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e7c23989
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e7c23989
编写于
3月 02, 2017
作者:
D
dangqingqing
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061e743c
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15 changed file
with
682 addition
and
35 deletion
+682
-35
demo/image_classification/api_v2_resnet.py
demo/image_classification/api_v2_resnet.py
+74
-0
demo/image_classification/api_v2_train.py
demo/image_classification/api_v2_train.py
+91
-0
demo/image_classification/api_v2_vgg.py
demo/image_classification/api_v2_vgg.py
+47
-0
demo/semantic_role_labeling/api_train_v2.py
demo/semantic_role_labeling/api_train_v2.py
+1
-1
paddle/api/GradientMachine.cpp
paddle/api/GradientMachine.cpp
+14
-0
paddle/api/PaddleAPI.h
paddle/api/PaddleAPI.h
+3
-0
paddle/py_paddle/util.py
paddle/py_paddle/util.py
+6
-0
python/paddle/v2/__init__.py
python/paddle/v2/__init__.py
+1
-0
python/paddle/v2/config_base.py
python/paddle/v2/config_base.py
+28
-5
python/paddle/v2/dataset/__init__.py
python/paddle/v2/dataset/__init__.py
+6
-1
python/paddle/v2/dataset/conll05.py
python/paddle/v2/dataset/conll05.py
+7
-16
python/paddle/v2/layer.py
python/paddle/v2/layer.py
+240
-8
python/paddle/v2/tests/CMakeLists.txt
python/paddle/v2/tests/CMakeLists.txt
+7
-3
python/paddle/v2/tests/test_rnn_layer.py
python/paddle/v2/tests/test_rnn_layer.py
+155
-0
python/paddle/v2/trainer.py
python/paddle/v2/trainer.py
+2
-1
未找到文件。
demo/image_classification/api_v2_resnet.py
0 → 100644
浏览文件 @
e7c23989
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
paddle.v2
as
paddle
__all__
=
[
'resnet_cifar10'
]
def
conv_bn_layer
(
input
,
ch_out
,
filter_size
,
stride
,
padding
,
active_type
=
paddle
.
activation
.
Relu
(),
ch_in
=
None
):
tmp
=
paddle
.
layer
.
img_conv
(
input
=
input
,
filter_size
=
filter_size
,
num_channels
=
ch_in
,
num_filters
=
ch_out
,
stride
=
stride
,
padding
=
padding
,
act
=
paddle
.
activation
.
Linear
(),
bias_attr
=
False
)
return
paddle
.
layer
.
batch_norm
(
input
=
tmp
,
act
=
active_type
)
def
shortcut
(
ipt
,
n_in
,
n_out
,
stride
):
if
n_in
!=
n_out
:
return
conv_bn_layer
(
ipt
,
n_out
,
1
,
stride
,
0
,
paddle
.
activation
.
Linear
())
else
:
return
ipt
def
basicblock
(
ipt
,
ch_out
,
stride
):
ch_in
=
ch_out
*
2
tmp
=
conv_bn_layer
(
ipt
,
ch_out
,
3
,
stride
,
1
)
tmp
=
conv_bn_layer
(
tmp
,
ch_out
,
3
,
1
,
1
,
paddle
.
activation
.
Linear
())
short
=
shortcut
(
ipt
,
ch_in
,
ch_out
,
stride
)
return
paddle
.
layer
.
addto
(
input
=
[
tmp
,
short
],
act
=
paddle
.
activation
.
Relu
())
def
layer_warp
(
block_func
,
ipt
,
features
,
count
,
stride
):
tmp
=
block_func
(
ipt
,
features
,
stride
)
for
i
in
range
(
1
,
count
):
tmp
=
block_func
(
tmp
,
features
,
1
)
return
tmp
def
resnet_cifar10
(
ipt
,
depth
=
32
):
# depth should be one of 20, 32, 44, 56, 110, 1202
assert
(
depth
-
2
)
%
6
==
0
n
=
(
depth
-
2
)
/
6
nStages
=
{
16
,
64
,
128
}
conv1
=
conv_bn_layer
(
ipt
,
ch_in
=
3
,
ch_out
=
16
,
filter_size
=
3
,
stride
=
1
,
padding
=
1
)
res1
=
layer_warp
(
basicblock
,
conv1
,
16
,
n
,
1
)
res2
=
layer_warp
(
basicblock
,
res1
,
32
,
n
,
2
)
res3
=
layer_warp
(
basicblock
,
res2
,
64
,
n
,
2
)
pool
=
paddle
.
layer
.
img_pool
(
input
=
res3
,
pool_size
=
8
,
stride
=
1
,
pool_type
=
paddle
.
pooling
.
