Zviri Mukati[Viga][Ratidza]
Kudzidza Kwakadzika (DL), kana kuteedzera kwehuropi hwehuropi hwemunhu, yaingova pfungwa yekufungidzira isingasviki makumi maviri emakore apfuura.
Nekukurumidza kusvika nhasi, uye iri kushandiswa kugadzirisa matambudziko epasirese sekushandura maodhiyo ekutaura-kune-mavara zvinyorwa uye mune akasiyana ekuona komputa kuita.
Iyo Attention process kana Attention Model ndiyo yakakosha nzira inosimbisa izvi maapplication.
Kuongororwa kwechimbichimbi kunoratidza izvozvo Machine Learning (ML), inova yekuwedzera yeArtificial Intelligence, chikamu cheKudzidza Kwakadzika.
Paunenge uchibata nenyaya dzine chekuita neKugadzirisa Mutauro Wechisikigo (NLP), sekupfupisa, kunzwisisa, uye kupedzisa nyaya, Kudzidza Kwakadzika Neural Networks shandisa nzira yekutarisisa.
Mune ino positi, isu tinofanirwa kunzwisisa kuti ndeipi nzira yekutarisisa, mashandiro anoita maitiro ekutarisa muDL uye zvimwe zvakakosha.
Chii chinonzi Attention Mechanism mukudzidza kwakadzama?
Iyo yekutarisisa nzira mukudzidza kwakadzama inzira inoshandiswa kuvandudza mashandiro eneural network nekubvumira modhi kuti itarise pane yakakosha data rekuisa uchigadzira fungidziro.
Izvi zvinoitwa nekuyeresa data rekuisa kuitira kuti modhi inokoshese zvimwe zvekushandisa pane zvimwe. Nekuda kweizvozvo, iyo modhi inogona kuburitsa yakanyatso kufungidzira nekutarisa chete akakosha ekuisa akasiyana.
Iyo yekutarisisa nzira inowanzo shandiswa mumutauro wechisikigo mabasa ekugadzirisa senge shandurudzo yemuchina, apo modhi inofanirwa kutarisisa zvikamu zvakasiyana-siyana zvemutsara wekuisa kuti unyatsonzwisisa zvazvinoreva uye kupa shanduro yakakodzera.
Inogonawo kushandiswa mune mamwe mabasa kudzidza zvakadzika maapplication, akadai sekucherechedzwa kwemufananidzo, uko modhi inogona kudzidza kutarisisa kune zvimwe zvinhu kana maitiro ari pamufananidzo kuti abudise fungidziro yechokwadi.
Iyo Attention Mechanism inoshanda sei?
Iyo yekutarisisa nzira inyanzvi inoshandiswa mukati mienzaniso yekudzidza yakadzama kuyera maitiro ekuisa, kubvumira modhi kuti itarise pane zvakanyanya kukosha zvikamu zvekuisa paunenge uchigadzira. chimiro chepakutanga chechimiro chepakutanga chechimiro chepakutanga.
Heunoi mufananidzo wemashandiro anoita hurongwa hwekutarisisa: Fungidzira kuti uri kugadzira modhi yekushandura yemuchina inoshandura mitsara yeChirungu kuenda kuFrench. Iyo modhi inotora chinyorwa cheChirungu sekuisa uye inoburitsa shanduro yechiFrench.
Iyo modhi inoita izvi nekutanga kukodha mutsara wekuisa munhevedzano yeakamisikidzwa-kureba mavheta (anonziwo "maficha" kana "embeddings"). Modhi yacho inobva yashandisa mavekita aya kuvaka dudziro yechiFrench vachishandisa decoder inoburitsa akatevedzana emazwi echiFrench.
Iyo yekutarisisa nzira inoita kuti modhi itarise pazvinhu chaizvo zvemutsara wekuisa izvo zvakakosha pakuburitsa izwi razvino mukutevedzana kwekubuda padanho rega rega rekuita decoding.
Semuenzaniso, decoder inogona kutarisa pamazwi mashoma ekutanga emutsara wechirungu kubatsira kusarudza dudziro yakakodzera kana ichiedza kugadzira izwi rekutanga rechiFrench.
Decoder icharamba ichiteerera zvikamu zvakasiyana-siyana zvechirevo chechirungu uku ichigadzira zvikamu zvasara zveshanduro yechiFrench kuti ibatsire kuwana shanduro yechokwadi inobvira.
Mamodheru ekudzidza akadzama ane nzira dzekutarisisa anogona kutarisisa pane izvo zvakakosha zvemukati paunenge uchigadzirisa, izvo zvinogona kubatsira modhi mukugadzira fungidziro dzakanyanya kunaka.
Inzira ine simba yakashandiswa zvakanyanya mumashandisirwo akasiyana siyana, kusanganisira mapikicha emifananidzo, kuziva kutaura, uye kushandura nemuchina.
Mhando dzakasiyana dzeAttention Mechanism
Maitiro ekutarisisa anosiyana zvichienderana negadziriro umo imwe nzira yekutarisisa kana modhi inoshandiswa. Nzvimbo kana zvikamu zvakakodzera zvekutevedzana kwekupinza iyo modhi inotarisa uye inotarisisa ndedzimwe pfungwa dzekusiyanisa.
