Mercurial > pylearn
comparison doc/v2_planning/architecture.txt @ 1248:b9d0a326e3e7
architecture: Removed duplicated 'evaluate' statement in my pipeline sketch
| author | Olivier Delalleau <delallea@iro> |
|---|---|
| date | Thu, 23 Sep 2010 13:20:08 -0400 |
| parents | 46527ae6db53 |
| children |
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| 1245:808e38dce8d6 | 1248:b9d0a326e3e7 |
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| 166 of saving the final "best" model. What I had in mind is a typical "applied ML" | 166 of saving the final "best" model. What I had in mind is a typical "applied ML" |
| 167 experiment, i.e. the following approach that hopefully can be understood just | 167 experiment, i.e. the following approach that hopefully can be understood just |
| 168 by writing it down in the form of a processing pipeline. The double cross | 168 by writing it down in the form of a processing pipeline. The double cross |
| 169 validation step, whose goal is to obtain an estimate of the generalization | 169 validation step, whose goal is to obtain an estimate of the generalization |
| 170 error of our final model, is: | 170 error of our final model, is: |
| 171 data -> k_fold_outer(preprocessing -> k_fold_inner(dbn -> evaluate) -> select_best -> retrain_on_all_data -> evaluate) -> evaluate | 171 data -> k_fold_outer(preprocessing -> k_fold_inner(dbn -> evaluate) -> select_best -> retrain_on_all_data -> evaluate) |
| 172 Once this is done, the model we want to save is obtained by doing | 172 Once this is done, the model we want to save is obtained by doing |
| 173 data -> preprocessing -> k_fold(dbn -> evaluate) -> select_best -> retrain_on_all_data | 173 data -> preprocessing -> k_fold(dbn -> evaluate) -> select_best -> retrain_on_all_data |
| 174 and we save | 174 and we save |
| 175 preprocessing -> best_model_selected | 175 preprocessing -> best_model_selected |