Avg
())
return
pool
demo/image_classification/api_v2_train.py
0 → 100644
浏览文件 @
e7c23989
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License
import
sys
import
paddle.v2
as
paddle
from
api_v2_vgg
import
vgg_bn_drop
from
api_v2_resnet
import
resnet_cifar10
def
main
():
datadim
=
3
*
32
*
32
classdim
=
10
# PaddlePaddle init
paddle
.
init
(
use_gpu
=
True
,
trainer_count
=
1
)
image
=
paddle
.
layer
.
data
(
name
=
"image"
,
type
=
paddle
.
data_type
.
dense_vector
(
datadim
))
# Add neural network config
# option 1. resnet
net
=
resnet_cifar10
(
image
,
depth
=
32
)
# option 2. vgg
# net = vgg_bn_drop(image)
out
=
paddle
.
layer
.
fc
(
input
=
net
,
size
=
classdim
,
act
=
paddle
.
activation
.
Softmax
())
lbl
=
paddle
.
layer
.
data
(
name
=
"label"
,
type
=
paddle
.
data_type
.
integer_value
(
classdim
))
cost
=
paddle
.
layer
.
classification_cost
(
input
=
out
,
label
=
lbl
)
# Create parameters
parameters
=
paddle
.
parameters
.
create
(
cost
)
# Create optimizer
momentum_optimizer
=
paddle
.
optimizer
.
Momentum
(
momentum
=
0.9
,
regularization
=
paddle
.
optimizer
.
L2Regularization
(
rate
=
0.0002
*
128
),
learning_rate
=
0.1
/
128.0
,
learning_rate_decay_a
=
0.1
,
learning_rate_decay_b
=
50000
*
100
,
learning_rate_schedule
=
'discexp'
,
batch_size
=
128
)
# End batch and end pass event handler
def
event_handler
(
event
):
if
isinstance
(
event
,
paddle
.
event
.
EndIteration
):
if
event
.
batch_id
%
100
==
0
:
print
"
\n
Pass %d, Batch %d, Cost %f, %s"
%
(
event
.
pass_id
,
event
.
batch_id
,
event
.
cost
,
event
.
metrics
)
else
:
sys
.
stdout
.
write
(
'.'
)
sys
.
stdout
.
flush
()
if
isinstance
(
event
,
paddle
.
event
.
EndPass
):
result
=
trainer
.
test
(
reader
=
paddle
.
reader
.
batched
(
paddle
.
dataset
.
cifar
.
test10
(),
batch_size
=
128
),
reader_dict
=
{
'image'
:
0
,
'label'
:
1
})
print
"
\n
Test with Pass %d, %s"
%
(
event
.
pass_id
,
result
.
metrics
)
# Create trainer
trainer
=
paddle
.
trainer
.
SGD
(
cost
=
cost
,
parameters
=
parameters
,
update_equation
=
momentum_optimizer
)
trainer
.
train
(
reader
=
paddle
.
reader
.
batched
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
cifar
.
train10
(),
buf_size
=
50000
),
batch_size
=
128
),
num_passes
=
5
,
event_handler
=
event_handler
,
reader_dict
=
{
'image'
:
0
,
'label'
:
1
})
if
__name__
==
'__main__'
:
main
()
demo/image_classification/api_v2_vgg.py
0 → 100644
浏览文件 @
e7c23989
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
paddle.v2
as
paddle
__all__
=
[
'vgg_bn_drop'
]
def
vgg_bn_drop
(
input
):
def
conv_block
(
ipt
,
num_filter
,
groups
,
dropouts
,
num_channels
=
None
):
return
paddle
.
networks
.
img_conv_group
(
input
=
ipt
,
num_channels
=
num_channels
,
pool_size
=
2
,
pool_stride
=
2
,
conv_num_filter
=
[
num_filter
]
*
groups
,
conv_filter_size
=
3
,
conv_act
=
paddle
.
activation
.
Relu
(),
conv_with_batchnorm
=
True
,
conv_batchnorm_drop_rate
=
dropouts
,
pool_type
=
paddle
.
pooling
.
Max
())
conv1
=
conv_block
(
input
,
64
,
2
,
[
0.3
,
0
],
3
)
conv2
=
conv_block
(
conv1
,
128
,
2
,
[
0.4
,
0
])
conv3
=
conv_block
(
conv2
,
256
,
3
,
[
0.4
,
0.4
,
0
])
conv4
=
conv_block
(
conv3
,
512
,
3
,
[
0.4
,
0.4
,
0
])
conv5
=
conv_block
(
conv4
,
512
,
3
,
[
0.4
,
0.4
,
0
])
drop
=
paddle
.
layer
.
dropout
(
input
=
conv5
,
dropout_rate
=
0.5
)
fc1
=
paddle
.
layer
.
fc
(
input
=
drop
,
size
=
512
,
act
=
paddle
.
activation
.
Linear
())
bn
=
paddle
.
layer
.
batch_norm
(
input
=
fc1
,
act
=
paddle
.
activation
.
Relu
(),
layer_attr
=
paddle
.
attr
.
Extra
(
drop_rate
=
0.5
))
fc2
=
paddle
.
layer
.
fc
(
input
=
bn
,
size
=
512
,
act
=
paddle
.
activation
.
Linear
())
return
fc2
demo/semantic_role_labeling/api_train_v2.py
浏览文件 @
e7c23989
...
