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体验新版 GitCode,发现更多精彩内容 >>
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13e89151
编写于
2月 22, 2019
作者:
S
shippingwang
提交者:
ceci3
3月 04, 2019
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差异文件
add cosine decay op, test=develop
上级
b2ce8320
变更
3
隐藏空白更改
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并排
Showing
3 changed file
with
49 addition
and
1 deletion
+49
-1
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-0
python/paddle/fluid/layers/learning_rate_scheduler.py
python/paddle/fluid/layers/learning_rate_scheduler.py
+36
-1
python/paddle/fluid/tests/unittests/test_learning_rate_scheduler.py
...dle/fluid/tests/unittests/test_learning_rate_scheduler.py
+12
-0
未找到文件。
paddle/fluid/API.spec
浏览文件 @
13e89151
...
...
@@ -336,6 +336,7 @@ paddle.fluid.layers.natural_exp_decay ArgSpec(args=['learning_rate', 'decay_step
paddle.fluid.layers.inverse_time_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.layers.polynomial_decay ArgSpec(args=['learning_rate', 'decay_steps', 'end_learning_rate', 'power', 'cycle'], varargs=None, keywords=None, defaults=(0.0001, 1.0, False))
paddle.fluid.layers.piecewise_decay ArgSpec(args=['boundaries', 'values'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.cosine_decay ArgSpec(args=['learning_rate', 'step_each_epoch', 'epochs'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.noam_decay ArgSpec(args=['d_model', 'warmup_steps'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.append_LARS ArgSpec(args=['params_grads', 'learning_rate', 'weight_decay'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.InitState.__init__ ArgSpec(args=['self', 'init', 'shape', 'value', 'init_boot', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, None, False, 'float32'))
...
...
python/paddle/fluid/layers/learning_rate_scheduler.py
浏览文件 @
13e89151
...
...
@@ -28,10 +28,12 @@ from . import ops
from
.
import
tensor
from
..initializer
import
init_on_cpu
from
..framework
import
default_main_program
,
Parameter
,
unique_name
,
name_scope
import
math
__all__
=
[
'exponential_decay'
,
'natural_exp_decay'
,
'inverse_time_decay'
,
'polynomial_decay'
,
'piecewise_decay'
,
'noam_decay'
,
'append_LARS'
'polynomial_decay'
,
'piecewise_decay'
,
'noam_decay'
,
'append_LARS'
,
'cosine_decay'
]
...
...
@@ -307,6 +309,39 @@ def piecewise_decay(boundaries, values):
return
lr
def
cosine_decay
(
learning_rate
,
step_each_epoch
,
epochs
):
"""
Applies cosine decay to the learning rate.
when training a model, it is oftem recommended to lower the learning rate as the
training progresses. By using this function, the learning rate will be decayed by
following cosine decay strategy.
Args:
learning_rate(Variable|float): The initial learning rate.
step_each_epoch(int): the number of steps in an epoch.
epochs(int): the number of epochs.
Returns:
Variable: The decayed learning rate.
Examples:
..code-block:: python
base_lr = 0.1
lr = fluid.layers.cosine_decay(
learning_rate = base_lr, step_each_epoch=10000, epochs=120)
"""
with
default_main_program
().
_lr_schedule_guard
():
global_step
=
_decay_step_counter
()
cur_epoch
=
ops
.
floor
(
global_step
/
step_each_epoch
)
decayed_lr
=
learning_rate
*
0.5
*
(
ops
.
cos
(
cur_epoch
*
math
.
pi
/
epochs
)
+
1
)
return
decayed_lr
def
append_LARS
(
params_grads
,
learning_rate
,
weight_decay
):
"""
Applies LARS (LAYER-WISE ADAPTIVE RATE SCALING) to learning rate for
...
...
python/paddle/fluid/tests/unittests/test_learning_rate_scheduler.py
浏览文件 @
13e89151
...
...
@@ -82,6 +82,13 @@ def piecewise_decay(global_step, boundaries, values):
return
values
[
len
(
values
)
-
1
]
def
cosine_decay
(
global_step
,
learning_rate
,
step_each_epoch
,
epochs
):
cur_epoch
=
math
.
floor
(
global_step
/
step_each_epoch
)
decayed_lr
=
learning_rate
*
0.5
*
(
math
.
cos
(
cur_epoch
*
math
.
pi
/
epochs
)
+
1
)
return
decayed_lr
class
TestLearningRateDecay
(
unittest
.
TestCase
):
def
check_decay
(
self
,
python_decay_fn
,
fluid_decay_fn
,
kwargs
):
places
=
[
fluid
.
CPUPlace
()]
...
...
@@ -149,6 +156,11 @@ class TestLearningRateDecay(unittest.TestCase):
"boundaries"
:
[
3
,
6
,
9
],
"values"
:
[
0.1
,
0.2
,
0.3
,
0.4
]
}),
(
cosine_decay
,
layers
.
cosine_decay
,
{
"learning_rate"
:
0.1
,
"step_each_epoch"
:
100
,
"epochs"
:
120
}),
]
for
py_decay_fn
,
fluid_decay_fn
,
kwargs
in
decay_fns
:
...
...
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