Aya anotevera ndiwo mashoma emhando yekutarisisa michina:
Generalized Attention
Generalized Attention imhando ye neural network dhizaini inobvumira modhi kusarudza kutarisa munzvimbo dzakasiyana dzekuisa kwayo, sezvinoita vanhu nezvinhu zvakasiyana munzvimbo dzavanogara.
Izvi zvinogona kubatsira nekuzivikanwa kwemifananidzo, kugadzira mutauro chaiwo, uye kushandura nemuchina, pakati pezvimwe zvinhu. Iyo network mune yakajairika yekutarisisa modhi inodzidza kungozvisarudzira kuti ndeapi zvikamu zveiyo zvinonyanya kukosha kune rimwe basa rakapihwa uye inonangidzira zviwanikwa zvekombuta pane izvo zvikamu.
Izvi zvinogona kuvandudza kushanda kwemodhi uye kuita kuti iite zvirinani pamabasa akasiyana.
Self Attention
Kuzvitarisa pane dzimwe nguva kunonzi intra-attention, imhando yekutarisisa nzira inoshandiswa mune neural network modhi. Inogonesa modhi kuti itarise pane zvakasiyana-siyana zvekuisa kwayo pasina kudiwa kwekutarisa kana kunze kwekuisa.
Pamabasa akaita semagadzirirwo emutauro wechisikigo, apo modhi inofanirwa kukwanisa kunzwisisa hukama huri pakati pemazwi akasiyana mumutsara kuti ubudise mibairo chaiyo, izvi zvinogona kubatsira.
Mukuzvitarisa wega, modhi yacho inotara kuti peya yega yega yemavheji ekuisa yakafanana sei kune imwe uye yobva yayera mipiro yevheta yega yega yekuisa kune zvinobuda zvichibva pane izvi zvibodzwa zvakafanana.
Izvi zvinogonesa iyo modhi kuti itarise otomatiki pazvikamu zvekuisa izvo zvinonyanya kukosha pasina kudiwa kwekutarisisa kwekunze.
Multi-head Attention
Multi-head kutarisisa imhando yekutarisisa nzira inoshandiswa mune mamwe neural network modhi. Kushandisa "misoro" yakawanda kana maitiro ekutarisa, kunogonesa modhi kuti itarise pane akati wandei eruzivo rwayo kamwechete.
Izvi zvinobatsira kumabasa akaita sechisikigo kugadzirisa mutauro apo modhi inofanirwa kunzwisisa hukama pakati pemazwi akasiyana mumutsara.
Iyo yakawanda-yemusoro yekutarisisa modhi inoshandura iyo yekuisa munzvimbo dzakawanda dzakasiyana dzekumiririra isati yashandisa yakaparadzana yekutarisisa kune yega yega nzvimbo yekumiririra.
Izvo zvinobuda zveimwe neimwe nzira yekutarisisa zvinobva zvabatanidzwa, zvichibvumira iyo modhi kugadzirisa ruzivo kubva kune akawanda maonero. Izvi zvinogona kukwidziridza mashandiro pamabasa akasiyana siyana ukuwo zvichiita kuti modhi iwedzere kusimba uye inoshanda.
Attention Mechanism inoshandiswa sei muhupenyu chaihwo?
Matanho ekutarisisa anoshandiswa mukusiyana-siyana kwezvikumbiro zvepasirese, kusanganisira kugadzirwa kwemutauro wechisikigo, kuzivikanwa kwemifananidzo, uye kududzira muchina.
Matanho ekutarisisa mukugadzirisa mutauro wechisikigo anobvumira modhi kuti itarise pamazwi akasiyana mumutsara uye kubata hukama hwavo. Izvi zvinogona kubatsira pamabasa akaita sekushandura mutauro, kupfupisa zvinyorwa, uye manzwiro ongororo.
Maitiro ekutarisisa mukuzivikanwa kwemufananidzo anobvumira modhi kuti itarise pazvinhu zvakasiyana mumufananidzo uye kubata hukama hwavo. Izvi zvinogona kubatsira nemabasa senge kucherechedzwa kwechinhu uye mifananidzo yemifananidzo.
Nzira dzekutarisa mukushandura nemuchina dzinobvumira modhi kuti itarise pazvikamu zvakasiyana zvemutsara wekuisa uye kugadzira mutsara wakashandurwa unonyatsoenderana nechirevo chepakutanga.
Pakazere, nzira dzekutarisisa dzinogona kuwedzera neural network modhi kuita pane akasiyana mabasa uye chinhu chakakosha cheakawanda-chaiwo epasirese application.
Zvakanakira Attention Mechanism
Kune akasiyana mabhenefiti ekushandisa nzira dzekutarisisa mune neural network modhi. Imwe yemabhenefiti akakosha ndeyekuti ivo vanogona kukwidziridza kuita kwemuenzaniso pamabasa akasiyana siyana.