...
@@ -165,7 +165,7 @@ def main():
trn_reader
=
paddle
.
reader
.
batched
(
paddle
.
reader
.
shuffle
(
conll05
.
test
,
buf_size
=
8192
),
batch_size
=
10
)
conll05
.
test
()
,
buf_size
=
8192
),
batch_size
=
10
)
trainer
.
train
(
reader
=
trn_reader
,
event_handler
=
event_handler
,
num_passes
=
10000
)
...
...
paddle/api/GradientMachine.cpp
浏览文件 @
e7c23989
...
...
@@ -142,6 +142,20 @@ Parameter* GradientMachine::getParameter(size_t i) throw(RangeError) {
}
}
size_t
GradientMachine
::
getNonStaticParameterSize
()
const
{
return
m
->
machine
->
getNonStaticParameters
().
size
();
}
Parameter
*
GradientMachine
::
getNonStaticParameter
(
size_t
i
)
throw
(
RangeError
)
{
auto
params
=
m
->
machine
->
getNonStaticParameters
();
if
(
i
<
params
.
size
())
{
return
Parameter
::
createFromSharedPtr
(
&
m
->
machine
->
getNonStaticParameters
()[
i
]);
}
else
{
throw
RangeError
();
}
}
void
GradientMachine
::
randParameters
()
{
m
->
machine
->
randParameters
();
}
Arguments
*
GradientMachine
::
getLayerOutput
(
const
std
::
string
&
layerName
)
const
...
...
paddle/api/PaddleAPI.h
浏览文件 @
e7c23989
...
...
@@ -771,6 +771,9 @@ public:
size_t
getParameterSize
()
const
;
Parameter
*
getParameter
(
size_t
i
)
throw
(
RangeError
);
size_t
getNonStaticParameterSize
()
const
;
Parameter
*
getNonStaticParameter
(
size_t
i
)
throw
(
RangeError
);
void
randParameters
();
Arguments
*
getLayerOutput
(
const
std
::
string
&
layerName
)
const
...
...
paddle/py_paddle/util.py
浏览文件 @
e7c23989
...
...
@@ -195,6 +195,12 @@ def __monkeypatch_gradient_machine__():
swig_paddle
.
GradientMachine
.
getParameters
=
getParameters
def
getNonStaticParameters
(
self
):
return
(
self
.
getNonStaticParameter
(
i
)
for
i
in
xrange
(
self
.
getNonStaticParameterSize
()))
swig_paddle
.
GradientMachine
.
getNonStaticParameters
=
getNonStaticParameters
def
getLayerOutputs
(
self
,
layerNames
):
"""
getLayerOutputs. get outputs of layers and return a numpy matrix dict.
...
...
python/paddle/v2/__init__.py
浏览文件 @
e7c23989
...
...
@@ -20,6 +20,7 @@ import event
import
data_type
import
topology
import
data_feeder
import
networks
from
.
import
dataset
from
.
import
reader
import
attr
...
...
python/paddle/v2/config_base.py
浏览文件 @
e7c23989
...
...
@@ -22,6 +22,7 @@ class Layer(object):
def
__init__
(
self
,
name
=
None
,
parent_layers
=
None
):
assert
isinstance
(
parent_layers
,
dict
)
self
.
name
=
name
self
.
__contex__
=
{}
self
.
__parent_layers__
=
parent_layers
def
to_proto
(
self
,
context
):
...
...
@@ -39,16 +40,38 @@ class Layer(object):
self
.
__parent_layers__
[
layer_name
])
kwargs
[
layer_name
]
=
v1_layer
if
self
.
name
is
None
:
if
self
.
context_name
()
is
None
:
return
self
.
to_proto_impl
(
**
kwargs
)
elif
self
.
name
not
in
context
:
context
[
self
.
name
]
=
self
.
to_proto_impl
(
**
kwargs
)
return
context
[
self
.
name
]
elif
self
.
context_name
()
not
in
context
:
context
[
self
.
context_name
()]
=
self
.
to_proto_impl
(
**
kwargs
)
self
.
__contex__
=
context
if
self
.
use_context_name
():
return
context
[
self
.
context_name
()]
else
:
return
context
[
self
.
name
]
def
to_proto_impl
(
self
,
**
kwargs
):
raise
NotImplementedError
()
def
context_name
(
self
):
"""
Context name means the context which stores `to_proto_impl` result.
If multiple layer share same context_name, the `to_proto_impl` of them
will be invoked only once.
"""
return
self
.
name
def
use_context_name
(
self
):
return
False
def
calculate_size
(
self
):
"""
lazy calculate size of the layer, should be called when to_proto_impl of
this layer is called.
:return:
"""
return
self
.
__contex__
[
self
.
context_name
()].
size
def
__convert_to_v2__
(
method_name
,
parent_names
,
is_default_name
=
True
):
if
is_default_name
:
...
...
python/paddle/v2/dataset/__init__.py
浏览文件 @
e7c23989
...
