
The ``loss`` is the one which has been pass into ``step`` , it must be 1-D Tensor with shape [1]. When ``loss``
stop descending for a ``patience`` number of epochs, the learning rate will be reduced to ``learning_rate * decay_rate`` .
(Specially, ``mode`` can also be set to ``'max`` , in this case, when ``loss`` stop ascending for a ``patience`` number
of epochs, the learning rate will be reduced.)
In addition, After each reduction, it will wait a ``cooldown`` number of epochs before resuming normal operation.
Args:
learning_rate (Variable|float|int): The initial learning rate. It can be set to python float or int number.
If the type is Variable, it should be 1-D Tensor with shape [1], the data type can be 'float32' or 'float64'.
mode (str, optional): ``'min'`` or ``'max'`` can be selected. Normally, it is ``'min'`` , which means that the
learning rate will reduce when ``loss`` stops descending. Specially, if it's set to ``'max'`` , the learning
rate will reduce when ``loss`` stops ascending. Default: ``'min'`` .
decay_rate (float, optional): The Ratio that the learning rate will be reduced. ``new_lr = origin_lr * decay_rate`` .
It should be less than 1.0. Default: 0.1.
patience (int, optional): When ``loss`` doesn't improve for this number of epochs, learing rate will be reduced.
Default: 10.
verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False``.
threshold (float, optional): ``threshold`` and ``threshold_mode`` will determine the minimum change of ``loss`` .
This make tiny changes of ``loss`` will be ignored. Default: 1e-4.
threshold_mode (str, optional): ``'rel'`` or ``'abs'`` can be selected. In ``'rel'`` mode, the minimum change of ``loss``
is ``last_loss * threshold`` , where ``last_loss`` is ``loss`` in last epoch. In ``'abs'`` mode, the minimum
change of ``loss`` is ``threshold`` . Default: ``'rel'`` .
cooldown (int, optional): The number of epochs to wait before resuming normal operation. Default: 0.
min_lr (float, optional): The lower bound of the learning rate after reduction. Default: 0.
eps (float, optional): Minimal decay applied to lr. If the difference between new and old lr is smaller than eps, the update is
ignored. Default: 1e-8.
dtype (str, optional): The data type used to create the learning rate variable. The data type can be set as
'float32', 'float64'. Default: 'float32'.
Returns:
Reduced learning rate.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
with fluid.dygraph.guard():
x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
linear = fluid.dygraph.Linear(10, 10)
input = fluid.dygraph.to_variable(x)
reduce_lr = fluid.dygraph.ReduceLROnPlateau(
learning_rate = 1.0,
decay_rate = 0.5,
patience = 5,
verbose = True,
cooldown = 3)
adam = fluid.optimizer.Adam(
learning_rate = reduce_lr,
parameter_list = linear.parameters())
for epoch in range(10):
total_loss = 0
for bath_id in range(5):
out = linear(input)
loss = fluid.layers.reduce_mean(out)
total_loss += loss
adam.minimize(loss)
avg_loss = total_loss/5
# adjust learning rate according to avg_loss
reduce_lr.step(avg_loss)
lr = adam.current_step_lr()
print("current avg_loss is %s, current lr is %s" % (avg_loss.numpy()[0], lr))
"""
def __init__(self,
learning_rate,
mode='min',
decay_rate=0.1,
patience=10,
verbose=False,
threshold=1e-4,
threshold_mode='rel',
cooldown=0,
min_lr=0,
eps=1e-8,
dtype='float32'):
super(ReduceLROnPlateau, self).__init__(dtype=dtype)
mode = mode.lower()
if mode not in ['min', 'max']:
raise ValueError('mode ' + mode + ' is unknown!')
self.mode = mode
if decay_rate >= 1.0:
raise ValueError(
'new_lr = origin_lr * decay_rate and decay_rate should be < 1.0.'
)
self.decay_rate = self.create_lr_var(decay_rate)
threshold_mode = threshold_mode.lower()
if threshold_mode not in ['rel', 'abs']:
raise ValueError('threshold mode ' + threshold_mode +
' is unknown!')
self.threshold_mode = threshold_mode
check_type(learning_rate, 'learning_rate', (float, int, Variable),
'ReduceLROnPlateau')
if not isinstance(learning_rate, (float, int, Variable)):
raise TypeError(
"The type of 'learning_rate' in 'ReduceLROnPlateau' must be 'float, int, Variable', but received %s."
