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8b30e2ab
编写于
11月 02, 2017
作者:
F
fengjiayi
提交者:
GitHub
11月 02, 2017
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Book chap6 (#5321)
* init * Fix bug * rename test_filw * refine test
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python/paddle/v2/framework/tests/test_understand_sentiment_conv.py
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8b30e2ab
import
paddle.v2
as
paddle
import
paddle.v2.framework.layers
as
layers
import
paddle.v2.framework.nets
as
nets
import
paddle.v2.framework.core
as
core
import
paddle.v2.framework.optimizer
as
optimizer
from
paddle.v2.framework.framework
import
Program
,
g_program
,
g_init_program
from
paddle.v2.framework.executor
import
Executor
import
numpy
as
np
def
convolution_net
(
input_dim
,
class_dim
=
2
,
emb_dim
=
32
,
hid_dim
=
32
):
data
=
layers
.
data
(
name
=
"words"
,
shape
=
[
1
],
data_type
=
"int64"
)
label
=
layers
.
data
(
name
=
"label"
,
shape
=
[
1
],
data_type
=
"int64"
)
emb
=
layers
.
embedding
(
input
=
data
,
size
=
[
input_dim
,
emb_dim
])
conv_3
=
nets
.
sequence_conv_pool
(
input
=
emb
,
num_filters
=
hid_dim
,
filter_size
=
3
,
act
=
"tanh"
,
pool_type
=
"sqrt"
)
conv_4
=
nets
.
sequence_conv_pool
(
input
=
emb
,
num_filters
=
hid_dim
,
filter_size
=
4
,
act
=
"tanh"
,
pool_type
=
"sqrt"
)
prediction
=
layers
.
fc
(
input
=
[
conv_3
,
conv_4
],
size
=
class_dim
,
act
=
"softmax"
)
cost
=
layers
.
cross_entropy
(
input
=
prediction
,
label
=
label
)
avg_cost
=
layers
.
mean
(
x
=
cost
)
adam_optimizer
=
optimizer
.
AdamOptimizer
(
learning_rate
=
0.002
)
opts
=
adam_optimizer
.
minimize
(
avg_cost
)
acc
=
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
=
core
.
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
cost
,
acc
=
convolution_net
(
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
=
core
.
CPUPlace
()
exe
=
Executor
(
place
)
exe
.
run
(
g_init_program
)
for
pass_id
in
xrange
(
PASS_NUM
):
for
data
in
train_data
():
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
([
BATCH_SIZE
,
1
])
tensor_label
=
core
.
LoDTensor
()
tensor_label
.
set
(
label
,
place
)
outs
=
exe
.
run
(
g_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
cost_val
<
1.0
and
acc_val
>
0.7
:
exit
(
0
)
exit
(
1
)
if
__name__
==
'__main__'
:
main
()
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