...
@@ -13,5 +13,10 @@
# limitations under the License.
import
mnist
import
imikolov
import
imdb
import
cifar
import
movielens
import
conll05
__all__
=
[
'mnist'
,
'
cifar'
,
'imdb'
,
'conll05'
,
'imikolov'
,
'movielens
'
]
__all__
=
[
'mnist'
,
'
imikolov'
,
'imdb'
,
'cifar'
,
'movielens'
,
'conll05
'
]
python/paddle/v2/dataset/conll05.py
浏览文件 @
e7c23989
...
...
@@ -12,10 +12,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import
paddle.v2.dataset.common
import
tarfile
import
gzip
import
itertools
from
common
import
download
__all__
=
[
'test, get_dict'
,
'get_embedding'
]
"""
...
...
@@ -173,33 +173,24 @@ def reader_creator(corpus_reader,
yield
word_idx
,
ctx_n2_idx
,
ctx_n1_idx
,
\
ctx_0_idx
,
ctx_p1_idx
,
ctx_p2_idx
,
pred_idx
,
mark
,
label_idx
return
reader
()
return
reader
def
get_dict
():
word_dict
=
load_dict
(
common
.
download
(
WORDDICT_URL
,
'conll05st'
,
WORDDICT_MD5
))
verb_dict
=
load_dict
(
common
.
download
(
VERBDICT_URL
,
'conll05st'
,
VERBDICT_MD5
))
label_dict
=
load_dict
(
common
.
download
(
TRGDICT_URL
,
'conll05st'
,
TRGDICT_MD5
))
word_dict
=
load_dict
(
download
(
WORDDICT_URL
,
'conll05st'
,
WORDDICT_MD5
))
verb_dict
=
load_dict
(
download
(
VERBDICT_URL
,
'conll05st'
,
VERBDICT_MD5
))
label_dict
=
load_dict
(
download
(
TRGDICT_URL
,
'conll05st'
,
TRGDICT_MD5
))
return
word_dict
,
verb_dict
,
label_dict
def
get_embedding
():
return
common
.
download
(
EMB_URL
,
'conll05st'
,
EMB_MD5
)
return
download
(
EMB_URL
,
'conll05st'
,
EMB_MD5
)
def
test
():
word_dict
,
verb_dict
,
label_dict
=
get_dict
()
reader
=
corpus_reader
(
common
.
download
(
DATA_URL
,
'conll05st'
,
DATA_MD5
),
download
(
DATA_URL
,
'conll05st'
,
DATA_MD5
),
words_name
=
'conll05st-release/test.wsj/words/test.wsj.words.gz'
,
props_name
=
'conll05st-release/test.wsj/props/test.wsj.props.gz'
)
return
reader_creator
(
reader
,
word_dict
,
verb_dict
,
label_dict
)
if
__name__
==
'__main__'
:
print
get_embedding
()
for
f
in
test
():
print
f
python/paddle/v2/layer.py
浏览文件 @
e7c23989
...
...
@@ -65,19 +65,24 @@ to be in a Python function but could be anywhere.
Also, the creation of a protobuf message is hidden in the invocation of
paddle.v2.parameters.create, no longer exposed to users.
"""
import
collections
import
inspect
from
config_base
import
Layer
,
__convert_to_v2__
import
paddle.trainer_config_helpers
as
conf_helps
from
paddle.trainer_config_helpers.config_parser_utils
import
\
parse_network_config
as
__parse__
from
paddle.trainer_config_helpers.default_decorators
import
wrap_name_default
from
paddle.trainer_config_helpers.default_decorators
import
wrap_act_default
from
paddle.trainer_config_helpers.default_decorators
import
\
wrap_bias_attr_default
from
paddle.trainer_config_helpers.default_decorators
import
wrap_name_default
from
paddle.trainer_config_helpers.layers
import
layer_support
from
paddle.trainer.config_parser
import
\
RecurrentLayerGroupWithoutOutLinksBegin
,
RecurrentLayerGroupSetOutLink
,
\
RecurrentLayerGroupEnd
,
model_type
import
data_type
import
activation
import
data_type
__all__
=
[
'parse_network'
,
'data'
]
...
...
@@ -130,6 +135,137 @@ class DataLayerV2(Layer):
return
getattr
(
conf_helps
,
self
.
__method_name__
)(
name
=
self
.
name
,
**
args
)
class
WithExtraParent
(
Layer
):
def
extra_parent
(
self
):
return
self
.
__extra_parent__
def
__init__
(
self
,
name
=
None
,
parent_layers
=
None
):
self
.
__extra_parent__
=
[]
super
(
WithExtraParent
,
self
).
__init__
(
name
=
name
,
parent_layers
=
parent_layers
)
def
append_extra_parent
(
self
,
parent
):
self
.
__extra_parent__
.
append
(
parent
)
def
to_proto
(
self
,
context
):
"""
function to set proto attribute
"""
kwargs
=
dict
()
for
p
in
self
.
