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体验新版 GitCode,发现更多精彩内容 >>
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cc5adfb8
编写于
11月 14, 2016
作者:
A
alvations
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差异文件
added resnet lstm architecture from GNMT
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demo/quick_start/train.sh
demo/quick_start/train.sh
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demo/quick_start/trainer_config.resnet-lstm.py
demo/quick_start/trainer_config.resnet-lstm.py
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demo/quick_start/train.sh
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...
...
@@ -20,6 +20,7 @@ cfg=trainer_config.lr.py
#cfg=trainer_config.lstm.py
#cfg=trainer_config.bidi-lstm.py
#cfg=trainer_config.db-lstm.py
#cfg=trainer_config.resnet-lstm.py
paddle train
\
--config
=
$cfg
\
--save_dir
=
./output
\
...
...
demo/quick_start/trainer_config.resnet-lstm.py
0 → 100644
浏览文件 @
cc5adfb8
# edit-mode: -*- python -*-
# Copyright (c) 2016 Baidu, Inc. 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.
"""
This configuration is a demonstration of how to implement the stacked LSTM
with residual connections, i.e. an LSTM layer takes the sum of the hidden states
and inputs of the previous LSTM layer instead of only the hidden states.
This architecture is from:
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi,
Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey,
Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser,
Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens,
George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa,
Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, Jeffrey Dean. 2016.
Google's Neural Machine Translation System: Bridging the Gap between Human and
Machine Translation. In arXiv https://arxiv.org/pdf/1609.08144v2.pdf
Different from the architecture described in the paper, we use a stack single
direction LSTM layers as the first layer instead of bi-directional LSTM. Also,
since this is a demo code, to reduce computation time, we stacked 4 layers
instead of 8 layers.
"""
from
paddle.trainer_config_helpers
import
*
dict_file
=
"./data/dict.txt"
word_dict
=
dict
()
with
open
(
dict_file
,
'r'
)
as
f
:
for
i
,
line
in
enumerate
(
f
):
w
=
line
.
strip
().
split
()[
0
]
word_dict
[
w
]
=
i
is_predict
=
get_config_arg
(
'is_predict'
,
bool
,
False
)
trn
=
'data/train.list'
if
not
is_predict
else
None
tst
=
'data/test.list'
if
not
is_predict
else
'data/pred.list'
process
=
'process'
if
not
is_predict
else
'process_predict'
define_py_data_sources2
(
train_list
=
trn
,
test_list
=
tst
,
module
=
"dataprovider_emb"
,
obj
=
process
,
args
=
{
"dictionary"
:
word_dict
})
batch_size
=
128
if
not
is_predict
else
1
settings
(
batch_size
=
batch_size
,
learning_rate
=
2e-3
,
learning_method
=
AdamOptimizer
(),
regularization
=
L2Regularization
(
8e-4
),
gradient_clipping_threshold
=
25
)
bias_attr
=
ParamAttr
(
initial_std
=
0.
,
l2_rate
=
0.
)
data
=
data_layer
(
name
=
"word"
,
size
=
len
(
word_dict
))
emb
=
embedding_layer
(
input
=
data
,
size
=
128
)
lstm
=
simple_lstm
(
input
=
emb
,
size
=
128
,
lstm_cell_attr
=
ExtraAttr
(
drop_rate
=
0.1
))
previous_input
,
previous_hidden_state
=
emb
,
lstm
for
i
in
range
(
3
):
# The input to the current layer is the sum of the hidden state
# and input of the previous layer.
current_input
=
addto_layer
(
input
=
[
previous_input
,
previous_hidden_state
])
hidden_state
=
simple_lstm
(
input
=
current_input
,
size
=
128
,
lstm_cell_attr
=
ExtraAttr
(
drop_rate
=
0.1
))
previous_input
,
previous_hidden_state
=
current_input
,
hidden_state
lstm
=
previous_hidden_state
lstm_last
=
pooling_layer
(
input
=
lstm
,
pooling_type
=
MaxPooling
())
output
=
fc_layer
(
input
=
lstm_last
,
size
=
2
,
bias_attr
=
bias_attr
,
act
=
SoftmaxActivation
())
if
is_predict
:
maxid
=
maxid_layer
(
output
)
outputs
([
maxid
,
output
])
else
:
label
=
data_layer
(
name
=
"label"
,
size
=
2
)
cls
=
classification_cost
(
input
=
output
,
label
=
label
)
outputs
(
cls
)
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