深度注意力机制模型
深度注意力机制模型（Deep Attention Matching Network）是开放领域多轮对话匹配模型。根据多轮对话历史和候选回复内容，排序出最合适的回复。 网络结构如下
任务定义与建模
多轮对话匹配任务输入是多轮对话历史和候选回复，输出是回复匹配得分，根据匹配得分排序。
模型原理介绍
可以参考论文:http://aclweb.org/anthology/P18-1103.
数据格式说明
训练、预测、评估使用的数据示例如下，数据由三列组成，以制表符（'\t'）分隔，第一列是以空 格分开的上文id，第二列是以空格分开的回复id，第三列是标签
286 642 865 36 87 25 693 0
17 54 975 512 775 54 6 1
注：本项目额外提供了分词预处理脚本（在preprocess目录下），可供用户使用，具体使用方法如 下：
python tokenizer.py \
--test_data_dir ./test.txt.utf8 \
--batch_size 1 > test.txt.utf8.seg
代码结构说明
main.py：该项目的主函数，封装包括训练、预测的部分
config.py：定义了该项目模型的相关配置，包括具体模型类别、以及模型的超参数
reader.py：定义了读入数据，加载词典的功能
evaluation.py：定义评估函数
run.sh：训练、预测运行脚本
文件介绍
deep_attention_matching/: 存放深度注意力机制模型的主要执行文件
deep_attention_matching/data目录下存放ubuntu数据集
deep_attention_matching/model_files下存放训练好的模型数据.
preprocess:数据预处理流程
本例算法运行基于GPU，若采用CPU，请将参数--use_cuda该行删去即可
In[1]
#本例采用ubuntu对话数据集
!cd data/data11447/ && unzip -qo ubuntu.zip In[25]
#模型训练，可通过修改data_path更换数据集
!python deep_attention_matching/main.py \
--do_train True \
--use_cuda \
--data_path data/data11447/ubuntu/data_small.pkl \
--save_path deep_attention_matching/model_files/ubuntu \
--vocab_size 434512 \
--_EOS_ 28270 \
--batch_size 32----------- Configuration Arguments -----------
_EOS_: 28270
batch_size: 32
channel1_num: 32
channel2_num: 16
data_path: data/data11447/ubuntu/data_small.pkl
do_infer: False
do_train: True
emb_size: 200
ext_eval: False
learning_rate: 0.001
max_turn_len: 50
max_turn_num: 9
model_path: None
num_scan_data: 2
save_path: deep_attention_matching/model_files/ubuntu
stack_num: 5
use_cuda: True
vocab_size: 434512
word_emb_init: None
------------------------------------------------
begin memory optimization ...
2019-09-05 15:58:35
end memory optimization ...
2019-09-05 15:58:35
device count 1
theoretical memory usage:
(8378.70401058197, 8777.689915847779, 'MB')
W0905 15:58:37.603806 1186 device_context.cc:259] Please NOTE: device: 0, CUDA Capability: 70, Driver API Version: 9.2, Runtime API Version: 9.0
W0905 15:58:37.611320 1186 device_context.cc:267] device: 0, cuDNN Version: 7.3.
WARNING:root:
You can try our memory optimize feature to save your memory usage:
# create a build_strategy variable to set memory optimize option
build_strategy = compiler.BuildStrategy()
build_strategy.enable_inplace = True
build_strategy.memory_optimize = True
# pass the build_strategy to with_data_parallel API
compiled_prog = compiler.CompiledProgram(main).with_data_parallel(
loss_name=loss.name, build_strategy=build_strategy)
!!! Memory optimize is our experimental feature !!!
some variables may be removed/reused internal to save memory usage,
in order to fetch the right value of the fetch_list, please set the
persistable property to true for each variable in fetch_list
# Sample
conv1 = fluid.layers.conv2d(data, 4, 5, 1, act=None)
# if you need to fetch conv1, then:
conv1.persistable = True
I0905 15:58:37.884307 1186 parallel_executor.cc:329] The number of CUDAPlace, which is used in ParallelExecutor, is 1. And the Program will be copied 1 copies
I0905 15:58:38.161104 1186 build_strategy.cc:340] SeqOnlyAllReduceOps:0, num_trainers:1
WARNING:root:
You can try our memory optimize feature to save your memory usage:
# create a build_strategy variable to set memory optimize option
build_strategy = compiler.BuildStrategy()
build_strategy.enable_inplace = True
build_strategy.memory_optimize = True
# pass the build_strategy to with_data_parallel API
compiled_prog = compiler.CompiledProgram(main).with_data_parallel(
loss_name=loss.name, build_strategy=build_strategy)
!!! Memory optimize is our experimental feature !!!
some variables may be removed/reused internal to save memory usage,
in order to fetch the right value of the fetch_list, please set the
persistable property to true for each variable in fetch_list
# Sample
conv1 = fluid.layers.conv2d(data, 4, 5, 1, act=None)
# if you need to fetch conv1, then:
conv1.persistable = True
share_vars_from is set, scope is ignored.
