Uhlolojikelele
I-Neural Architecture Search (NAS) yenza ngokuzenzakalelayo ukuklama kwezakhiwo zenethiwekhi ye-neural - ivumela ama-algorithms, hhayi abantu, anqume ukuthi zingaki izendlalelo, yiziphi izinto ezisebenzayo, nokuthi zixhuma kanjani. Ishintsha idizayini yemodeli ibe inkinga yosesho, ithola izakhiwo ezingaqhudelana noma zehlule ezenziwe ngezandla.
I-Neural Architecture Search ihlezi kukhithi yamathuluzi eyinhloko ye-AI. Uma uyiqonda, ezinye izihloko ze-AI ziba lula ukuzihlola nokuqhathanisa.
I-Deep Dive
Ukuklama amanethiwekhi e-neural ngesandla kuhamba kancane futhi kuncike ekwazisweni kochwepheshe. I-NAS ithatha indawo yalokho ngokusesha endaweni echaziwe yezakhiwo ezingaba khona, eziqondiswa isu eliphakamisa amakhandidethi kanye nendlela yokulinganisa ukuthi ngalinye lihle kangakanani. I-NAS yakudala yasebenzisa ukufunda okuqinisiwe noma ama-algorithms wokuziphendukela kwemvelo, iqeqesha izinkulungwane zamanethiwekhi amakhandidethi - ebiza izinkulungwane zezinsuku ze-GPU. Ukuphumelela bekwenza ukusesha kungabizi kakhulu: ukwabelana ngesisindo ('i-supernet' equkethe wonke amakhandidethi) nezindlela ezihlukanisekayo ezifana ne-DARTS, ekhulula ukukhetha okuhlukile kube okuqhubekayo ukuze ukwehla kwe-gradient kuthuthukise izakhiwo nezisindo ndawonye. I-NAS ikhiqize amamodeli asebenza kahle njenge-EfficientNet kanye namanethiwekhi amaningana enziwe kahle eselula manje asetshenziswa ekukhiqizeni.
I-Technical Insight
I-NAS inezingxenye ezintathu: indawo yokusesha (amabhulokhi wokwakha nokuthi angaxhuma kanjani), isu lokusesha (ukufunda ukuqinisa, ukuziphendukela kwemvelo, ukusesha okungahleliwe, noma okusekelwe ku-gradient), kanye nendlela yokulinganisa ukusebenza. Ukuqeqesha umuntu ngamunye ukuze ahlangane kubiza ngendlela engafanele, ngakho i-NAS isebenzisa izinqamuleli: ukwabelana ngesisindo ku-supernet, ama-proxies ane-low-fidelity (izinkathi ezimbalwa, idatha encane), nezibikezelo ezifundiwe. I-DARTS yenza ukukhetha okuhlukile kokuthi 'ikuphi ukusebenza okuza lapha' okuqhubekayo ngezingxube ezinesisindo esilinganiselwe, ithuthukisa ngama-gradients, bese ihlukanisa umphumela ube isakhiwo sokugcina.
I-Mastering Neural Architecture Search
I-Neural Architecture Search (NAS) yenza ngokuzenzakalelayo ukuklama kwezakhiwo zenethiwekhi ye-neural - ivumela ama-algorithms, hhayi abantu, anqume ukuthi zingaki izendlalelo, yiziphi izinto ezisebenzayo, nokuthi zixhuma kanjani. Ishintsha idizayini yemodeli ibe inkinga yosesho, ithola izakhiwo ezingaqhudelana noma zehlule ezenziwe ngezandla. I-Neural Architecture Search ihlezi kukhithi yamathuluzi eyinhloko ye-AI. Uma uyiqonda, ezinye izihloko ze-AI ziba lula ukuzihlola nokuqhathanisa. Ukuze wakhe ukuqonda okujulile, phatha i-Neural Architecture Search njengemodeli yokusebenza, hhayi isici esisodwa: chaza imiphumela oyifunayo, cacisa ukucabanga, futhi uhlukanise lokho isistimu engakwenza ngokwethembeka kulokho okusadinga ukwahlulela kochwepheshe.
Empeleni, amaqembu aqinile asebenzisa i-Neural Architecture Search akha amamodeli aqinile emicabango kuqala, bese ebeka imephu lawo mamodeli emikhawulweni yokukhiqiza yangempela. Babhala imibandela yempumelelo ecacile, ukuhlola okuqhathaniswa nedatha engokoqobo nokugeleza komsebenzi, futhi baphindaphinde ngokusekelwe kumaphethini okuhluleka aqashiwe esikhundleni sokuwina kwebhentshimakhi yesikhathi esisodwa. Yilapho ukuqonda kwethiyori kuguquka kube amandla ahlala njalo kuwo wonke umkhiqizo, inqubomgomo, kanye nokusebenza.
Kukusiza ukuthi uhlukanise izimangalo ezicacile zobuchwepheshe kusukela olimini lokumaketha. Ngesikhathi esifanayo, amaqembu ahlukene angasebenzisa igama elifanayo ngokuhlukile, ngakho chaza ububanzi kusenesikhathi. Indlela eqine kakhulu iwukuhlanganisa isivinini sokuhlola nesiyalo sokuphatha: qhuba abashayeli bezindiza, bamba ubufakazi, ushicilele amalogi ezinqumo, futhi ubuyekeze izivikelo ngokuqhubekayo njengoba imodeli yokuziphatha, okulindelwe ngabasebenzisi, kanye nezimfuneko zokulawula zishintsha.