__extra_parent__
:
p
.
to_proto
(
context
=
context
)
for
layer_name
in
self
.
__parent_layers__
:
if
not
isinstance
(
self
.
__parent_layers__
[
layer_name
],
collections
.
Sequence
):
v1_layer
=
self
.
__parent_layers__
[
layer_name
].
to_proto
(
context
=
context
)
else
:
v1_layer
=
map
(
lambda
x
:
x
.
to_proto
(
context
=
context
),
self
.
__parent_layers__
[
layer_name
])
kwargs
[
layer_name
]
=
v1_layer
if
self
.
context_name
()
is
None
:
return
self
.
to_proto_impl
(
context
=
context
,
**
kwargs
)
elif
self
.
context_name
()
not
in
context
:
context
[
self
.
context_name
()]
=
self
.
to_proto_impl
(
context
=
context
,
**
kwargs
)
if
self
.
use_context_name
():
return
context
[
self
.
context_name
()]
else
:
return
context
[
self
.
name
]
class
MemoryV2
(
WithExtraParent
):
def
__init__
(
self
,
name
,
**
kwargs
):
self
.
name
=
name
super
(
MemoryV2
,
self
).
__init__
(
name
=
name
,
parent_layers
=
dict
())
self
.
__kwargs__
=
kwargs
self
.
__boot_layer_name__
=
None
if
'boot_layer'
in
kwargs
:
begin_of_current_rnn
=
[]
# TODO(yuyang18): Fix inspect, it could be wrong when user invoke a
# function inside step.
st
=
inspect
.
stack
()
for
i
in
xrange
(
len
(
st
)):
locs
=
inspect
.
stack
()[
i
][
0
].
f_locals
keys
=
locs
.
keys
()
for
key
in
keys
:
val
=
locs
[
key
]
if
isinstance
(
val
,
RecurrentLayerInput
):
begin_of_current_rnn
.
append
(
val
)
elif
isinstance
(
val
,
collections
.
Sequence
):
for
v
in
val
:
if
isinstance
(
v
,
RecurrentLayerInput
):
begin_of_current_rnn
.
append
(
v
)
if
begin_of_current_rnn
:
break
assert
begin_of_current_rnn
is
not
None
for
extra
in
begin_of_current_rnn
:
self
.
append_extra_parent
(
extra
)
assert
isinstance
(
extra
,
WithExtraParent
)
extra
.
append_extra_parent
(
kwargs
[
'boot_layer'
])
self
.
__boot_layer_name__
=
kwargs
[
'boot_layer'
].
name
def
to_proto_impl
(
self
,
context
,
**
kwargs
):
args
=
dict
()
for
each
in
kwargs
:
args
[
each
]
=
kwargs
[
each
]
for
each
in
self
.
__kwargs__
:
args
[
each
]
=
self
.
__kwargs__
[
each
]
if
self
.
__boot_layer_name__
is
not
None
:
args
[
'boot_layer'
]
=
context
[
self
.
__boot_layer_name__
]
size
=
args
.
get
(
'size'
,
None
)
if
size
is
not
None
:
if
callable
(
size
):
real_size
=
size
()
else
:
real_size
=
size
args
[
'size'
]
=
real_size
return
conf_helps
.
memory
(
name
=
self
.
name
,
**
args
)
def
context_name
(
self
):
return
self
.
name
+
"#memory"
def
use_context_name
(
self
):
"""
memory layer will have the same name with some layer
:return:
"""
return
True
class
LayerOutputV2
(
Layer
):
"""
LayerOutputV2 is used to store the result of LayerOutput in v1 api.
It will not store it's parents because layer_output has been parsed already.
"""
def
__init__
(
self
,
layer_output
):
assert
isinstance
(
layer_output
,
conf_helps
.
LayerOutput
)
self
.
layer_output
=
layer_output
super
(
LayerOutputV2
,
self
).
__init__
(
name
=
layer_output
.
name
,
parent_layers
=
dict
())
def
to_proto_impl
(
self
):
return
self
.
layer_output
class
StaticInputV2
(
object
):
def
__init__
(
self
,
input
,
is_seq
=
False
,
size
=
None
):
assert
isinstance
(
input
,
LayerV2
)
self
.
name
=
input
.
name
self
.
input
=
input
self
.
is_seq
=
is_seq
self
.
size
=
size
# TODO(qiaolongfei): add size
# assert input.size is not None or size is not None
class
MixedLayerV2
(
Layer
):
"""
This class is use to support `with` grammar. If not, the following code
...
...
@@ -161,7 +297,6 @@ class MixedLayerV2(Layer):
other_kwargs
[
'act'
]
=
act
other_kwargs
[
'bias_attr'
]
=
bias_attr
other_kwargs
[
'layer_attr'
]
=
layer_attr
parent_layers
=
{
"input"
:
self
.
__inputs__
}
super
(
MixedLayerV2
,
self
).
__init__
(
name
,
parent_layers
)
self
.
__other_kwargs__
=
other_kwargs
...
...
