提交 e387cdba 编写于 作者: A alvations 提交者: Yu Yang

Added Bidi-LSTM and DB-LSTM to quick_start demo (#226)

上级 e1f57bfd
...@@ -18,6 +18,8 @@ cfg=trainer_config.lr.py ...@@ -18,6 +18,8 @@ cfg=trainer_config.lr.py
#cfg=trainer_config.emb.py #cfg=trainer_config.emb.py
#cfg=trainer_config.cnn.py #cfg=trainer_config.cnn.py
#cfg=trainer_config.lstm.py #cfg=trainer_config.lstm.py
#cfg=trainer_config.bidi-lstm.py
#cfg=trainer_config.db-lstm.py
paddle train \ paddle train \
--config=$cfg \ --config=$cfg \
--save_dir=./output \ --save_dir=./output \
......
# 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.
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)
bi_lstm = bidirectional_lstm(input=emb, size=128)
dropout = dropout_layer(input=bi_lstm, dropout_rate=0.5)
output = fc_layer(input=dropout, 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)
# 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.
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)
hidden_0 = mixed_layer(size=128, input=[full_matrix_projection(input=emb)])
lstm_0 = lstmemory(input=hidden_0, layer_attr=ExtraAttr(drop_rate=0.1))
input_layers = [hidden_0, lstm_0]
for i in range(1,8):
fc = fc_layer(input=input_layers, size=128)
lstm = lstmemory(input=fc, layer_attr=ExtraAttr(drop_rate=0.1),
reverse=(i % 2) == 1,)
input_layers = [fc, lstm]
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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