Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
PaddlePaddle
Paddle
提交
1bb579a3
P
Paddle
项目概览
PaddlePaddle
/
Paddle
大约 1 年 前同步成功
通知
2292
Star
20931
Fork
5422
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
1423
列表
看板
标记
里程碑
合并请求
543
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
1,423
Issue
1,423
列表
看板
标记
里程碑
合并请求
543
合并请求
543
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
提交
1bb579a3
编写于
5月 02, 2018
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
A naive trainer implementation
上级
6a378251
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
185 addition
and
27 deletion
+185
-27
python/paddle/fluid/__init__.py
python/paddle/fluid/__init__.py
+1
-2
python/paddle/fluid/layers/io.py
python/paddle/fluid/layers/io.py
+3
-3
python/paddle/fluid/optimizer.py
python/paddle/fluid/optimizer.py
+2
-1
python/paddle/fluid/tests/book/word2vec/no_test_word2vec_new_api.py
...dle/fluid/tests/book/word2vec/no_test_word2vec_new_api.py
+7
-5
python/paddle/fluid/trainer.py
python/paddle/fluid/trainer.py
+172
-16
未找到文件。
python/paddle/fluid/__init__.py
浏览文件 @
1bb579a3
...
...
@@ -21,8 +21,7 @@ import executor
from
executor
import
*
import
trainer
from
trainer
import
Trainer
from
trainer
import
Event
from
trainer
import
*
import
inferencer
from
inferencer
import
Inferencer
...
...
python/paddle/fluid/layers/io.py
浏览文件 @
1bb579a3
...
...
@@ -50,8 +50,6 @@ def data(name,
dtype(int|float): The type of data : float32, float_16, int etc
type(VarType): The output type. By default it is LOD_TENSOR.
lod_level(int): The LoD Level. 0 means the input data is not a sequence.
main_program(Program): Name of the main program that calls this
startup_program(Program): Name of the startup program
stop_gradient(bool): A boolean that mentions whether gradient should flow.
Returns:
...
...
@@ -74,13 +72,15 @@ def data(name,
if
append_batch_size
:
shape
=
[
-
1
]
+
shape
# append batch size as -1
return
helper
.
create_global_variable
(
data_var
=
helper
.
create_global_variable
(
name
=
name
,
shape
=
shape
,
dtype
=
dtype
,
type
=
type
,
stop_gradient
=
stop_gradient
,
lod_level
=
lod_level
)
data_var
.
is_data
=
True
return
data_var
class
BlockGuardServ
(
BlockGuard
):
...
...
python/paddle/fluid/optimizer.py
浏览文件 @
1bb579a3
...
...
@@ -28,7 +28,8 @@ from contextlib import contextmanager
__all__
=
[
'SGD'
,
'Momentum'
,
'Adagrad'
,
'Adam'
,
'Adamax'
,
'DecayedAdagrad'
,
'SGDOptimizer'
,
'MomentumOptimizer'
,
'AdagradOptimizer'
,
'AdamOptimizer'
,
'AdamaxOptimizer'
,
'DecayedAdagradOptimizer'
,
'Adadelta'
,
'ModelAverage'
'AdamaxOptimizer'
,
'DecayedAdagradOptimizer'
,
'Adadelta'
,
'ModelAverage'
,
'Optimizer'
]
...
...
python/paddle/fluid/tests/book/word2vec/no_test_word2vec_new_api.py
浏览文件 @
1bb579a3
...
...
@@ -79,9 +79,9 @@ def inference_network(is_sparse):
return
predict_word
def
train_network
():
def
train_network
(
is_sparse
):
next_word
=
fluid
.
layers
.
data
(
name
=
'nextw'
,
shape
=
[
1
],
dtype
=
'int64'
)
predict_word
=
inference_network
()
predict_word
=
inference_network
(
is_sparse
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict_word
,
label
=
next_word
)
avg_cost
=
fluid
.
layers
.
mean
(
cost
)
return
avg_cost
...