Maitiro ekutarisisa anogonesa modhi kuti isarudze kutarisa pazvikamu zvakasiyana zvekuisa, ichiibatsira kuti inzwisise zvirinani hukama pakati pezvinhu zvakasiyana zvekuisa uye kugadzira kufanotaura kwakanyatso.
Izvi zvinonyanya kubatsira kumashandisirwo akaita semutauro wechisikigo kugadzirisa uye chiratidziro chemufananidzo, apo modhi inofanirwa kunzwisisa kubatana kuri pakati pemazwi akasiyana kana zvinhu mukuisa.
Imwe bhenefiti yemaitiro ekutarisisa ndeyekuti ivo vanogona kuvandudza kushanda kweiyo modhi. Nzira dzekucherekedza dzinogona kudzikisira huwandu hwemakomputa hunofanirwa kuitwa nemodhiyo nekuibvumira kuti itarise pane zvakakosha mabhiti ekuisa, ichiita kuti ishande uye nekukurumidza kumhanya.
Izvi zvinonyanya kubatsira kumabasa apo modhi inofanirwa kugadzirisa huwandu hwakakosha hwedata rekuisa, sekushandura nemuchina kana kuzivikanwa kwemufananidzo.
Chekupedzisira, maitiro ekutarisisa anogona kuvandudza kududzira uye kunzwisisa kweneural network modhi.
Maitiro ekutarisisa, ayo anoita kuti modhi itarise munzvimbo dzakasiyana dzekuisa, inogona kupa ruzivo rwekuti modhi inofanotaura sei, iyo inogona kubatsira pakunzwisisa maitiro eiyo modhi nekuvandudza mashandiro ayo.
Pakazere, nzira dzekutarisisa dzinogona kuunza akati wandei mabhenefiti uye chinhu chakakosha cheakawanda anoshanda neural network modhi.
Kuganhurirwa kweAttention Mechanism
Kunyangwe maitiro ekutarisisa anogona kubatsira zvakanyanya, mashandisiro azvo mumaneural network modhi ane akati wandei. Chimwe chezvipingamupinyi zvayo zvikuru ndezvekuti vanogona kunge vakaoma kudzidzisa.
Maitiro ekutarisisa anowanzoda modhi kuti idzidze kuwirirana pakati pezvikamu zvakasiyana-siyana zvekuisa, izvo zvinogona kunetsa kuti modhi idzidze.
Izvi zvinogona kuita kuti kudzidzisa kutarisisa-kwakavakirwa modhi kunetsa uye kungangoda kushandiswa kwemaitiro akaomarara ekugadzirisa uye mamwe mazano.
Chimwe chinokanganisa maitiro ekutarisa ndeye computational kuomarara. Nekuti nzira dzekutarisisa dzinoda iyo modhi kuti iverenge kufanana pakati pezvinhu zvakasiyana zvekuisa, dzinogona kuve dzakasimba pamakomputa, kunyanya kune makuru ekuisa.
Attention-based modhi inogona kunge isingashande uye inononoka kushanda pane mamwe marudzi emamodheru semhedzisiro, izvo zvingave dhizaini mune mamwe maapplication.
Pakupedzisira, nzira dzekutarisa dzinogona kuve dzakaoma kubata uye kunzwisisa. Zvingave zvakaoma kunzwisisa kuti iyo yekutarisisa-yakavakirwa modhi inoita sei fungidziro sezvo ichisanganisira kupindirana kwakaoma pakati pezvikamu zvakasiyana zvekuisa.
Izvi zvinogona kuita kuti kugadzirisa uye kugadzirisa mashandiro emhando idzi kuve kwakaoma, izvo zvinogona kuve zvisina kunaka mune mamwe maapplication.
Pakazere, nepo nzira dzekutarisa dzichipa akawanda mabhenefiti, ivo zvakare vane mimwe miganho inofanirwa kugadziriswa usati waishandisa mune chaiyo application.
mhedziso
Mukupedzisa, maitiro ekutarisisa inzira ine simba yekusimudzira neural network modhi kuita.
Ivo vanopa iyo modhi kugona kwekusarudza kutarisa pane akasiyana ekuisa zvikamu, izvo zvinogona kubatsira modhi kuti ubate hukama pakati pezvakaiswa zvinoumba zvikamu uye kuburitsa fungidziro inonyatso.
Zvishandiso zvakawanda, zvinosanganisira kushandura nemuchina, kucherechedzwa kwemifananidzo, uye kugadzirisa mutauro wechisikigo, zvinotsamira pamaitiro ekutarisa.
Nekudaro, pane zvimwe zvinogumira kumatanho ekutarisisa, sekuoma kwekudzidziswa, kuwanda kwekombuta, uye kuoma kwekududzira.
Kana uchifunga nezve kushandisa nzira dzekutarisisa mune imwe application, zvirambidzo izvi zvinofanirwa kutariswa.
Pakazere, nzira dzekutarisisa chinhu chakakosha chenzvimbo yekudzidza yakadzika, ine mukana wekuwedzera kushanda kwemhando dzakawanda dzakasiyana dzeneural network modhi.
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