“14-10-19 Dongxu Zhang”版本间的差异
来自cslt Wiki
（→Accomplished this week） |
（→Accomplished this week） |
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* Train LSTM-Rnn LM with 200MB corpus(vocabulary 10k, classes 100, i100*m100). when using 2 cpu kernels, it takes aroung 200min per epoch. | * Train LSTM-Rnn LM with 200MB corpus(vocabulary 10k, classes 100, i100*m100). when using 2 cpu kernels, it takes aroung 200min per epoch. | ||
* Train 5-gram LM using Baiduzhidao_corpus(~30GB after preprocess) with new lexicon. There is a mistake when counted possiblity after merge. | * Train 5-gram LM using Baiduzhidao_corpus(~30GB after preprocess) with new lexicon. There is a mistake when counted possiblity after merge. | ||
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* Read paper "Learning Long-Term Dependencies with Gradient Descent is Difficult". Still in progress. | * Read paper "Learning Long-Term Dependencies with Gradient Descent is Difficult". Still in progress. | ||
| + | * An idea occured to me which may improve word2vec with much more semantic information. But there is huge computation complexity problem that bothers me, which I wish we can discuss. | ||
=== Next week === | === Next week === | ||
2014年10月19日 (日) 12:45的版本
Accomplished this week
- Train LSTM-Rnn LM with 200MB corpus(vocabulary 10k, classes 100, i100*m100). when using 2 cpu kernels, it takes aroung 200min per epoch.
- Train 5-gram LM using Baiduzhidao_corpus(~30GB after preprocess) with new lexicon. There is a mistake when counted possiblity after merge.
- Read paper "Learning Long-Term Dependencies with Gradient Descent is Difficult". Still in progress.
- An idea occured to me which may improve word2vec with much more semantic information. But there is huge computation complexity problem that bothers me, which I wish we can discuss.
Next week
- Test LSTM-Rnn LM.
- Finished building lexion.
- Understand the paper.
- May have time to achieve my baseline idea on text8.