@@ -171,7 +306,7 @@ class MixedLayerV2(Layer):
self
.
__inputs__
.
append
(
other
)
return
self
else
:
raise
MixedLayer
Type
V2
.
AddToSealedMixedLayerExceptionV2
()
raise
MixedLayerV2
.
AddToSealedMixedLayerExceptionV2
()
def
__enter__
(
self
):
assert
len
(
self
.
__inputs__
)
==
0
...
...
@@ -186,6 +321,13 @@ class MixedLayerV2(Layer):
args
[
each
]
=
kwargs
[
each
]
for
each
in
self
.
__other_kwargs__
:
args
[
each
]
=
self
.
__other_kwargs__
[
each
]
size
=
args
.
get
(
'size'
,
None
)
if
size
is
not
None
:
if
callable
(
size
):
real_size
=
size
()
else
:
real_size
=
size
args
[
'size'
]
=
real_size
return
getattr
(
conf_helps
,
self
.
__method_name__
)(
**
args
)
...
...
@@ -202,14 +344,51 @@ def mixed(size=0,
return
MixedLayerV2
(
size
,
input
,
name
,
act
,
bias_attr
,
layer_attr
)
class
RecurrentLayerInput
(
WithExtraParent
):
def
__init__
(
self
,
recurrent_name
,
index
,
parent_layers
):
assert
len
(
parent_layers
)
==
1
self
.
__parents__
=
parent_layers
.
values
()[
0
]
super
(
RecurrentLayerInput
,
self
).
__init__
(
name
=
self
.
__parents__
[
index
].
name
,
parent_layers
=
parent_layers
)
self
.
__recurrent_name__
=
recurrent_name
def
context_name
(
self
):
return
self
.
__recurrent_name__
+
".begin"
def
to_proto_impl
(
self
,
context
,
**
kwargs
):
model_type
(
'recurrent_nn'
)
RecurrentLayerGroupWithoutOutLinksBegin
(
name
=
self
.
__recurrent_name__
,
in_links
=
map
(
lambda
x
:
x
.
name
,
self
.
__parents__
))
return
self
class
RecurrentLayerOutput
(
Layer
):
def
__init__
(
self
,
recurrent_name
,
index
,
parent_layers
):
assert
len
(
parent_layers
)
==
1
self
.
__parents__
=
parent_layers
.
values
()[
0
]
super
(
RecurrentLayerOutput
,
self
).
__init__
(
name
=
self
.
__parents__
[
index
].
name
,
parent_layers
=
parent_layers
)
self
.
__recurrent_name__
=
recurrent_name
def
context_name
(
self
):
return
self
.
__recurrent_name__
+
".end"
def
to_proto_impl
(
self
,
**
kwargs
):
for
l
in
self
.
__parents__
:
RecurrentLayerGroupSetOutLink
(
l
.
name
)
RecurrentLayerGroupEnd
(
name
=
self
.
__recurrent_name__
)
LayerV2
=
Layer
data
=
DataLayerV2
AggregateLevel
=
conf_helps
.
layers
.
AggregateLevel
ExpandLevel
=
conf_helps
.
layers
.
ExpandLevel
memory
=
MemoryV2
def
__layer_name_mapping__
(
inname
):
if
inname
in
[
'data_layer'
,
'memory'
,
'mixed_layer'
]:
if
inname
in
[
'data_layer'
,
'memory'
,
'mixed_layer'
,
'recurrent_group'
]:
# Do Not handle these layers
return
elif
inname
==
'maxid_layer'
:
...
...
@@ -231,8 +410,10 @@ def __layer_name_mapping__(inname):
def
__layer_name_mapping_parent_names__
(
inname
):
all_args
=
getattr
(
conf_helps
,
inname
).
argspec
.
args
return
filter
(
lambda
x
:
x
in
[
'input1'
,
'input2'
,
'label'
,
'input'
,
'a'
,
'b'
,
'expand_as'
,
'weights'
,
'vectors'
,
'weight'
,
'score'
,
'left'
,
'right'
],
lambda
x
:
x
in
[
'input1'
,
'input2'
,
'label'
,
'input'
,
'a'
,
'b'
,
'expand_as'
,
'weights'
,
'vectors'
,
'weight'
,
'score'
,
'left'
,
'right'
,
'output_mem'
],
all_args
)
...
...
@@ -267,3 +448,54 @@ operator_list = [
for
op
in
operator_list
:
globals
()[
op
[
0
]]
=
__convert_to_v2__
(
op
[
0
],
parent_names
=
op
[
1
],
is_default_name
=
False
)
@
wrap_name_default
()
def
recurrent_group
(
step
,
input
,
name
=
None
):
if
not
isinstance
(
input
,
collections
.
