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| （相同用户的一个中间修订版本未显示） |
| 第1行： |
第1行： |
| | Last Week: | | Last Week: |
| − | * Did following experiments:
| + | ---- |
| − | * pooling method.(have run 195 hours for 117 iterations. performance is still raising(0.49). but haven't exceeded transE yet.(0.53), one iteration takes 1h40min )
| + | * Read Tensorflow guidebook. |
| − | * pooling method with sampling and minibatch.(performance grew very slowly after several iteration.(0.37) It seems that sampling is not as good as origin pooling method. And because cost stops going down when it is still a quite big value, which means sampling may lead to a saddle point? one iteration takes 40 minutes)
| + | * Run tensorflow's translation code. |
| − | * attention pooling method.(very slow, one iteration may take two days or more. still waiting for the performance of first iteration.)
| + | |
| − | * attention pooling method with sampling and minibatch.( still running. one iteration takes 1 hour.)
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| | This Week: | | This Week: |
| − | * waiting for results and also think about other improvements. | + | ---- |
| − | * pooling method with entity and relation is too slow. Another simplification is to ignore the around entities during pooling, only relation types left, which leads to a feature vector joining behind the orginal entity vector, describing the type of entity. This is more like the original transE. | + | * Read the translation code and print alignment information. |
| − | * I feel that the chain rule should based on random walk. Mean pooling leads to equal weights. But random walk give high possibility to high page rank entities. So there should be improvements.
| + | * Try to hack into the code and change the structure. |
