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f91f1344
编写于
1月 25, 2018
作者:
C
Cao Ying
提交者:
GitHub
1月 25, 2018
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Merge pull request #7770 from peterzhang2029/nmt_fix
enhance the machine_translation model in unittest.
上级
b4565172
e3b8222c
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1
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1 changed file
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+202
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python/paddle/v2/fluid/tests/book/test_machine_translation_encoder_context.py
...id/tests/book/test_machine_translation_encoder_context.py
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python/paddle/v2/fluid/tests/book/test_machine_translation_encoder_context.py
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f91f1344
# 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
import
paddle.v2.fluid.core
as
core
import
paddle.v2.fluid.framework
as
framework
import
paddle.v2.fluid.layers
as
layers
from
paddle.v2.fluid.executor
import
Executor
dict_size
=
30000
source_dict_dim
=
target_dict_dim
=
dict_size
src_dict
,
trg_dict
=
paddle
.
dataset
.
wmt14
.
get_dict
(
dict_size
)
hidden_dim
=
32
embedding_dim
=
16
batch_size
=
10
max_length
=
50
topk_size
=
50
encoder_size
=
decoder_size
=
hidden_dim
IS_SPARSE
=
True
USE_PEEPHOLES
=
False
def
bi_lstm_encoder
(
input_seq
,
hidden_size
):
input_forward_proj
=
fluid
.
layers
.
fc
(
input
=
input_seq
,
size
=
hidden_size
*
4
,
bias_attr
=
True
)
forward
,
_
=
fluid
.
layers
.
dynamic_lstm
(
input
=
input_forward_proj
,
size
=
hidden_size
*
4
,
use_peepholes
=
USE_PEEPHOLES
)
input_backward_proj
=
fluid
.
layers
.
fc
(
input
=
input_seq
,
size
=
hidden_size
*
4
,
bias_attr
=
True
)
backward
,
_
=
fluid
.
layers
.
dynamic_lstm
(
input
=
input_backward_proj
,
size
=
hidden_size
*
4
,
is_reverse
=
True
,
use_peepholes
=
USE_PEEPHOLES
)
return
forward
,
backward
# FIXME(peterzhang2029): Replace this function with the lstm_unit_op.
def
lstm_step
(
x_t
,
hidden_t_prev
,
cell_t_prev
,
size
):
def
linear
(
inputs
):
return
fluid
.
layers
.
fc
(
input
=
inputs
,
size
=
size
,
bias_attr
=
True
)
forget_gate
=
fluid
.
layers
.
sigmoid
(
x
=
linear
([
hidden_t_prev
,
x_t
]))
input_gate
=
fluid
.
layers
.
sigmoid
(
x
=
linear
([
hidden_t_prev
,
x_t
]))
output_gate
=
fluid
.
layers
.
sigmoid
(
x
=
linear
([
hidden_t_prev
,
x_t
]))
cell_tilde
=
fluid
.
layers
.
tanh
(
x
=
linear
([
hidden_t_prev
,
x_t
]))
cell_t
=
fluid
.
layers
.
sums
(
input
=
[
fluid
.
layers
.
elementwise_mul
(
x
=
forget_gate
,
y
=
cell_t_prev
),
fluid
.
layers
.
elementwise_mul
(
x
=
input_gate
,
y
=
cell_tilde
)
])
hidden_t
=
fluid
.
layers
.
elementwise_mul
(
x
=
output_gate
,
y
=
fluid
.
layers
.
tanh
(
x
=
cell_t
))
return
hidden_t
,
cell_t
def
lstm_decoder_without_attention
(
target_embedding
,
decoder_boot
,
context
,
decoder_size
):
rnn
=
fluid
.
layers
.
