InoteveraGaidhi rinotevera
RMSNorm uye Pre-Layer Normalization
Tekinoroji
Nhungamiro yehunyanzvi
Layer Normalization
Layer normalization inodzikamisa kudzidziswa nekudzoreredza ma activation mukati memumwe nemumwe muenzaniso kuitira kuti vave ne zero zvinoreva uye unit musiyano.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Pa peji ino2 min verenga
Pfupiso
It is a quiet but essential ingredient that makes deep transformers trainable.
Kudzika Kwakadzika
Yakaunzwa naBa, Kiros, uye Hinton muna 2016, layer normalization (LayerNorm) inogadzirisa dambudziko rekuti activation mukati metiweki yakadzika inogona kukukurwa kuenda kune zvikero zvakasiyana-siyana sezvo masaini achipfuura nematanho akawanda, achinonoka kana kukanganisa kudzidza. Kusiyana nebatch normalization, iyo inojairisa chimiro chega chega mumienzaniso mune mini-batch, LayerNorm inojairira pane ese maficha emuenzaniso mumwechete. Izvi zvinoita kuti ive yakazvimirira pahukuru hwebatch uye ishandiswe zvakaenzana pakudzidziswa uye kufungidzira, uye inoshanda seyakajairika nekusiyana-kureba kutevedzana, ndosaka yakave chiyero chevashanduri vane simba mamodheru emitauro yemazuva ano. Mushure mekuita zvakajairika, inoshandisa chikero chinodzidzisika (gamma) uye shift (beta) kuitira kuti network ikwanise kudzoreredza chero chinomiririra chainoda.
Kuitwa muNyika yeChokwadi
Kudzikamisa bhuroka yega yega inoshandura mitauro seGPT neBERT.
Kugonesa RMSNorm seyakareruka yakajairika sarudzo mukati meLlama-mhuri modhi.
Kugadzirisa dhata rekutevedzana-kureba mukutaura nemodhiyo yeshanduro apo masaizi ebhetch anosiyana.
Kubvumira kudzidziswa kwakavimbika ine batch size yeimwe, senge mune mamwe ekusimbisa ekudzidzira setups.
Ramangwana reLayer Normalization
Normalization iri kugadziridzwa kuitira kushanda zvakanaka pamwero. RMSNorm yakatsiva zvakanyanya LayerNorm mumhando mitsva yemitauro mikuru nekuti yakachipa uye inoshandawo saizvozvo, uye pre-norm yekuisa ikozvino ndiyo yakasarudzika kune yakadzika mastacks. Vatsvaguri vanoenderera mberi nekuongorora magadzirirwo-emahara ezvivakwa anoshandisa nekuchenjerera kutanga kana kuyera matipi panzvimbo, vachivavarira kucheka pamusoro uku vachichengeta kugadzikana kwekudzidziswa kunopihwa nejaivha.
Kuzvishandisa
Nei zvichikosha
- Mutengo uye bhajeti. Zvisarudzo zvezvivakwa zvinotyaira kuita uye mutengo wekushandisa kwemakore.
- Sarudzo dzakajeka. Dzidzo yehunyanzvi inobatsira zvikwata kusarudza murwi wakakodzera, kwete iwo mutsva chete.
- Kudzora kwemhando yepamusoro. Sarudzo dzeinjiniya dziri nani dzinoderedza zviitiko zvekuvimbika mukugadzira.
Chenjera
- Kugadzirisa imwe bhenji kunogona kuvanza yakafara system kushaya simba.
- Infrastructure uye mari yekugadzirisa inowanzotarisirwa pasi.
- Chengetedzo uye kucherechedzwa mapundu anogona kukura sezvo masisitimu anowedzera kuoma.
Matanho anotevera
- Tsanangura latency, mhando, uye mutengo zvinangwa usati waitwa.
- Benchmark pasi pechokwadi mutoro uye data mamiriro.
- Chishandiso chekutarisa zvikanganiso, kudonha, uye mushandisi maitiro.
- Gadzirira nzira dzekudzosera kumashure uye dzezviitiko usati wawedzera.
Ramba Uchiongorora
Tsamba yemahara
Tora iyo yemazuva ese AI pfupiso
Nhatu dzakasimbiswa dzeAI nyaya mangwanani ega ega pakati pevhiki, dzakanyorwa neChirungu chakajeka. Yemahara zvachose, hapana ads.
Imwe email zuva rega rega revhiki. Bvisa kunyoresa kamwe chete. Hatimbotengese kana kugovera kero yako.
Zviedze iwe pachako
Take the Layer Normalization quiz
Mhinduro yepakarepo pamhinduro yega yega, uye chitupa chinogovaniswa chine ID inobatika kana wapedza kosi.
Tsigira dzidzo yemahara yeAI. AI Understanding ndeye 501(c)(3) isingabatsiri - hapana ads, hapana paywall, zvachose. Ita chipo
Mibvunzo inowanzo bvunzwa
What is Layer Normalization?
Layer normalization inodzikamisa kudzidziswa nekudzoreredza ma activation mukati memumwe nemumwe muenzaniso kuitira kuti vave ne zero zvinoreva uye unit musiyano. Icho chinhu chinyararire asi chakakosha chinoita kuti ma transformer akadzika adzidziswe.
Mhiri kwechii chinonzi layerization inoverengera zvinoreva uye musiyano?
LayerNorm inojairira pamusoro pehukuru hwesample yemunhu mumwe, zvichiita kuti ive yakazvimirira kubva kune mimwe mienzaniso mubatch.
Sei layerization ichifarirwa pane batch normalization muma transformer?
Nekuti nhamba dzayo dzinobva kumuenzaniso mumwechete, LayerNorm inozvibata zvisingaite zvisinei nehukuru hwebatch uye inokodzera kusiyana-kureba kutevedzana kwemavara.
Chii chinodzidziswa paramita gamma uye beta regai LayerNorm iite?
Mushure mekuita zvakajairika kusvika ku zero zvinoreva uye unit musiyano, zvikero zvegamma uye beta zvinoshandura mhedzisiro kuitira kuti modhi isamanikidzwe mukugovera kwakamisikidzwa.
RMSNorm inosiyana sei neyakajairwa LayerNorm?
RMSNorm inosiya nhanho-yepakati-yepakati uye zvikero nemudzi-zvinoreva-square yezviitwa, izvo zvakachipa uye zvinoshandiswa mumhando dzakaita seLlama.
Nderipi dambudziko iro layerization rinonyanya kubatsira kugadzirisa mune akadzika network?
Sezvo zviratidzo zvichipfuura nepakati pezvikamu zvakawanda chiyero chavo chinogona kuputika kana kuderera; normalization inochengeta ma activation muyakagadzika renji kuitira kuti ma gradients aite zvakanaka.
Ramba uchidzidza
Mazano anoenderana
Mamwe madhairekitori akasarudzirwa nyaya iyi