Adam ak Optimisateurs Adaptifs
Adam mooy optimisatëru fasu liggéey bi ci ginaaw reso neuronal yu bees yi, di aju ci saasi tolluwaayu jàng bu wuute ci parametre bu nekk.
Résumé
It matters because it makes training deep models faster and far less finicky than plain gradient descent.
Plongeur bu xóot
Adam (Xayma waxtu buy méngoo), bi Kingma ak Ba dugal ci 2014, dafa boole ñaari xalaat. Bi njëkk mooy momentum: dafay tëye ab moyenne buy jeex ci gradient yi weesu (waxtu bu njëkk bi) suko defee yeesali tabax gaawaay ci yoon yu méngoo. Ñaareel ba mooy, eskalaasioŋ bu nekk ci parametre yi: dafay topp gradient kaare yi (ñaareelu saa bi) ba noppi xaaj jéego bu nekk ak rasine kare bu valeur boobu, suko defee parametre yi am gradient yu yaatu te bari bruit dañuy jël jéego yu gëna ndaw, te yu bariwul luñuy yeesal dañuy jël jéego yu gëna mag. Adaptabilite bii dafay tekki ni mën nga jëfandikoo benn tolluwaayu jàng ci reso bi yépp. Benn anam, AdamW, dafay dindi diisaay bi ci yeesali gradient bi ba noppi nekk na default ngir tàggat transformateur yu mag ak modeli làkk.
Gis-gis xarala
Adam dafay tëye ñaari moyenne ci parametre bu nekk: m (degrade) ak v (degrade kaare), ñu yeesal ko ak tolluwaayu yàqu-yàqu beta1 (dafay faral di nekk 0.9) ak beta2 (dafay faral di nekk 0.999). Ndax ñoom ñaar ñu ngi tàmbalee ci nul, dañu leen di xaaj (1 - beta^t) ngir saafara jafe-jafe yi. Coppite bi mooy theta = theta - lr * m_hat / (sqrt (v_hat) + epsilon), fu epsilon (ci diggu 1e-8) tere xaaj ak nul. Lii moo tax Adam soxla tuning bu néew ci wàllu jàng buñu ko méngale ak SGD bu woyof bi.
njeextalu pexe
Njëgg ak budget
Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw.
dogal yu gëna leer
Njàngalem xarala yi dafay jàppale ekip yi ñu tànn li gën, te baña yam ci li gëna bees daal.
Xool kalite
Tanneef yu gëna baax ci wàllu ingeñër dina wàññi jafe-jafe yi ci wàllu wóor ci liggéey bi.
Ëlëgu Adam ak Optimiser yiy méngoo
Adam ak AdamW ñu ngi wéy di am doole, waaye gëstu yi dañuy puus efficacité ngir model yu am trillion-paramètre, fu denc ñaari valeur yu gëna bari ci poid bu nekk lu seer la. Xeetu mémoire yu woyof yu melni Adafactor, Adam 8-bit, ak optimisatër yu bees yu melni Lion (yiy jëfandikoo momentum bu lalu ci màndarga rek) ak Sophia dañu bëgga méngale kalite Adam ak mémoire bu néew wala convergence bu gëna gaaw. Xaarandil ay optimisatër yuñ defar ngir tàggat yaram bu woyof, ngir wéy di jëm kanam.
Doxal ci àdduna dëgg
Taggat xeetu làkk yu mag yu melni GPT ak Llama, yuy jëfandikoo AdamW ni optimisatër buñ miin.
Fine-tuning benn classifier nataal buñ tàggat bu njëkk (lu melni, ResNet) ci kaw benn done buñ personaalise bu am taxawaayu njàngum Adam buñ jagleel.
Taggat xeetu diffusion yi ci ginaaw defarkati nataal yu melni Diffusion bu dëgër.
Doxal Adam 8-bit ci bibliotek yu melni bitsandbytes ngir méngale staadu optimisatër bi ci mémoire GPU bu néew.
Risk yi ak balustrade yi
Optimize benn benchmark mën na nëbb ñakk kattan yu gëna yaatu ci sistem bi.
Njëg li ñuy fay ci infrastructure yi ak ci toppatoo dañuy faral di suufeel.
Bu sistem yi di gëna xawa jafee xam, jafe-jafe yi am ci wàllu kaaraange ak seetlu mën nañu gëna bari.
Roadmap ngir samp gi
Mandargal latency, kalite, ak njëg yi laata ngay jëfandikoo.
Benchmark ci biir sargal ak done yu dëggu.
Jumtukaay bi di saytu njuumte yi, derive bi ak njeextalu jëfandikukat bi.
Waajal rollback ak yooni tontu ci jafe-jafe yi laata ngay eskale.
Weyal di banneexu
Free newsletter
Get the daily AI briefing
Three verified AI stories every weekday morning, written in plain English. Free forever, no ads.
One email each weekday. Unsubscribe in one click. We never sell or share your address.
Test yourself
Take the Adam and Adaptive Optimizers quiz
Instant feedback on every answer, and a shareable certificate with a verifiable ID once you pass a course.
Support free AI education. AI Understanding is a 501(c)(3) nonprofit — no ads, no paywall, ever. Make a donation
Gis bi ci topp
ZeRO ak Optimisatër yu xaaj
Laaj yi ñuy faral di laaj
What is Adam and Adaptive Optimizers?
Adam mooy optimisatëru fasu liggéey bi ci ginaaw reso neuronal yu bees yi, di aju ci saasi tolluwaayu jàng bu wuute ci parametre bu nekk. Dafa am solo ndax dafay tax tàggat model yu xóot yi gëna gaaw te gëna néew finicky ni wàcci gradient bu leer.
Yan ñaari limu Adam di topp ci parametre bu nekk?
Adam dafay tëye ab moyenne buy jeex ci degrade yi (waxtu bu njëkk bi) ak degrade yu kaare yi (waxtu bu ñaareel bi) ci parametre bu nekk.
Lan moo tax Adam di jëfandikoo njuumte ci xaymaam ci saa si?
m ak v ñoom ñaar ñu ngi tàmbali ci nul, kon xayma yu njëkk yi dañu tuuti; xaaj ak (1 - beta^t) dafay saafara lii.
Lan mooy wuutale Adam ak AdamW?
AdamW dafay jëfandikoo poids decay directement ci poids yi moo gën ñu ko jaxase ak terme gradient biy méngoo, loolu mooy gëna yombal generalisation ci transformateur yi.
Ban cër la tur epsilon bu ndaw bi di def ci sàrtu yeesal bu Adam?
Epsilon (ci diggante 1e-8) dañu koy yokk ci lim bi suko defee parametre yi am moyenne gradient kaare bu jege nul du waral benn explosion.
Lu tax ñuy faral di wax ci Adam ni 'mën ànd ak jamono'?
Soo xaajalee ak rasin kare bu moyenne gradient kaare bu parametre bu nekk, Adam dafay eskale dayo jéego bu parametre bu nekk moo gën jëfandikoo benn valeur global.