“Asr-language-processing-research-rnng-mn”版本间的差异

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第10行： 第10行：
 
| 2016/10/31
 
| 2016/10/31
 
|  
 
|  
−
# implement rnng+static memory discriminative model
+
* implement rnng+static memory discriminative model
−
* fix the unexpected action
+
# fix the unexpected action
−
* rerun the original discriminative model
+
#  rerun the original discriminative model
−
* rerun the centred memory rnng model
+
# rerun the centred memory rnng model
−
* get wrong instances of original trained model, and get statistics
+
#  get wrong instances of original trained model, and get statistics
−
* run the wrong memory rnng model
+
# run the wrong memory rnng model
−
* run the sampled memory rnng model
+
# run the sampled memory rnng model
−
* update experiment report
+
#  update experiment report
 
||
 
||
−
* fixed the unexpected action
+
* implementation is done, but result is not satisfied.
−
* reran the original discriminative model
+
#  fixed the unexpected action
−
* reran the centred memory rnng model
+
# reran the original discriminative model
−
* got wrong instances of original trained model, and get statistics
+
#  reran the centred memory rnng model
−
* ran the sampled memory rnng model
+
#  got wrong instances of original trained model, and get statistics
−
* ran the wrong memory rnng model
+
# ran the sampled memory rnng model
−
* updated experiment report [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/2/2f/RNNG%2Bmm%E5%AE%9E%E9%AA%8C%E6%8A%A5%E5%91%8A.pdf]
+
#  ran the wrong memory rnng model
 +
# updated experiment report [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/2/2f/RNNG%2Bmm%E5%AE%9E%E9%AA%8C%E6%8A%A5%E5%91%8A.pdf]
 
||
 
||
 
* 100%
 
* 100%
第31行： 第32行：
 
| 2016/11/7
 
| 2016/11/7
 
|  
 
|  
−
* modify model  
+
* modify discriminative model,  try to prove the positive function of static memory
−
* try to prove the positive function of static memory
+
# change train set
−
||  ||
+
# add parameter before cos
 +
# modify model structure
 +
* read the code of Teacher Feng
 +
# understand and run
 +
* how to use GPU in rnng
 +
# learn to use it
 +
||   
 +
* modify discriminative model,  try to prove the positive function of static memory
 +
# changed train set
 +
# added parameter before cos
 +
# modified model structure
 +
* read the code of Teacher Feng
 +
# understood but did't run
 +
* how to use GPU in rnng
 +
# the result is rnng cannot run fast on GPU
 +
* update the report [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/2/2f/RNNG%2Bmm%E5%AE%9E%E9%AA%8C%E6%8A%A5%E5%91%8A.pdf report]
 +
||
 +
* 90%
 
|-
 
|-
 
| 2016/11/14
 
| 2016/11/14
 
|
 
|
−
* try to implement a dynamic memory  
+
* try to run rnng on multi cpu cores
−
||  ||
+
* refine the models tried before and give the result report
 +
* finish the code of dynamic memory model
 +
||  
 +
* implemented on MKL successfully
 +
* reran original model
 +
* reran the models  tried before, but still running
 +
* finished the code of dynamic memory model, ran and got a result
 +
* tried another structure of memory
 +
  ||
 +
* 90%
 
|-
 
|-
 
| 2016/11/21
 
| 2016/11/21
 
|  
 
|  
−
* modify model  
+
* try more different models
−
* try to prove the positive function of dynamic memory
+
# rerun original model, if the same result
 +
# run more dynamic memory models, hope to see better results
 +
# run models with another structure of memory, if it is better than previous structure
 +
* summary experiments' results and give report
 +
* publish the TRPs of RNNG
 
||  ||
 
||  ||
 
|-
 
|-

2016年11月21日 (一) 06:55的最后版本

Main Idea

People

Yang Feng, Shiyue Zhang, Andi Zhang

Time Table

Date Work Plan Work Done Completion Rate
2016/10/31
  • implement rnng+static memory discriminative model
  1. fix the unexpected action
  2. rerun the original discriminative model
  3. rerun the centred memory rnng model
  4. get wrong instances of original trained model, and get statistics
  5. run the wrong memory rnng model
  6. run the sampled memory rnng model
  7. update experiment report
  • implementation is done, but result is not satisfied.
  1. fixed the unexpected action
  2. reran the original discriminative model
  3. reran the centred memory rnng model
  4. got wrong instances of original trained model, and get statistics
  5. ran the sampled memory rnng model
  6. ran the wrong memory rnng model
  7. updated experiment report [1]
  • 100%
2016/11/7
  • modify discriminative model, try to prove the positive function of static memory
  1. change train set
  2. add parameter before cos
  3. modify model structure
  • read the code of Teacher Feng
  1. understand and run
  • how to use GPU in rnng
  1. learn to use it
  • modify discriminative model, try to prove the positive function of static memory
  1. changed train set
  2. added parameter before cos
  3. modified model structure
  • read the code of Teacher Feng
  1. understood but did't run
  • how to use GPU in rnng
  1. the result is rnng cannot run fast on GPU
  • 90%
2016/11/14
  • try to run rnng on multi cpu cores
  • refine the models tried before and give the result report
  • finish the code of dynamic memory model
  • implemented on MKL successfully
  • reran original model
  • reran the models tried before, but still running
  • finished the code of dynamic memory model, ran and got a result
  • tried another structure of memory
  • 90%
2016/11/21
  • try more different models
  1. rerun original model, if the same result
  2. run more dynamic memory models, hope to see better results
  3. run models with another structure of memory, if it is better than previous structure
  • summary experiments' results and give report
  • publish the TRPs of RNNG
2016/11/28
  • modify model
  • try to prove the positive function of dynamic memory
2016/12/5
  • get the first final rnng+mm discriminative model
2016/12/12
  • give a plan to transfer to generative model
2016/12/19
  • implement rnng+mm generative model
2016/12/26
  • modify model
  • try to prove the positive function of memory

Progress

取自“http://cslt.org/mediawiki/index.php?title=Asr-language-processing-research-rnng-mn&oldid=23973”