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c091dbdf
编写于
1月 31, 2018
作者:
Y
Yu Yang
提交者:
GitHub
1月 31, 2018
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差异文件
Merge pull request #7974 from reyoung/feature/unify_understand_sentiment
Merge test_understand_sentiment together
上级
94965806
4fee15e8
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
78 addition
and
298 deletion
+78
-298
python/paddle/v2/fluid/tests/book/test_understand_sentiment.py
...n/paddle/v2/fluid/tests/book/test_understand_sentiment.py
+78
-37
python/paddle/v2/fluid/tests/book/test_understand_sentiment_conv.py
...dle/v2/fluid/tests/book/test_understand_sentiment_conv.py
+0
-101
python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py
...dle/v2/fluid/tests/book/test_understand_sentiment_lstm.py
+0
-160
未找到文件。
python/paddle/v2/fluid/tests/book/test_understand_sentiment
_dynamic_lstm
.py
→
python/paddle/v2/fluid/tests/book/test_understand_sentiment.py
浏览文件 @
c091dbdf
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve
d
.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
...
...
@@ -12,9 +12,36 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import
numpy
as
np
import
paddle.v2
as
paddle
import
unittest
import
paddle.v2.fluid
as
fluid
import
paddle.v2
as
paddle
import
contextlib
def
convolution_net
(
data
,
label
,
input_dim
,
class_dim
=
2
,
emb_dim
=
32
,
hid_dim
=
32
):
emb
=
fluid
.
layers
.
embedding
(
input
=
data
,
size
=
[
input_dim
,
emb_dim
])
conv_3
=
fluid
.
nets
.
sequence_conv_pool
(
input
=
emb
,
num_filters
=
hid_dim
,
filter_size
=
3
,
act
=
"tanh"
,
pool_type
=
"sqrt"
)
conv_4
=
fluid
.
nets
.
sequence_conv_pool
(
input
=
emb
,
num_filters
=
hid_dim
,
filter_size
=
4
,
act
=
"tanh"
,
pool_type
=
"sqrt"
)
prediction
=
fluid
.
layers
.
fc
(
input
=
[
conv_3
,
conv_4
],
size
=
class_dim
,
act
=
"softmax"
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
prediction
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
adam_optimizer
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.002
)
adam_optimizer
.
minimize
(
avg_cost
)
accuracy
=
fluid
.
layers
.
accuracy
(
input
=
prediction
,
label
=
label
)
return
avg_cost
,
accuracy
def
stacked_lstm_net
(
data
,
...
...
@@ -51,63 +78,77 @@ def stacked_lstm_net(data,
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
adam_optimizer
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.002
)
adam_optimizer
.
minimize
(
avg_cost
)
accuracy
=
fluid
.
evaluator
.
Accuracy
(
input
=
prediction
,
label
=
label
)
return
avg_cost
,
accuracy
,
accuracy
.
metrics
[
0
]
def
to_lodtensor
(
data
,
place
):
seq_lens
=
[
len
(
seq
)
for
seq
in
data
]
cur_len
=
0
lod
=
[
cur_len
]
for
l
in
seq_lens
:
cur_len
+=
l
lod
.
append
(
cur_len
)
flattened_data
=
np
.
concatenate
(
data
,
axis
=
0
).
astype
(
"int64"
)
flattened_data
=
flattened_data
.
reshape
([
len
(
flattened_data
),
1
])
res
=
fluid
.
LoDTensor
()
res
.
set
(
flattened_data
,
place
)
res
.
set_lod
([
lod
])
return
res
def
main
():
BATCH_SIZE
=
100
PASS_NUM
=
5
accuracy
=
fluid
.
layers
.
accuracy
(
input
=
prediction
,
label
=
label
)
return
avg_cost
,
accuracy
word_dict
=
paddle
.
dataset
.
imdb
.
word_dict
()
print
"load word dict successfully"
def
main
(
word_dict
,
net_method
,
use_cuda
):
if
use_cuda
and
not
fluid
.
core
.
is_compiled_with_cuda
():
return
BATCH_SIZE
=
128
PASS_NUM
=
5
dict_dim
=
len
(
word_dict
)
class_dim
=
2
data
=
fluid
.
layers
.
data
(
name
=
"words"
,
shape
=
[
1
],
dtype
=
"int64"
,
lod_level
=
1
)
label
=
fluid
.
layers
.
data
(
name
=
"label"
,
shape
=
[
1
],
dtype
=
"int64"
)
cost
,
acc
uracy
,
acc_out
=
stacked_lstm_net
(
cost
,
acc
_out
=
net_method
(
data
,
label
,
input_dim
=
dict_dim
,
class_dim
=
class_dim
)
train_data
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
imdb
.
train
(
word_dict
),
buf_size
=
1000
),
batch_size
=
BATCH_SIZE
)
place
=
fluid
.