% type(learning_rate))
self.learning_rate = learning_rate
self.verbose = verbose
self.patience = patience
self.threshold = threshold
self.threshold_mode = threshold_mode
self.cooldown = cooldown
self.min_lr = self.create_lr_var(min_lr)
self.eps = eps
self.cooldown_counter = 0
self.best_loss = None
self.num_bad_epochs = 0
self.epoch_num = 0
# "cooldown_counter / best_loss / num_bad_epochs / epoch_num / learning_rate" will be stored.
def _state_keys(self):
self.keys = [
'cooldown_counter', 'best_loss', 'num_bad_epochs', 'epoch_num',
'learning_rate'
]
def __call__(self):
if not isinstance(self.learning_rate, Variable):
self.learning_rate = self.create_lr_var(self.learning_rate)
return self.learning_rate
def step(self, loss):
"""
It should be invoked on each epoch. Update the learning rate in optimizer according to ``loss`` .
The new learning rate will take effect on next call to ``optimizer.minimize`` .
Args:
loss (Variable): A ``Variable`` that will be monitored to determine whether the learning rate will reduce.
If it stop descending for a ``patience`` number of epochs, the learning rate will reduce. It should
be 1-D Tensor with shape [1].
Specially, if ``mode`` has been set to ``'max'`` , the learning rate will reduce when it stops ascending.
Returns:
None
Examples:
Please refer to the example of current LearningRateDecay.
"""
# loss must be 1-D Tensor with shape [1]
check_type(loss, 'loss', Variable, 'ReduceLROnPlateau.step')
assert len(loss.shape) == 1 and loss.shape[0] == 1, "the loss.shape " \
"should be (1L,), but the current loss.shape is {}. Maybe that " \
"you should call fluid.layers.mean to process it first.".format(loss.shape)
self.epoch_num += 1
if self.cooldown_counter > 0:
self.cooldown_counter -= 1
else:
if self.best_loss is None or self._is_better(loss, self.best_loss):
self.best_loss = loss
self.num_bad_epochs = 0
else:
self.num_bad_epochs += 1
if self.num_bad_epochs > self.patience:
from .. import layers
self.cooldown_counter = self.cooldown
self.num_bad_epochs = 0
new_lr = layers.elementwise_max(self.learning_rate *
self.decay_rate, self.min_lr)
if self.learning_rate - new_lr > self.eps:
if self.verbose:
old_lr = self.learning_rate.numpy()[0] if isinstance(
self.learning_rate,
Variable) else self.learning_rate
print('Epoch {}: reducing learning rate from {} to {}.'.
format(self.epoch_num, old_lr, new_lr.numpy()[0]))
self.learning_rate = new_lr
def _is_better(self, current, best):
if self.mode == 'min' and self.threshold_mode == 'rel':
return current < best - best * self.threshold
elif self.mode == 'min' and self.threshold_mode == 'abs':
return current < best - self.threshold
elif self.mode == 'max' and self.threshold_mode == 'rel':
return current > best + best * self.threshold
else:
return current > best + self.threshold
class _LearningRateEpochDecay(LearningRateDecay):
"""
:api_attr: imperative
Base class of learning rate decay, which is updated each epoch.
Define the common interface of an _LearningRateEpochDecay.
User should not use this class directly,
but need to use one of it's implementation. And invoke method: `epoch()` each epoch.
"""
def __init__(self, learning_rate, dtype=None):
if not isinstance(learning_rate, (float, int)):
raise TypeError(
"The type of 'learning_rate' must be 'float, int', but received %s."
% type(learning_rate))
if learning_rate < 0:
raise ValueError("Invalid learning rate: {}".format(learning_rate))
self.base_lr = float(learning_rate)
self.epoch_num = -1
self.dtype = dtype
if dtype is None:
self.dtype = "float32"
self.learning_rate = self.create_lr_var(self.base_lr)
self.epoch()
# For those subclass who overload _LearningRateEpochDecay, "self.epoch_num/learning_rate" will be stored by default.
# you can change it for your subclass.
def _state_keys(self):
self.keys = ['epoch_num', 'learning_rate']
def __call__(self):
"""
Return last computed learning rate on current epoch.
"""
if not isinstance(self.learning_rate, Variable):
self.learning_rate = self.create_lr_var(self.learning_rate)
return self.learning_rate
def epoch(self, epoch=None):
"""
compueted learning_rate and update it when invoked.
"""
if epoch is None:
self.epoch_num += 1
else:
self.epoch_num = epoch
self.learning_rate = self.get_lr()
def get_lr(self):
raise NotImplementedError
class StepDecay(_LearningRateEpochDecay):
"""
:api_attr: imperative