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
52dc6a9c
P
Paddle
项目概览
PaddlePaddle
/
Paddle
大约 1 年 前同步成功
通知
2298
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
52dc6a9c
编写于
3月 06, 2017
作者:
Y
Yu Yang
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'feature/better_infer_interface' into feature/recommendation_v2_api
上级
c7d259e1
d5365bb7
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
98 addition
and
21 deletion
+98
-21
demo/mnist/api_train_v2.py
demo/mnist/api_train_v2.py
+10
-15
doc/api/v2/run_logic.rst
doc/api/v2/run_logic.rst
+8
-0
python/paddle/v2/data_feeder.py
python/paddle/v2/data_feeder.py
+3
-0
python/paddle/v2/inference.py
python/paddle/v2/inference.py
+77
-6
未找到文件。
demo/mnist/api_train_v2.py
浏览文件 @
52dc6a9c
...
...
@@ -92,12 +92,8 @@ def main():
def
event_handler
(
event
):
if
isinstance
(
event
,
paddle
.
event
.
EndIteration
):
if
event
.
batch_id
%
1000
==
0
:
result
=
trainer
.
test
(
reader
=
paddle
.
batch
(
paddle
.
dataset
.
mnist
.
test
(),
batch_size
=
256
))
print
"Pass %d, Batch %d, Cost %f, %s, Testing metrics %s"
%
(
event
.
pass_id
,
event
.
batch_id
,
event
.
cost
,
event
.
metrics
,
result
.
metrics
)
print
"Pass %d, Batch %d, Cost %f, %s"
%
(
event
.
pass_id
,
event
.
batch_id
,
event
.
cost
,
event
.
metrics
)
with
gzip
.
open
(
'params.tar.gz'
,
'w'
)
as
f
:
parameters
.
to_tar
(
f
)
...
...
@@ -123,17 +119,16 @@ def main():
print
'Best pass is %s, testing Avgcost is %s'
%
(
best
[
0
],
best
[
1
])
print
'The classification accuracy is %.2f%%'
%
(
100
-
float
(
best
[
2
])
*
100
)
test_creator
=
paddle
.
dataset
.
mnist
.
test
()
test_data
=
[]
for
item
in
test_creator
():
test_data
.
append
(
item
[
0
])
if
len
(
test_data
)
==
100
:
break
# output is a softmax layer. It returns probabilities.
# Shape should be (100, 10)
probs
=
paddle
.
infer
(
output
=
predict
,
parameters
=
parameters
,
reader
=
paddle
.
batch
(
paddle
.
reader
.
firstn
(
paddle
.
reader
.
map_readers
(
lambda
item
:
(
item
[
0
],
),
paddle
.
dataset
.
mnist
.
test
()),
n
=
100
),
batch_size
=
32
))
probs
=
paddle
.
infer
(
output
=
predict
,
parameters
=
parameters
,
input
=
test_data
)
print
probs
.
shape
...
...
doc/api/v2/run_logic.rst
浏览文件 @
52dc6a9c
...
...
@@ -2,6 +2,7 @@
Trainer API
###########
==========
Parameters
==========
...
...
@@ -24,3 +25,10 @@ Event
.. automodule:: paddle.v2.event
:members:
=========
Inference
=========
.. autofunction:: paddle.v2.infer
\ No newline at end of file
python/paddle/v2/data_feeder.py
浏览文件 @
52dc6a9c
...
...
@@ -85,6 +85,9 @@ class DataFeeder(DataProviderConverter):
input_types
.
append
(
each
[
1
])
DataProviderConverter
.
__init__
(
self
,
input_types
)
def
__len__
(
self
):
return
len
(
self
.
input_names
)
def
convert
(
self
,
dat
,
argument
=
None
):
"""
:param dat: A list of mini-batch data. Each sample is a list or tuple
...
...
python/paddle/v2/inference.py
浏览文件 @
52dc6a9c
import
numpy
import
py_paddle.swig_paddle
as
api
import
collections
import
topology
import
minibatch
from
data_feeder
import
DataFeeder
import
itertools
import
numpy
__all__
=
[
'infer'
]
...
...
@@ -21,8 +21,33 @@ class Inference(object):
self
.
__gradient_machine__
=
gm
self
.
__data_types__
=
topo
.
data_type
()
def
iter_infer
(
self
,
reader
,
feeding
=
None
):
def
iter_infer
(
self
,
input
=
None
,
batch_size
=
None
,
reader
=
None
,
feeding
=
None
):
feeder
=
DataFeeder
(
self
.
__data_types__
,
feeding
)
if
reader
is
None
:
assert
input
is
not
None
and
isinstance
(
input
,
collections
.
Iterable
)
if
not
isinstance
(
input
,
collections
.
Iterable
):
raise
TypeError
(
"When reader is None, input should be whole "
"inference data and should be iterable"
)
if
batch_size
is
None
:
if
not
hasattr
(
input
,
'__len__'
):
raise
ValueError
(
"Should set batch size when input data "
"don't contain length."
)
batch_size
=
len
(
input
)
def
__reader_impl__
():
for
each_sample
in
input
:
if
len
(
feeder
)
==
1
:
yield
[
each_sample
]
else
:
yield
each_sample
reader
=
minibatch
.
batch
(
__reader_impl__
,
batch_size
=
batch_size
)
else
:
if
input
is
not
None
:
raise
ValueError
(
"User should set either input or reader, "
"should not set them both."
)
self
.
__gradient_machine__
.
start
()
for
data_batch
in
reader
():
yield
self
.
__gradient_machine__
.
forwardTest
(
feeder
(
data_batch
))
...
...
@@ -46,6 +71,52 @@ class Inference(object):
return
retv
def
infer
(
output
,
parameters
,
reader
,
feeding
=
None
,
field
=
'value'
):
def
infer
(
output
,
parameters
,
input
=
None
,
batch_size
=
None
,
reader
=
None
,
feeding
=
None
,
field
=
'value'
):
"""
Infer a neural network by given neural network output and parameters. The
user should pass either a batch of input data or reader method.
Example usages:
.. code-block:: python
result = paddle.infer(prediction, parameters, input=SomeData,
batch_size=32)
print result
:param output: output of the neural network that would be inferred
:type output: paddle.v2.config_base.Layer
:param parameters: parameters of the neural network.
:type parameters: paddle.v2.parameters.Parameters
:param input: input data batch. Should be a python iterable object, and each
element is the data batch.
:type input: collections.Iterable
:param batch_size: the batch size when perform inference. Default is the
length of input.
:type batch_size: int
:param reader: input data reader creator in batch. If this field is set, the
`input` and `batch_size` will be ignored.
:type reader: callable
:param feeding: Reader dictionary. Default could generate from input
value.
:param field: The prediction field. It should in [`value`, `ids`]. `value`
means return the prediction probabilities, `ids` means return
the prediction labels. Default is `value`
:type field: str
:return: a numpy array
:rtype: numpy.ndarray
"""
inferer
=
Inference
(
output
=
output
,
parameters
=
parameters
)
return
inferer
.
infer
(
field
=
field
,
reader
=
reader
,
feeding
=
feeding
)
return
inferer
.
infer
(
field
=
field
,
input
=
input
,
batch_size
=
batch_size
,
reader
=
reader
,
feeding
=
feeding
)
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