CPUPlace
()
place
=
fluid
.
C
UDAPlace
(
0
)
if
use_cuda
else
fluid
.
C
PUPlace
()
exe
=
fluid
.
Executor
(
place
)
feeder
=
fluid
.
DataFeeder
(
feed_list
=
[
data
,
label
],
place
=
place
)
exe
.
run
(
fluid
.
default_startup_program
())
for
pass_id
in
xrange
(
PASS_NUM
):
accuracy
.
reset
(
exe
)
for
data
in
train_data
():
cost_val
,
acc_val
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
cost
,
acc_out
])
pass_acc
=
accuracy
.
eval
(
exe
)
print
(
"cost="
+
str
(
cost_val
)
+
" acc="
+
str
(
acc_val
)
+
" pass_acc="
+
str
(
pass_acc
))
if
cost_val
<
1.0
and
acc_val
>
0.8
:
exit
(
0
)
exit
(
1
)
print
(
"cost="
+
str
(
cost_val
)
+
" acc="
+
str
(
acc_val
))
if
cost_val
<
0.4
and
acc_val
>
0.8
:
return
raise
AssertionError
(
"Cost is too large for {0}"
.
format
(
net_method
.
__name__
))
class
TestUnderstandSentiment
(
unittest
.
TestCase
):
@
classmethod
def
setUpClass
(
cls
):
cls
.
word_dict
=
paddle
.
dataset
.
imdb
.
word_dict
()
@
contextlib
.
contextmanager
def
new_program_scope
(
self
):
prog
=
fluid
.
Program
()
startup_prog
=
fluid
.
Program
()
scope
=
fluid
.
core
.
Scope
()
with
fluid
.
scope_guard
(
scope
):
with
fluid
.
program_guard
(
prog
,
startup_prog
):
yield
def
test_conv_cpu
(
self
):
with
self
.
new_program_scope
():
main
(
self
.
word_dict
,
net_method
=
convolution_net
,
use_cuda
=
False
)
def
test_stacked_lstm_cpu
(
self
):
with
self
.
new_program_scope
():
main
(
self
.
word_dict
,
net_method
=
stacked_lstm_net
,
use_cuda
=
False
)
def
test_conv_gpu
(
self
):
with
self
.
new_program_scope
():
main
(
self
.
word_dict
,
net_method
=
convolution_net
,
use_cuda
=
True
)
def
test_stacked_lstm_gpu
(
self
):
with
self
.
new_program_scope
():
main
(
self
.
word_dict
,
net_method
=
stacked_lstm_net
,
use_cuda
=
True
)
if
__name__
==
'__main__'
:
main
()
unittest
.
main
()
python/paddle/v2/fluid/tests/book/test_understand_sentiment_conv.py
已删除
100644 → 0
浏览文件 @
94965806
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
from
__future__
import
print_function
import
numpy
as
np
import
paddle.v2
as
paddle
import
paddle.v2.fluid
as
fluid
def
convolution_net
(
data
,
label
,
input_dim
,
class_dim
=
2
,
emb_dim
=
32
,
hid_dim
=
32
):
emb
=
fluid
.
layers
.
embedding
(
input
=
data
,
size
=
[
input_dim
,
emb_dim
])
conv_3
=
fluid
.
nets
.
sequence_conv_pool
(
input
=
emb
,
num_filters
=
hid_dim
,
filter_size
=
3
,
act
=
"tanh"
,
pool_type
=
"sqrt"
)
conv_4
=
fluid
.
nets
.
sequence_conv_pool
(
input
=
emb
,
num_filters
=
hid_dim
,
filter_size
=
4
,
act
=
"tanh"
,
pool_type
=
"sqrt"
)
prediction
=
fluid
.
layers
.
fc
(
input
=
[
conv_3
,
conv_4
],
size
=
class_dim
,
act
=
"softmax"
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
prediction
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
adam_optimizer
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.002
)
adam_optimizer
.
minimize
(
avg_cost
)
accuracy
=
fluid
.
evaluator
.