I0905 15:58:38.416013 1186 parallel_executor.cc:329] The number of CUDAPlace, which is used in ParallelExecutor, is 1. And the Program will be copied 1 copies
I0905 15:58:38.489773 1186 build_strategy.cc:340] SeqOnlyAllReduceOps:0, num_trainers:1
start loading data ...
finish loading data ...
begin model training ...
2019-09-05 15:58:39
[1676 9116 5609 ... 1722 2436 5949]
processed: [0.00961538461538] ave loss: [0.7781140208244324]
processed: [0.0192307692308] ave loss: [0.7981151739756266]
processed: [0.0288461538462] ave loss: [0.6933611432711283]
processed: [0.0384615384615] ave loss: [0.7092911005020142] In[21]
#模型评估,预测结果保存在--save_path中，此处仅打印出预测结果中的一例
#turns为多轮历史对话，response为回复，scores为回复得分
!python deep_attention_matching/main.py \
--do_infer True \
--use_cuda \
--data_path ./data/data11447/ubuntu/data_small.pkl \
--save_path deep_attention_matching/infer_result \
--model_path deep_attention_matching/model_files/ubuntu/ \
--vocab_size 434512 \
--_EOS_ 28270 \
--batch_size 1----------- Configuration Arguments -----------
_EOS_: 28270
batch_size: 1
channel1_num: 32
channel2_num: 16
data_path: ./data/data11447/ubuntu/data_small.pkl
do_infer: True
do_train: False
emb_size: 200
ext_eval: False
learning_rate: 0.001
max_turn_len: 50
max_turn_num: 9
model_path: deep_attention_matching/model_files/ubuntu/
num_scan_data: 2
save_path: deep_attention_matching/infer_result
stack_num: 5
use_cuda: True
vocab_size: 434512
word_emb_init: None
------------------------------------------------
W0905 15:46:10.321343 919 device_context.cc:259] Please NOTE: device: 0, CUDA Capability: 70, Driver API Version: 9.2, Runtime API Version: 9.0
W0905 15:46:10.325507 919 device_context.cc:267] device: 0, cuDNN Version: 7.3.
start loading data ...
finish loading data ...
test batch num: 1000
begin inference ...
2019-09-05 15:46:11
('turns:', array([[[393704, 0, 0, 0, 0, 0, 0, 0,
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[250191, 34296, 350284, 30835, 59150, 74395, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
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[180037, 28847, 88281, 115692, 413324, 279504, 354176, 20481,
418397, 418397, 177048, 197682, 115692, 373516, 192382, 285320,
20484, 20494, 229901, 9751, 20494, 11317, 20484, 347085,
0, 0, 0, 0, 0, 0, 0, 0,
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0, 0],
[ 97341, 291041, 14781, 414881, 126529, 174798, 1828, 324795,
324507, 227764, 20484, 54259, 7198, 296758, 259553, 354176,
123155, 20484, 149834, 343709, 238, 20484, 106788, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[131277, 326026, 146729, 170184, 180037, 418453, 20484, 20494,
229901, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[131277, 326026, 207572, 307055, 284678, 285320, 180037, 404776,
364101, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[395666, 75844, 233777, 195724, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[ 81947, 427943, 257613, 20484, 373516, 1598, 395666, 233777,
195724, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0],
[262553, 368624, 15331, 115692, 107043, 343709, 27044, 307801,
367146, 309360, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0]]]))
('response:', array([[[131277],
[104719],
[285320],
[191048],
[393704],
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('scores:', array([[-0.2420864]], dtype=float32))
finish test
2019-09-05 15:46:36 点击链接，使用AI Studio一键上手实践项目吧：https://aistudio.baidu.com/aistudio/projectdetail/122287
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## GPU版本安装命令
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