DynamicRNN
()
cell_init
=
fluid
.
layers
.
fill_constant_batch_size_like
(
input
=
decoder_boot
,
value
=
0.0
,
shape
=
[
-
1
,
decoder_size
],
dtype
=
'float32'
)
cell_init
.
stop_gradient
=
False
with
rnn
.
block
():
current_word
=
rnn
.
step_input
(
target_embedding
)
context
=
rnn
.
static_input
(
context
)
hidden_mem
=
rnn
.
memory
(
init
=
decoder_boot
,
need_reorder
=
True
)
cell_mem
=
rnn
.
memory
(
init
=
cell_init
)
decoder_inputs
=
fluid
.
layers
.
concat
(
input
=
[
context
,
current_word
],
axis
=
1
)
h
,
c
=
lstm_step
(
decoder_inputs
,
hidden_mem
,
cell_mem
,
decoder_size
)
rnn
.
update_memory
(
hidden_mem
,
h
)
rnn
.
update_memory
(
cell_mem
,
c
)
out
=
fluid
.
layers
.
fc
(
input
=
h
,
size
=
target_dict_dim
,
bias_attr
=
True
,
act
=
'softmax'
)
rnn
.
output
(
out
)
return
rnn
()
def
seq_to_seq_net
():
"""Construct a seq2seq network."""
src_word_idx
=
fluid
.
layers
.
data
(
name
=
'source_sequence'
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
src_embedding
=
fluid
.
layers
.
embedding
(
input
=
src_word_idx
,
size
=
[
source_dict_dim
,
embedding_dim
],
dtype
=
'float32'
)
src_forward
,
src_backward
=
bi_lstm_encoder
(
input_seq
=
src_embedding
,
hidden_size
=
encoder_size
)
encoded_vector
=
fluid
.
layers
.
concat
(
input
=
[
src_forward
,
src_backward
],
axis
=
1
)
enc_vec_last
=
fluid
.
layers
.
sequence_last_step
(
input
=
encoded_vector
)
decoder_boot
=
fluid
.
layers
.
fc
(
input
=
enc_vec_last
,
size
=
decoder_size
,
bias_attr
=
False
,
act
=
'tanh'
)
trg_word_idx
=
fluid
.
layers
.
data
(
name
=
'target_sequence'
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
trg_embedding
=
fluid
.
layers
.
embedding
(
input
=
trg_word_idx
,
size
=
[
target_dict_dim
,
embedding_dim
],
dtype
=
'float32'
)
prediction
=
lstm_decoder_without_attention
(
trg_embedding
,
decoder_boot
,
enc_vec_last
,
decoder_size
)
label
=
fluid
.
layers
.
data
(
name
=
'label_sequence'
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
prediction
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
return
avg_cost
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
():
avg_cost
=
seq_to_seq_net
()
optimizer
=
fluid
.
optimizer
.
Adagrad
(
learning_rate
=
1e-4
)
optimizer
.
minimize
(
avg_cost
)
train_data
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
wmt14
.
train
(
dict_size
),
buf_size
=
1000
),
batch_size
=
batch_size
)
place
=
core
.
CPUPlace
()
exe
=
Executor
(
place
)
exe
.
run
(
framework
.
default_startup_program
())
batch_id
=
0
for
pass_id
in
xrange
(
2
):
for
data
in
train_data
():
word_data
=
to_lodtensor
(
map
(
lambda
x
:
x
[
0
],
data
),
place
)
trg_word
=
to_lodtensor
(
map
(
lambda
x
:
x
[
1
],
data
),
place
)
trg_word_next
=
to_lodtensor
(
map
(
lambda
x
:
x
[
2
],
data
),
place
)
outs
=
exe
.
run
(
framework
.
default_main_program
(),
feed
=
{
'source_sequence'
:
word_data
,
'target_sequence'
:
trg_word
,
'label_sequence'
:
trg_word_next
},
fetch_list
=
[
avg_cost
])
avg_cost_val
=
np
.
array
(
outs
[
0
])
print
(
'pass_id='
+
str
(
pass_id
)
+
' batch='
+
str
(
batch_id
)
+
" avg_cost="
+
str
(
avg_cost_val
))
if
batch_id
>
3
:
exit
(
0
)
batch_id
+=
1
if
__name__
==
'__main__'
:
main
()
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