Accuracy
(
input
=
prediction
,
label
=
label
)
return
avg_cost
,
accuracy
,
accuracy
.
metrics
[
0
]
def
to_lodtensor
(
data
,
place
):
seq_lens
=
[
len
(
seq
)
for
seq
in
data
]
cur_len
=
0
lod
=
[
cur_len
]
for
l
in
seq_lens
:
cur_len
+=
l
lod
.
append
(
cur_len
)
flattened_data
=
np
.
concatenate
(
data
,
axis
=
0
).
astype
(
"int64"
)
flattened_data
=
flattened_data
.
reshape
([
len
(
flattened_data
),
1
])
res
=
fluid
.
LoDTensor
()
res
.
set
(
flattened_data
,
place
)
res
.
set_lod
([
lod
])
return
res
def
main
():
BATCH_SIZE
=
100
PASS_NUM
=
5
word_dict
=
paddle
.
dataset
.
imdb
.
word_dict
()
dict_dim
=
len
(
word_dict
)
class_dim
=
2
data
=
fluid
.
layers
.
data
(
name
=
"words"
,
shape
=
[
1
],
dtype
=
"int64"
,
lod_level
=
1
)
label
=
fluid
.
layers
.
data
(
name
=
"label"
,
shape
=
[
1
],
dtype
=
"int64"
)
cost
,
accuracy
,
acc_out
=
convolution_net
(
data
,
label
,
input_dim
=
dict_dim
,
class_dim
=
class_dim
)
train_data
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
imdb
.
train
(
word_dict
),
buf_size
=
1000
),
batch_size
=
BATCH_SIZE
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
feeder
=
fluid
.
DataFeeder
(
feed_list
=
[
data
,
label
],
place
=
place
)
exe
.
run
(
fluid
.
default_startup_program
())
for
pass_id
in
xrange
(
PASS_NUM
):
accuracy
.
reset
(
exe
)
for
data
in
train_data
():
cost_val
,
acc_val
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
cost
,
acc_out
])
pass_acc
=
accuracy
.
eval
(
exe
)
print
(
"cost="
+
str
(
cost_val
)
+
" acc="
+
str
(
acc_val
)
+
" pass_acc="
+
str
(
pass_acc
))
if
cost_val
<
1.0
and
pass_acc
>
0.8
:
exit
(
0
)
exit
(
1
)
if
__name__
==
'__main__'
:
main
()
python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py
已删除
100644 → 0
浏览文件 @
94965806
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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
numpy
as
np
import
paddle.v2
as
paddle
import
paddle.v2.fluid
as
fluid
from
paddle.v2.fluid.layer_helper
import
LayerHelper
def
lstm
(
x
,
c_pre_init
,
hidden_dim
,
forget_bias
=
None
):
"""
This function helps create an operator for the LSTM (Long Short Term
Memory) cell that can be used inside an RNN.
"""
helper
=
LayerHelper
(
'lstm_unit'
,
**
locals
())
rnn
=
fluid
.
layers
.
StaticRNN
()
with
rnn
.
step
():
c_pre
=
rnn
.
memory
(
init
=
c_pre_init
)
x_t
=
rnn
.
step_input
(
x
)
before_fc
=
fluid
.
layers
.
concat
(
input
=
[
x_t
,
c_pre
],
axis
=
1
)
after_fc
=
fluid
.
layers
.
fc
(
input
=
before_fc
,
size
=
hidden_dim
*
4
)
dtype
=
x
.
dtype
c
=
helper
.
create_tmp_variable
(
dtype
)
h
=
helper
.
create_tmp_variable
(
dtype
)
helper
.
append_op
(
type
=
'lstm_unit'
,
inputs
=
{
"X"
:
after_fc
,
"C_prev"
:
c_pre
},
outputs
=
{
"C"
:
c
,
"H"
:
h
},
attrs
=
{
"forget_bias"
:
forget_bias
})
rnn
.
update_memory
(
c_pre
,
c
)
rnn
.
output
(
h
)
return
rnn
()
def
lstm_net
(
dict_dim
,
class_dim
=
2
,
emb_dim
=
32
,
seq_len
=
80
,
batch_size
=
50
):
data
=
fluid
.
layers
.
