Kudzidza Kuronga Kuronga Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Chii chinangwa chechikamu che'warmup' muchirongwa chechiyero chekudzidza?
   • Kutanga nechiyero chidiki uye kukwira kumusoro, kudzivirira kusagadzikana kubva kune ekutanga ruzha gradients
   • Kuita kuti chokwadi chekupedzisira chidzike
   • Kuti usvetuke mashoma ekutanga mabheji edata
   • Kuomesa huremu hwese pakutanga
2. Ndeipi purogiramu inonyatso tevera hafu-cosine curve pasi kusvika pachiyero chidiki?
   • Nguva dzose purogiramu
   • Cosine annealing
   • Random schedule
   • Linear kuwedzera
3. Chii chinoitika kana mwero wekudzidza ukakwidzwa zvakanyanya?
   • Kudzidzira kunopindirana ipapo nekurongeka kwakakwana
   • Iyo modhi inodzidzisa zvishoma asi inogara ichibudirira
   • Iko kurasikirwa kunogona kutsauka kana kutenderera pane kuderera
   • Hapana chinoshanduka; chiyero hachina mhedzisiro
4. Kuora kwedanho kunoitei pachiyero chekudzidza?
   • Inowedzera iyo linearly batch imwe neimwe
   • Inoichengeta zvachose
   • Inoiisa zvisina tsarukano nhanho imwe neimwe
   • Inodonhedza nechinhu chakagadziriswa panguva dzakafanotsanangurwa
5. What does a warm restart do to a learning-rate schedule?
   • It raises the learning rate again to begin another schedule cycle
   • It resets the model parameters to random values
   • It permanently fixes the learning rate at zero
   • It deletes the optimizer’s moment estimates
6. In Goyal et al.’s large-minibatch ImageNet setup, what did their warmup scheme address?
   • It removed the need for a validation set
   • It eased early optimization difficulties after the learning-rate scale increased with batch size
   • It changed the model architecture after each epoch
   • It converted the learning-rate curve into a cosine wave
7. In the cosine formula, what does T denote?
   • The number of model layers
   • Saizi yebatch
   • The schedule horizon or total number of steps used for the decay
   • The optimizer’s momentum coefficient
8. If the cosine schedule uses lr_min greater than zero, where does the rate approach at the end of the horizon?
   • Exactly zero regardless of lr_min
   • The original warmup starting rate
   • An unbounded value
   • The specified minimum learning rate