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52dc6a9c
编写于
3月 06, 2017
作者:
Y
Yu Yang
浏览文件
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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
)
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