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体验新版 GitCode,发现更多精彩内容 >>
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844d0620
编写于
2月 15, 2017
作者:
Y
Yu Yang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Remove NAG optimizer
上级
8e0d1d8b
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
10 addition
and
91 deletion
+10
-91
demo/mnist/api_train_v2.py
demo/mnist/api_train_v2.py
+6
-21
python/paddle/v2/trainer.py
python/paddle/v2/trainer.py
+4
-70
未找到文件。
demo/mnist/api_train_v2.py
浏览文件 @
844d0620
from
paddle.trainer_config_helpers
import
*
from
paddle.trainer.PyDataProvider2
import
dense_vector
,
integer_value
import
paddle.v2
as
paddle
_v2
import
paddle.v2
as
paddle
import
numpy
import
mnist_util
...
...
@@ -24,7 +24,7 @@ def network_config():
def
event_handler
(
event
):
if
isinstance
(
event
,
paddle
_v2
.
trainer
.
CompleteTrainOneBatch
):
if
isinstance
(
event
,
paddle
.
trainer
.
CompleteTrainOneBatch
):
print
"Pass %d, Batch %d, Cost %f"
%
(
event
.
pass_id
,
event
.
batch_id
,
event
.
cost
)
else
:
...
...
@@ -32,31 +32,16 @@ def event_handler(event):
def
main
():
paddle
_v2
.
init
(
use_gpu
=
False
,
trainer_count
=
1
)
paddle
.
init
(
use_gpu
=
False
,
trainer_count
=
1
)
model_config
=
parse_network_config
(
network_config
)
pool
=
paddle
_v2
.
parameters
.
create
(
model_config
)
pool
=
paddle
.
parameters
.
create
(
model_config
)
for
param_name
in
pool
.
get_names
():
array
=
pool
.
get_parameter
(
param_name
)
array
[:]
=
numpy
.
random
.
uniform
(
low
=-
1.0
,
high
=
1.0
,
size
=
array
.
shape
)
def
nag
(
v
,
g
,
vel_t_1
):
"""
NAG Optimizer. A optimizer which Paddle CPP is not implemented.
https://arxiv.org/pdf/1212.0901v2.pdf eq.6 eq.7
:param v: parameter value
:param g: parameter gradient
:param vel_t_1: t-1 velocity
:return:
"""
mu
=
0.09
e
=
0.00001
adam_optimizer
=
paddle
.
optimizer
.
Adam
(
learning_rate
=
1e-3
)
vel_t
=
mu
*
vel_t_1
-
e
*
g
v
[:]
=
v
+
(
mu
**
2
)
*
vel_t
-
(
1
+
mu
)
*
e
*
g
vel_t_1
[:]
=
vel_t
trainer
=
paddle_v2
.
trainer
.
SGDTrainer
(
update_equation
=
nag
)
trainer
=
paddle
.
trainer
.
SGDTrainer
(
update_equation
=
adam_optimizer
)
trainer
.
train
(
train_data_reader
=
train_reader
,
topology
=
model_config
,
...
...
python/paddle/v2/trainer.py
浏览文件 @
844d0620
...
...
@@ -2,7 +2,7 @@ import collections
from
paddle.proto.ModelConfig_pb2
import
ModelConfig
from
paddle.proto.ParameterConfig_pb2
import
ParameterConfig
from
.
import
parameters
as
v2_parameters
import
numpy
from
.
import
optimizer
as
v2_optimizer
import
py_paddle.swig_paddle
as
api
from
py_paddle
import
DataProviderConverter
...
...
@@ -93,72 +93,6 @@ class LazyParameterPool(v2_parameters.IParameterPool):
self
.
arrays
=
dict
()
class
CustomizeUpdateEquation
(
object
):
def
__init__
(
self
,
callback
):
self
.
__callback__
=
callback
if
self
.
__callback__
.
func_code
.
co_argcount
<
2
:
raise
ValueError
(
"The update equation at least should contain 2 arguments, "
"first is value, second is gradient"
)
self
.
local_params_count
=
self
.
__callback__
.
func_code
.
co_argcount
-
2
self
.
local_params
=
dict
()
def
enable_types
(
self
):
return
[
api
.
PARAMETER_VALUE
,
api
.
PARAMETER_GRADIENT
]
def
init
(
self
,
gradient_machine
):
assert
isinstance
(
gradient_machine
,
api
.
GradientMachine
)
for
param
in
gradient_machine
.
getParameters
():
conf
=
param
.
getConfig
().
toProto
()
shape
=
map
(
int
,
conf
.
dims
)
self
.
local_params
[
conf
.
name
]
=
[]
for
_
in
xrange
(
self
.
local_params_count
):
self
.
local_params
[
conf
.
name
].
append
(
numpy
.
zeros
(
shape
=
shape
,
dtype
=
'float32'
))
def
create_local_updater
(
self
):
return
self
def
startPass
(
self
):
pass
def
finishPass
(
self
):
pass
def
startBatch
(
self
,
batch_size
):
return
api
.
PASS_TRAIN
def
finishBatch
(
self
,
cost
):
pass
def
update
(
self
,
param
):
conf
=
param
.
getConfig
().
toProto
()
shape
=
map
(
int
,
conf
.
dims
)
if
not
api
.
isUsingGpu
():
v
=
param
.
getBuf
(
api
.
PARAMETER_VALUE
).
toNumpyArrayInplace
().
reshape
(
shape
)
g
=
param
.
getBuf
(
api
.
PARAMETER_GRADIENT
).
toNumpyArrayInplace
(
).
reshape
(
shape
)
else
:
v
=
param
.
getBuf
(
api
.
PARAMETER_VALUE
).
copyToNumpyArray
().
reshape
(
shape
)
g
=
param
.
getBuf
(
api
.
PARAMETER_GRADIENT
).
copyToNumpyArray
().
reshape
(
shape
)
args
=
[
v
,
g
]
for
arg
in
self
.
local_params
[
conf
.
name
]:
args
.
append
(
arg
)
self
.
__callback__
(
*
args
)
if
api
.
isUsingGpu
():
param
.
getBuf
(
api
.
PARAMETER_VALUE
).
copyFromNumpyArray
(
v
.
flatten
(
).
astype
(
'float32'
))
# discard gradient changed.
class
SGDTrainer
(
ITrainer
):
def
__init__
(
self
,
update_equation
):
"""
...
...
@@ -166,9 +100,9 @@ class SGDTrainer(ITrainer):
:param update_equation: Maybe we should give a DSL for update equation?
"""
if
callable
(
update_equation
):
update_equation
=
CustomizeUpdateEquation
(
update_equation
)
if
not
isinstance
(
update_equation
,
v2_optimizer
.
Optimizer
):
raise
ValueError
(
"update equation parameter must be "
"paddle.v2.optimizer.Optimizer"
)
self
.
__optimizer__
=
update_equation
def
train
(
self
,
...
...
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