I-Strategic Impact
Kukusiza ukuthi uhlukanise izimangalo ezicacile zobuchwepheshe kusukela olimini lokumaketha.
Kukusiza ukuthi uhlukanise izimangalo ezicacile zobuchwepheshe kusukela olimini lokumaketha. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Ungabuza imibuzo yokusebenzisa kangcono ngaphambi kokusebenzisa imali noma isikhathi.
Ungabuza imibuzo yokusebenzisa kangcono ngaphambi kokusebenzisa imali noma isikhathi. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Amaqembu anokuqonda okwabiwe enza izinqumo ezingcono zomkhiqizo, inqubomgomo, nokufunda.
Amaqembu anokuqonda okwabiwe enza izinqumo ezingcono zomkhiqizo, inqubomgomo, nokufunda. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Ukuqaliswa Komhlaba Wangempela
Umndeni wakwa-EfficientNet we-Google, owakhiwe ngesilinganiso esihlanganisiwe wawuholwa ukusesha okuzenzakalelayo kokunemba okuqinile kwe-FLOP ngayinye.
Amamodeli ombono weselula (afana ne-MnasNet) aseshe ngokubambezeleka ocingweni lwangempela ku-loop ngesivinini esikudivayisi.
I-NAS eqaphela izingxenyekazi zekhompyutha evumelanisa inethiwekhi nenkumbulo yesisheshisi esithile futhi ibale imikhawulo.
Izingxenyekazi ze-AutoML ezivumela abangebona ochwepheshe bathole imodeli yangokwezifiso yokuncintisana ngokusesha izakhiwo ngokuzenzakalela.
Amaphethini Okusebenzisa
I-Neural Architecture Search in practice
Umndeni wakwa-EfficientNet we-Google, owakhiwe ngesilinganiso esihlanganisiwe wawuholwa ukusesha okuzenzakalelayo kokunemba okuqinile kwe-FLOP ngayinye.
Umndeni wakwa-EfficientNet we-Google, izakhiwo zawo ezilinganisiwe zaziholwa ukusesha okuzenzakalelayo kokunemba okuqinile kwe-FLOP Teams ngokuvamile athola imiphumela engcono uma echaza izinga eliphezulu ngaphambili, agcina indlela yokukhuphuka kwabantu yamakesi aphambili, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-Neural Architecture Search in practice
Amamodeli ombono weselula (afana ne-MnasNet) aseshe ngokubambezeleka ocingweni lwangempela ku-loop ngesivinini esikudivayisi.
Amamodeli ombono weselula (afana ne-MnasNet) aseshwa ngokubambezeleka ocingweni lwangempela ku-loop yesivinini esikudivayisi Amaqembu ngokuvamile athola imiphumela engcono lapho echaza ikhwalithi ephezulu ngaphambili, egcina indlela yokukhuphuka yomuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-Neural Architecture Search in practice
I-NAS eqaphela izingxenyekazi zekhompyutha evumelanisa inethiwekhi nenkumbulo yesisheshisi esithile futhi ibale imikhawulo.
I-NAS eyazi ngezingxenyekazi zekhompyutha evumelanisa inethiwekhi nenkumbulo yesisheshisi esithile kanye nemikhawulo yokubala Amaqembu ngokuvamile athola imiphumela engcono lapho echaza izinga eliphezulu ngaphambili, egcina indlela yokukhuphuka yomuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-Neural Architecture Search in practice
Izingxenyekazi ze-AutoML ezivumela abangebona ochwepheshe bathole imodeli yangokwezifiso yokuncintisana ngokusesha izakhiwo ngokuzenzakalela.
Izingxenyekazi ze-AutoML ezivumela abangebona ochwepheshe bathole imodeli yangokwezifiso yokuncintisana ngokusesha izakhiwo ngokuzenzakalela Amaqembu ngokuvamile athola imiphumela engcono uma echaza izinga eliphezulu ngaphambili, egcina indlela yokukhuphuka kwabantu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Izingozi & Guardrails
Amaqembu ahlukene angasebenzisa igama elifanayo ngokuhlukile, ngakho chaza ububanzi kusenesikhathi.
Amabhentshimakhi angabukeka eqinile kuyilapho ukusebenza komhlaba wangempela kungalingani.
Ukuziba ikhwalithi yedatha nezinhlelo zokuhlaziya kuvame ukudala imiphumela entekenteke.
Ukuqalisa Umhlahlandlela
Qala ngencazelo yolimi olulula yomphumela oyidingayo.
Qala ngencazelo yolimi olulula yomphumela oyidingayo. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Khetha imethrikhi eyodwa yempumelelo nesimo esisodwa sokuhluleka ngaphambi kokuhlolwa.
Khetha imethrikhi eyodwa yempumelelo nesimo esisodwa sokuhluleka ngaphambi kokuhlolwa. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Qalisa umshayeli omncane onedatha emele, hhayi isethi yedemo ephucuziwe.
Qalisa umshayeli omncane onedatha emele, hhayi isethi yedemo ephucuziwe. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Idokhumenti lapho i-Neural Architecture Search isiza nalapho izindlela ezilula zingcono.
Idokhumenti lapho i-Neural Architecture Search isiza nalapho izindlela ezilula zingcono. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.