Model Performance is just 88%

[lisn3]

I trained the model using parameters in the paper for 300 epochs, but the final training accuracy is just 88%, and the testing F1-score is 88%. Could you please give some ideas? Has anyone got a higher performance?

[wuyifan18]

@lisn3 try to use list to generate the full dataset, just like this: https://github.com/wuyifan18/DeepLog/blob/502aaf05be4c1251b7dc96f6439025c4fc988c66/LogKeyModel_predict.py#L11-L22

[lisn3]

Thanks, I will try again.

[Abraham12580]

Thanks, I will try again.

hi，have your performance gotten improved yet?

[ldselvera]

Unfortunately, that will be the highest performance with this implementation. The only way to increase it is by modifying the model and fine tuning parameters.

[hayhan]

Use one-hot vector for input data may help.

[gutjuri]

If you do what wuifan suggested (replacing set with list) you'll get a tremendously increased performance.

elapsed_time: 2877.333s
false positive (FP): 841, false negative (FN): 381, Precision: 95.138%, Recall: 97.737%, F1-measure: 96.420%

[Abraham12580]

您好，我已经收到你的邮件。祝您工作顺利，天天开心思密达~                                                                 唐子超