Sequence
):
input
=
[
input
]
non_static_inputs
=
filter
(
lambda
x
:
not
isinstance
(
x
,
StaticInputV2
),
input
)
actual_input
=
[
RecurrentLayerInput
(
recurrent_name
=
name
,
index
=
i
,
parent_layers
=
{
'recurrent_inputs'
:
non_static_inputs
})
for
i
in
xrange
(
len
(
non_static_inputs
))
]
def
__real_step__
(
*
args
):
rnn_input
=
list
(
args
)
static_inputs
=
filter
(
lambda
x
:
isinstance
(
x
,
StaticInputV2
),
input
)
for
static_input
in
static_inputs
:
mem_name
=
"__%s_memory__"
%
static_input
.
input
.
name
mem
=
memory
(
name
=
mem_name
,
is_seq
=
static_input
.
is_seq
,
size
=
static_input
.
input
.
calculate_size
,
boot_layer
=
static_input
.
input
)
with
mixed
(
name
=
mem_name
,
size
=
static_input
.
input
.
calculate_size
,
act
=
activation
.
Identity
())
as
mix
:
mix
+=
identity_projection
(
input
=
mem
)
rnn_input
.
insert
(
input
.
index
(
static_input
),
mix
)
return
step
(
*
rnn_input
)
actual_output
=
__real_step__
(
*
actual_input
)
if
not
isinstance
(
actual_output
,
collections
.
Sequence
):
actual_output
=
[
actual_output
]
retv
=
[
RecurrentLayerOutput
(
recurrent_name
=
name
,
index
=
i
,
parent_layers
=
{
'recurrent_outputs'
:
actual_output
})
for
i
in
xrange
(
len
(
actual_output
))
]
if
len
(
retv
)
==
1
:
return
retv
[
0
]
else
:
return
retv
python/paddle/v2/tests/CMakeLists.txt
浏览文件 @
e7c23989
add_test
(
NAME test_v2_api
COMMAND bash
${
PROJ_ROOT
}
/python/paddle/v2/tests/run_tests.sh
${
PYTHON_EXECUTABLE
}
)
add_test
(
NAME test_v2_layer
COMMAND
${
PROJ_ROOT
}
/paddle/.set_python_path.sh -d
${
PROJ_ROOT
}
/python/
${
PYTHON_EXECUTABLE
}
${
PROJ_ROOT
}
/python/paddle/v2/tests/test_layer.py
WORKING_DIRECTORY
${
PROJ_ROOT
}
/python/paddle
)
add_test
(
NAME test_v2_api
COMMAND bash
${
PROJ_ROOT
}
/python/paddle/v2/tests/run_tests.sh
${
PYTHON_EXECUTABLE
}
)
add_test
(
NAME test_v2_rnn_layer
COMMAND
${
PROJ_ROOT
}
/paddle/.set_python_path.sh -d
${
PROJ_ROOT
}
/python/
${
PYTHON_EXECUTABLE
}
${
PROJ_ROOT
}
/python/paddle/v2/tests/test_rnn_layer.py
)
add_test
(
NAME t
opology_test
add_test
(
NAME t
est_topology
COMMAND
${
PROJ_ROOT
}
/paddle/.set_python_path.sh -d
${
PROJ_ROOT
}
/python/
${
PYTHON_EXECUTABLE
}
${
PROJ_ROOT
}
/python/paddle/v2/tests/test_topology.py
WORKING_DIRECTORY
${
PROJ_ROOT
}
/python/paddle
)
python/paddle/v2/tests/test_rnn_layer.py
0 → 100644
浏览文件 @
e7c23989
# Copyright PaddlePaddle contributors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
difflib
import
unittest
import
paddle.trainer_config_helpers
as
conf_helps
import
paddle.v2.activation
as
activation
import
paddle.v2.data_type
as
data_type
import
paddle.v2.layer
as
layer
from
paddle.trainer_config_helpers.config_parser_utils
import
\
parse_network_config
as
parse_network
class
RNNTest
(
unittest
.
TestCase
):
def
test_simple_rnn
(
self
):
dict_dim
=
10
word_dim
=
8
hidden_dim
=
8
def
parse_old_rnn
():
def
step
(
y
):
mem
=
conf_helps
.
memory
(
name
=
"rnn_state"
,
size
=
hidden_dim
)
out
=
conf_helps
.
fc_layer
(
input
=
[
y
,
mem
],
size
=
hidden_dim
,
act
=
activation
.
Tanh
(),
bias_attr
=
True
,
name
=
"rnn_state"
)
return
out
def
test
():
data
=
conf_helps
.
data_layer
(
name
=
"word"
,
size
=
dict_dim
)
embd
=
conf_helps
.
embedding_layer
(
input
=
data
,
size
=
word_dim
)
conf_helps
.
recurrent_group
(
name
=
"rnn"
,
step
=
step
,
input
=
embd
)
return
str
(
parse_network
(
test
))
def
parse_new_rnn
():
def
new_step
(
y
):
mem
=
layer
.
memory
(
name
=
"rnn_state"
,
size
=
hidden_dim
)
out
=
layer
.
fc
(
input
=
[
y
,
mem
],
size
=
hidden_dim
,
act
=
activation
.