data
(
name
=
"words"
,
shape
=
[
seq_len
*
batch_size
,
1
],
append_batch_size
=
False
,
dtype
=
"int64"
,
lod_level
=
1
)
label
=
fluid
.
layers
.
data
(
name
=
"label"
,
shape
=
[
batch_size
,
1
],
append_batch_size
=
False
,
dtype
=
"int64"
)
emb
=
fluid
.
layers
.
embedding
(
input
=
data
,
size
=
[
dict_dim
,
emb_dim
])
emb
=
fluid
.
layers
.
reshape
(
x
=
emb
,
shape
=
[
batch_size
,
seq_len
,
emb_dim
])
emb
=
fluid
.
layers
.
transpose
(
x
=
emb
,
perm
=
[
1
,
0
,
2
])
c_pre_init
=
fluid
.
layers
.
fill_constant
(
dtype
=
emb
.
dtype
,
shape
=
[
batch_size
,
emb_dim
],
value
=
0.0
)
c_pre_init
.
stop_gradient
=
False
layer_1_out
=
lstm
(
emb
,
c_pre_init
=
c_pre_init
,
hidden_dim
=
emb_dim
)
layer_1_out
=
fluid
.
layers
.
transpose
(
x
=
layer_1_out
,
perm
=
[
1
,
0
,
2
])
prediction
=
fluid
.
layers
.
fc
(
input
=
layer_1_out
,
size
=
class_dim
,
act
=
"softmax"
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
prediction
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
adam_optimizer
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.002
)
adam_optimizer
.
minimize
(
avg_cost
)
acc
=
fluid
.
layers
.
accuracy
(
input
=
prediction
,
label
=
label
)
return
avg_cost
,
acc
def
to_lodtensor
(
data
,
place
):
seq_lens
=
[
len
(
seq
)
for
seq
in
data
]
cur_len
=
0
lod
=
[
cur_len
]
for
l
in
seq_lens
:
cur_len
+=
l
lod
.
append
(
cur_len
)
flattened_data
=
np
.
concatenate
(
data
,
axis
=
0
).
astype
(
"int64"
)
flattened_data
=
flattened_data
.
reshape
([
len
(
flattened_data
),
1
])
res
=
fluid
.
LoDTensor
()
res
.
set
(
flattened_data
,
place
)
res
.
set_lod
([
lod
])
return
res
def
chop_data
(
data
,
chop_len
=
80
,
batch_size
=
50
):
data
=
[(
x
[
0
][:
chop_len
],
x
[
1
])
for
x
in
data
if
len
(
x
[
0
])
>=
chop_len
]
return
data
[:
batch_size
]
def
prepare_feed_data
(
data
,
place
):
tensor_words
=
to_lodtensor
(
map
(
lambda
x
:
x
[
0
],
data
),
place
)
label
=
np
.
array
(
map
(
lambda
x
:
x
[
1
],
data
)).
astype
(
"int64"
)
label
=
label
.
reshape
([
len
(
label
),
1
])
tensor_label
=
fluid
.
LoDTensor
()
tensor_label
.
set
(
label
,
place
)
return
tensor_words
,
tensor_label
def
main
():
BATCH_SIZE
=
100
PASS_NUM
=
5
word_dict
=
paddle
.
dataset
.
imdb
.
word_dict
()
print
"load word dict successfully"
dict_dim
=
len
(
word_dict
)
class_dim
=
2
cost
,
acc
=
lstm_net
(
dict_dim
=
dict_dim
,
class_dim
=
class_dim
)
train_data
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
imdb
.
train
(
word_dict
),
buf_size
=
BATCH_SIZE
*
10
),
batch_size
=
BATCH_SIZE
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
fluid
.
default_startup_program
())
for
pass_id
in
xrange
(
PASS_NUM
):
for
data
in
train_data
():
chopped_data
=
chop_data
(
data
)
tensor_words
,
tensor_label
=
prepare_feed_data
(
chopped_data
,
place
)
outs
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
"words"
:
tensor_words
,
"label"
:
tensor_label
},
fetch_list
=
[
cost
,
acc
])
cost_val
=
np
.
array
(
outs
[
0
])
acc_val
=
np
.
array
(
outs
[
1
])
print
(
"cost="
+
str
(
cost_val
)
+
" acc="
+
str
(
acc_val
))
if
acc_val
>
0.7
:
exit
(
0
)
exit
(
1
)
if
__name__
==
'__main__'
:
main
()
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