...
@@ -94,7 +94,8 @@ def train(use_cuda, is_sparse, save_path):
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
def
event_handler
(
event
):
if
isinstance
(
event
,
fluid
.
Event
.
END_EPOCH
):
print
type
(
event
)
if
isinstance
(
event
,
fluid
.
EndEpochEvent
):
avg_cost
=
trainer
.
test
(
reader
=
paddle
.
dataset
.
imikolov
.
test
(
word_dict
,
N
))
...
...
@@ -105,10 +106,11 @@ def train(use_cuda, is_sparse, save_path):
sys
.
exit
(
"got NaN loss, training failed."
)
trainer
=
fluid
.
Trainer
(
partial
(
inference
_network
,
is_sparse
),
partial
(
train
_network
,
is_sparse
),
fluid
.
optimizer
.
SGD
(
learning_rate
=
0.001
),
place
=
place
)
trainer
.
train
(
train_reader
,
100
,
event_handler
)
trainer
.
train
(
reader
=
train_reader
,
num_epochs
=
100
,
event_handler
=
event_handler
)
def
infer
(
use_cuda
,
save_path
):
...
...
python/paddle/fluid/trainer.py
浏览文件 @
1bb579a3
...
...
@@ -12,44 +12,200 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import
core
import
framework
import
executor
import
data_feeder
import
contextlib
# optimizer is same as the parameter of Trainer.__init__. Rename it to opt_module
import
optimizer
as
opt_module
__all__
=
[
'Event'
,
'Trainer'
,
'BeginEpochEvent'
,
'EndEpochEvent'
,
'BeginStepEvent'
,
'EndStepEvent'
,
]
class
Event
(
object
):
BEGIN_EPOCH
=
0
END_EPOCH
=
1
BEGIN_STEP
=
2
END_STEP
=
3
class
BeginEpochEvent
(
object
):
def
__init__
(
self
,
epoch_id
):
self
.
epoch
=
epoch_id
class
EndEpochEvent
(
object
):
def
__init__
(
self
,
epoch_id
):
self
.
epoch
=
epoch_id
def
__init__
(
self
):
self
.
step
=
0
self
.
epoch
=
0
self
.
type
=
Event
.
BEGIN_EPOCH
class
BeginStepEvent
(
object
):
def
__init__
(
self
,
epoch_id
,
step_id
):
self
.
epoch
=
epoch_id
self
.
step
=
step_id
class
EndStepEvent
(
object
):
def
__init__
(
self
,
epoch_id
,
step_id
):
self
.
epoch
=
epoch_id
self
.
step
=
step_id
class
Trainer
(
object
):
"""
Args:
network_func(callable): A function which will return loss. The loss must be a scaler.
optimizer(optimizer.Optimizer): The optimizer should be an instance of Optimizer
params:
place: The device place of this trainer.
"""
def
__init__
(
self
,
network_func
,
optimizer
,
params
=
None
,
place
=
None
):
# 1. we need to generate a framework.Program by calling
# network_func. Reference: fluid.program_guard in
# test_word2vec.py
self
.
scope
=
self
.
_get_scope_from_params
(
params
)
self
.
startup_program
=
framework
.
Program
()
self
.
train_program
=
framework
.
Program
()
with
framework
.
program_guard
(
self
.
train_program
,
self
.
startup_program
):
loss
=
network_func
()
if
not
isinstance
(
optimizer
,
opt_module
.
Optimizer
):
raise
TypeError
(
"The optimizer should be an instance of Optimizer"
)
optimizer
.
minimize
(
loss
)
self
.
place
=
Trainer
.
_check_and_get_place
(
place
)
# 2. move the default_main_program to self.program and run the
# default_startup program on an empty core.Scope()
# Run startup program
if
params
is
None
:
exe
=
executor
.