Tanh
(),
bias_attr
=
True
,
name
=
"rnn_state"
)
return
out
data
=
layer
.
data
(
name
=
"word"
,
type
=
data_type
.
integer_value
(
dict_dim
))
embd
=
layer
.
embedding
(
input
=
data
,
size
=
word_dim
)
rnn_layer
=
layer
.
recurrent_group
(
name
=
"rnn"
,
step
=
new_step
,
input
=
embd
)
return
str
(
layer
.
parse_network
(
rnn_layer
))
diff
=
difflib
.
unified_diff
(
parse_old_rnn
().
splitlines
(
1
),
parse_new_rnn
().
splitlines
(
1
))
print
''
.
join
(
diff
)
def
test_sequence_rnn_multi_input
(
self
):
dict_dim
=
10
word_dim
=
8
hidden_dim
=
8
label_dim
=
3
def
parse_old_rnn
():
def
test
():
data
=
conf_helps
.
data_layer
(
name
=
"word"
,
size
=
dict_dim
)
label
=
conf_helps
.
data_layer
(
name
=
"label"
,
size
=
label_dim
)
emb
=
conf_helps
.
embedding_layer
(
input
=
data
,
size
=
word_dim
)
boot_layer
=
conf_helps
.
data_layer
(
name
=
"boot"
,
size
=
10
)
boot_layer
=
conf_helps
.
fc_layer
(
name
=
'boot_fc'
,
input
=
boot_layer
,
size
=
10
)
def
step
(
y
,
wid
):
z
=
conf_helps
.
embedding_layer
(
input
=
wid
,
size
=
word_dim
)
mem
=
conf_helps
.
memory
(
name
=
"rnn_state"
,
size
=
hidden_dim
,
boot_layer
=
boot_layer
)
out
=
conf_helps
.
fc_layer
(
input
=
[
y
,
z
,
mem
],
size
=
hidden_dim
,
act
=
conf_helps
.
TanhActivation
(),
bias_attr
=
True
,
name
=
"rnn_state"
)
return
out
out
=
conf_helps
.
recurrent_group
(
name
=
"rnn"
,
step
=
step
,
input
=
[
emb
,
data
])
rep
=
conf_helps
.
last_seq
(
input
=
out
)
prob
=
conf_helps
.
fc_layer
(
size
=
label_dim
,
input
=
rep
,
act
=
conf_helps
.
SoftmaxActivation
(),
bias_attr
=
True
)
conf_helps
.
outputs
(
conf_helps
.
classification_cost
(
input
=
prob
,
label
=
label
))
return
str
(
parse_network
(
test
))
def
parse_new_rnn
():
data
=
layer
.
data
(
name
=
"word"
,
type
=
data_type
.
dense_vector
(
dict_dim
))
label
=
layer
.
data
(
name
=
"label"
,
type
=
data_type
.
dense_vector
(
label_dim
))
emb
=
layer
.
embedding
(
input
=
data
,
size
=
word_dim
)
boot_layer
=
layer
.
data
(
name
=
"boot"
,
type
=
data_type
.
dense_vector
(
10
))
boot_layer
=
layer
.
fc
(
name
=
'boot_fc'
,
input
=
boot_layer
,
size
=
10
)
def
step
(
y
,
wid
):
z
=
layer
.
embedding
(
input
=
wid
,
size
=
word_dim
)
mem
=
layer
.
memory
(
name
=
"rnn_state"
,
size
=
hidden_dim
,
boot_layer
=
boot_layer
)
out
=
layer
.
fc
(
input
=
[
y
,
z
,
mem
],
size
=
hidden_dim
,
act
=
activation
.
Tanh
(),
bias_attr
=
True
,
name
=
"rnn_state"
)
return
out
out
=
layer
.
recurrent_group
(
name
=
"rnn"
,
step
=
step
,
input
=
[
emb
,
data
])
rep
=
layer
.
last_seq
(
input
=
out
)
prob
=
layer
.
fc
(
size
=
label_dim
,
input
=
rep
,
act
=
activation
.
Softmax
(),
bias_attr
=
True
)
cost
=
layer
.
classification_cost
(
input
=
prob
,
label
=
label
)
return
str
(
layer
.
parse_network
(
cost
))
diff
=
difflib
.
unified_diff
(
parse_old_rnn
().
splitlines
(
1
),
parse_new_rnn
().
splitlines
(
1
))
print
''
.
join
(
diff
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/v2/trainer.py
浏览文件 @
e7c23989
...
...
@@ -120,7 +120,8 @@ class SGD(ITrainer):
feeder
(
data_batch
),
out_args
,
pass_type
)
self
.
__gradient_machine__
.
eval
(
pass_evaluator
)
self
.
__gradient_machine__
.
eval
(
batch_evaluator
)
for
each_param
in
self
.
__gradient_machine__
.
getParameters
():
for
each_param
in
self
.
__gradient_machine__
.
getNonStaticParameters
(
):
updater
.
update
(
each_param
)
# Get cost. We use numpy to calculate total cost for this batch.
cost_vec
=
out_args
.
getSlotValue
(
0
)
...
...
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