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6a378251
编写于
5月 02, 2018
作者:
Y
Yu Yang
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Merge remote-tracking branch 'helin/word2vec' into feature/new_api_train_impl
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python/paddle/fluid/tests/book/word2vec/no_test_word2vec_new_api.py
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python/paddle/fluid/tests/book/word2vec/no_test_word2vec_new_api.py
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6a378251
# 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
paddle
import
paddle.fluid
as
fluid
import
numpy
as
np
import
math
import
sys
from
functools
import
partial
PASS_NUM
=
100
EMBED_SIZE
=
32
HIDDEN_SIZE
=
256
N
=
5
BATCH_SIZE
=
32
def
create_random_lodtensor
(
lod
,
place
,
low
,
high
):
# The range of data elements is [low, high]
data
=
np
.
random
.
random_integers
(
low
,
high
,
[
lod
[
-
1
],
1
]).
astype
(
"int64"
)
res
=
fluid
.
LoDTensor
()
res
.
set
(
data
,
place
)
res
.
set_lod
([
lod
])
return
res
word_dict
=
paddle
.
dataset
.
imikolov
.
build_dict
()
dict_size
=
len
(
word_dict
)
def
inference_network
(
is_sparse
):
first_word
=
fluid
.
layers
.
data
(
name
=
'firstw'
,
shape
=
[
1
],
dtype
=
'int64'
)
second_word
=
fluid
.
layers
.
data
(
name
=
'secondw'
,
shape
=
[
1
],
dtype
=
'int64'
)
third_word
=
fluid
.
layers
.
data
(
name
=
'thirdw'
,
shape
=
[
1
],
dtype
=
'int64'
)
forth_word
=
fluid
.
layers
.
data
(
name
=
'forthw'
,
shape
=
[
1
],
dtype
=
'int64'
)
embed_first
=
fluid
.
layers
.
embedding
(
input
=
first_word
,
size
=
[
dict_size
,
EMBED_SIZE
],
dtype
=
'float32'
,
is_sparse
=
is_sparse
,
param_attr
=
'shared_w'
)
embed_second
=
fluid
.
layers
.
embedding
(
input
=
second_word
,
size
=
[
dict_size
,
EMBED_SIZE
],
dtype
=
'float32'
,
is_sparse
=
is_sparse
,
param_attr
=
'shared_w'
)
embed_third
=
fluid
.
layers
.
embedding
(
input
=
third_word
,
size
=
[
dict_size
,
EMBED_SIZE
],
dtype
=
'float32'
,
is_sparse
=
is_sparse
,
param_attr
=
'shared_w'
)
embed_forth
=
fluid
.
layers
.
embedding
(
input
=
forth_word
,
size
=
[
dict_size
,
EMBED_SIZE
],
dtype
=
'float32'
,
is_sparse
=
is_sparse
,
param_attr
=
'shared_w'
)
concat_embed
=
fluid
.
layers
.
concat
(
input
=
[
embed_first
,
embed_second
,
embed_third
,
embed_forth
],
axis
=
1
)
hidden1
=
fluid
.
layers
.
fc
(
input
=
concat_embed
,
size
=
HIDDEN_SIZE
,
act
=
'sigmoid'
)
predict_word
=
fluid
.
layers
.
fc
(
input
=
hidden1
,
size
=
dict_size
,
act
=
'softmax'
)
return
predict_word
def
train_network
():
next_word
=
fluid
.
layers
.
data
(
name
=
'nextw'
,
shape
=
[
1
],
dtype
=
'int64'
)
predict_word
=
inference_network
()
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict_word
,
label
=
next_word
)
avg_cost
=
fluid
.
layers
.
mean
(
cost
)
return
avg_cost
def
train
(
use_cuda
,
is_sparse
,
save_path
):
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
imikolov
.
train
(
word_dict
,
N
),
BATCH_SIZE
)
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
def
event_handler
(
event
):
if
isinstance
(
event
,
fluid
.
Event
.
END_EPOCH
):
avg_cost
=
trainer
.
test
(
reader
=
paddle
.
dataset
.
imikolov
.
test
(
word_dict
,
N
))
if
avg_cost
<
5.0
:
trainer
.
params
.
save
(
save_path
)
return
if
math
.
isnan
(
avg_cost
):
sys
.
exit
(
"got NaN loss, training failed."
)
trainer
=
fluid
.
Trainer
(
partial
(
inference_network
,
is_sparse
),
fluid
.
optimizer
.
SGD
(
learning_rate
=
0.001
),
place
=
place
)
trainer
.
train
(
train_reader
,
100
,
event_handler
)
def
infer
(
use_cuda
,
save_path
):
params
=
fluid
.
Params
(
save_path
)
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
inferencer
=
fluid
.
Inferencer
(
inference_network
,
params
,
place
=
place
)
lod
=
[
0
,
1
]
first_word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
dict_size
-
1
)
second_word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
dict_size
-
1
)
third_word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
dict_size
-
1
)
fourth_word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
dict_size
-
1
)
result
=
inferencer
.
infer
({
'firstw'
:
first_word
,
'secondw'
:
second_word
,
'thirdw'
:
third_word
,
'forthw'
:
fourth_word
})
print
(
result
)
def
main
(
use_cuda
,
is_sparse
):
if
use_cuda
and
not
fluid
.
core
.
is_compiled_with_cuda
():
return
save_path
=
"word2vec.inference.model"
train
(
use_cuda
,
is_sparse
,
save_path
)
infer
(
use_cuda
,
save_path
)
if
__name__
==
'__main__'
:
for
use_cuda
in
(
False
,
True
):
for
is_sparse
in
(
False
,
True
):
main
(
use_cuda
=
use_cuda
,
is_sparse
=
is_sparse
)
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