Executor
(
place
)
exe
.
run
(
self
.
startup_program
,
scope
=
self
.
scope
)
# 3. call self.params.add_vars with the initialized scope, it
# will add the new vars of the initialized scope into
# self.params.
self
.
network_func
=
network_func
self
.
optimizer
=
optimizer
self
.
params
=
params
self
.
place
=
place
# TODO(yuyang): This depends on parameters implementation.
# TODO(helin): support distributed training
def
train
(
self
,
reader
,
num_epochs
,
event_handler
):
pass
def
train
(
self
,
num_epochs
,
event_handler
,
reader
=
None
,
parallel
=
False
,
feed_order
=
None
):
"""
Train the model.
Args:
num_epochs: The number of epoch. An epoch will process all data in reader
event_handler: The event handler. A function with type (ev:Event)->void
reader:
parallel: True if use multi-CPUs or multi-GPUs
feed_order: Feeding order of reader. None will following the defining
order in program
Returns:
"""
if
parallel
:
raise
NotImplementedError
(
"Parallel Executor version of trainer is not implemented"
)
self
.
_train_by_executor
(
num_epochs
,
event_handler
,
reader
,
feed_order
)
def
test
(
self
,
reader
):
pass
def
_get_scope_from_params
(
self
,
params
):
"""
Get Scope from parameter object.
Args:
params(Parameter|None): The parameter object instance. Could be None.
Returns: New scope if params is None. Or params.scope()
NOTE: This method is WIP. Not fully implemented.
"""
if
params
is
None
:
return
core
.
Scope
()
# new scope when params is None
else
:
raise
NotImplementedError
(
"Not implemented right now."
)
@
staticmethod
def
_check_and_get_place
(
place
):
"""
Check the type of place or get the default place
Args:
place(None|core.CUDAPlace|core.CPUPlace): the place that trainer will be executed on.
Raises:
TypeError if the type mismatched.
Returns:
the original place if it is not None.
if fluid is compiled with CUDA, returns CUDAPlace(0) by default.
Otherwise returns CPUPlace by default.
"""
if
place
is
None
:
if
core
.
is_compiled_with_cuda
():
return
core
.
CUDAPlace
(
0
)
else
:
return
core
.
CPUPlace
()
else
:
if
not
isinstance
(
place
,
core
.
CUDAPlace
)
and
not
isinstance
(
place
,
core
.
CPUPlace
):
raise
TypeError
(
"Place should be either CUDAPlace or CPUPlace"
)
return
place
@
contextlib
.
contextmanager
def
_prog_and_scope_guard
(
self
):
with
framework
.
program_guard
(
main_program
=
self
.
train_program
,
startup_program
=
self
.
startup_program
):
with
executor
.
scope_guard
(
self
.
scope
):
yield
def
_train_by_executor
(
self
,
num_epochs
,
event_handler
,
reader
,
feed_order
):
"""
Train by Executor and single device.
Args:
num_epochs:
event_handler:
reader:
feed_order:
Returns:
"""
with
self
.
_prog_and_scope_guard
():
exe
=
executor
.
Executor
(
self
.
place
)
if
feed_order
is
None
:
feed_var_list
=
[
var
for
var
in
self
.
train_program
.
global_block
(
).
vars
.
itervalues
()
if
hasattr
(
var
,
'is_data'
)
and
var
.
is_data
]
else
:
feed_var_list
=
[
self
.
train_program
.
global_block
().
var
(
var_name
)
for
var_name
in
feed_order
]
feeder
=
data_feeder
.
DataFeeder
(
feed_list
=
feed_var_list
,
place
=
self
.
place
)
for
epoch_id
in
range
(
num_epochs
):
event_handler
(
BeginEpochEvent
(
epoch_id
))
for
step_id
,
data
in
enumerate
(
reader
()):
event_handler
(
BeginStepEvent
(
epoch_id
,
step_id
))
exe
.
run
(
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[])
event_handler
(
EndStepEvent
(
epoch_id
,
step_id
))
event_handler
(
EndEpochEvent
(
epoch_id
))
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录