Awoṣe Pruning adanwo

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

1. Kini pruning awoṣe yọ kuro lati inu nẹtiwọọki oṣiṣẹ kan?
   • Yiyọ awọn òṣuwọn tabi awọn ẹya ti o ṣe alabapin diẹ si iṣelọpọ
   • Fifi awọn paramita diẹ sii lati mu išedede dara sii
   • Ikẹkọ awoṣe fun awọn akoko diẹ sii
   • Nmu ipinnu titẹ sii pọ si
2. Kini o ṣe iyatọ si pruning ti a ṣeto si pruning ti a ko ṣeto?
   • Ti eleto pruning ṣiṣẹ nikan lori awọn aworan
   • Igi gige ti a ṣeto n yọ gbogbo awọn ẹya kuro bi awọn neuronu tabi awọn ori; unstructured yọ olukuluku òṣuwọn
   • Igi gige ti a ko ṣeto nilo atunṣe lati ibere
   • Ko si iyato to wulo laarin wọn
3. Kini idi ti pruning ti ko ni ipilẹ nigbagbogbo kuna lati yara yara awoṣe kan lori ohun elo lasan?
   • O npa ọpọlọpọ awọn fẹlẹfẹlẹ
   • Nigbagbogbo o dinku deede si odo
   • Awọn matiri iwuwo fọnka nilo ohun elo pataki tabi awọn ile-ikawe lati lo nilokulo awọn odo
   • O mu ki awọn paramita ka
4. Ohun ti o jẹ aṣoju aṣetunṣe pruning ilana?
   • Pa ohun gbogbo ni ẹẹkan, lẹhinna ran lọ
   • Ṣe iwọn ni akọkọ, lẹhinna ko tun ṣe ikẹkọ
   • Laileto paarẹ awọn ipele titi ti deede yoo lọ silẹ
   • Reluwe, piruni awọn paramita to ṣe pataki ti o kere ju, tune daradara lati gba pada, ati tun ṣe
5. Kini Irosọ Tikẹti Lotiri sọ?
   • Awọn nẹtiwọọki ipon ni awọn nẹtiwọọki abẹlẹ fọnka ti o le baamu awoṣe kikun nigba ikẹkọ lati ipilẹṣẹ to tọ
   • Pruning nigbagbogbo mu išedede dara
   • Igi gige laileto jẹ nigbagbogbo dara julọ
   • Awọn awoṣe nla ko le fisinuirindigbindigbin
6. Apeere ti o rọrun wo ni a lo lati pinnu iru awọn iwuwo lati piruni?
   • Ilana labidi ti awọn orukọ Layer
   • Awọn idi titobi ti awọn òṣuwọn
   • Awọn awọ ti awọn maapu imuṣiṣẹ
   • Nọmba awọn apẹẹrẹ ikẹkọ
7. When is pruning most likely to reduce inference latency on ordinary dense hardware?
   • When individual zeros are added but the dense kernel still computes them
   • When the model is renamed without changing its graph
   • When whole channels or other structures are removed to produce a smaller dense model
   • When a sparse checkpoint is stored in a larger file
8. What should a team measure after pruning for a deployment target?
   • Only the number of zeros in a checkpoint
   • Only training accuracy before pruning
   • Whether the file extension changed
   • Task quality and latency or memory on the intended hardware