Uhlolojikelele
Ukwenziwa kwe-oda lesibili kusebenzisa ulwazi lwe-curvature (i-matrix ye-Hessian yokuphuma kokuphuma kwesibili) ukuthatha izinyathelo ezihlakaniphile eziya kokuncane, hhayi nje emthambekeni. Ingahlangana ngokuphindaphinda okumbalwa kunokwehla kwe-gradient engenalutho, kodwa izindleko zokugoba kwekhompyutha zikwenza kube nzima ukukala.
I-Second-Order Optimization and Newton Methods iyibhulokhi yokwakha yobuchwepheshe ethinta ikhwalithi yemodeli, izindleko zengqalasizinda, ukubambezeleka, nokuthembeka esikalini.
I-Deep Dive
Ukwehla kwe-Gradient kwazi kuphela umthambeka endaweni yakho yamanje, ngakho-ke ikhetha usayizi wesinyathelo esimisiwe noma oshuthwe ngesandla futhi inethemba lokungcono kakhulu. Indlela kaNewton iqhubekela phambili: iphinde ibheke ukuthi i-slope ishintsha kanjani (i-curvature), ethathwe yi-Hessian, i-matrix yazo zonke izingxenye zesibili zengxenye. Isibuyekezo siphindaphinda i-Hessian ephambene ngegradient, ekala ngokuzenzakalelayo isiqondiso ngasinye futhi ihlale eduze nobuncane bokuqagela kwasendaweni kwe-quadratic. Ukuze uthole indishi ene-quadratic ngokuphelele, indlela ka-Newton ifinyelela phansi ngesinyathelo esisodwa. Ukubamba kunonya: imodeli enamapharamitha angu-N ine-N-by-N Hessian, ngakho ukuyigcina nokuyiguqula kubiza cishe inkumbulo eyisikwele esingu-N kanye nekhompuyutha ye-N-cubed. Kumanethiwekhi wamapharamitha ayizigidi eziyizinkulungwane akunakwenzeka, yingakho odokotela besebenzisa ukulinganisa okushibhile.
I-Technical Insight
Isibuyekezo se-Newton esiyinhloko sithi x_new = x - H_inverse izikhathi zegradient, lapho u-H eyi-Hessian. Izindlela ze-Quasi-Newton ezifana ne-BFGS ne-L-BFGS zigwema ukusebenzisa ikhompuyutha H ngokuqondile ngokwakha ukulinganisa okusebenzayo kokuphambene nokuhluka okulandelanayo kwegradient. I-L-BFGS igcina kuphela ama-gradient ambalwa okugcina nezinyathelo esikhundleni se-matrix egcwele, isika inkumbulo isuka ku-N-squared iye kokuphindaphindeka okuncane kuka-N kuyilapho igcina iningi lokusheshisa kokuhlangana.
Ukuphumelela Kwe-Oda Yesibili kanye Nezindlela ZaseNewton
Ukwenziwa kwe-oda lesibili kusebenzisa ulwazi lwe-curvature (i-matrix ye-Hessian yokuphuma kokuphuma kwesibili) ukuthatha izinyathelo ezihlakaniphile eziya kokuncane, hhayi nje emthambekeni. Ingahlangana ngokuphindaphinda okumbalwa kunokwehla kwe-gradient engenalutho, kodwa izindleko zokugoba kwekhompyutha zikwenza kube nzima ukukala. I-Second-Order Optimization and Newton Methods iyibhulokhi yokwakha yobuchwepheshe ethinta ikhwalithi yemodeli, izindleko zengqalasizinda, ukubambezeleka, nokuthembeka esikalini. Ukuze wakhe ukuqonda okujulile, phatha ukuthuthukiswa kwe-Second-Order kanye ne-Newton Methods njengemodeli yokusebenza, hhayi isici esisodwa: chaza imiphumela efiselekayo, ucacise ukucabanga, futhi uhlukanise lokho uhlelo olungakwenza ngokwethembeka kulokho okusadinga ukwahlulela kochwepheshe.
Empeleni, amaqembu aqinile asebenzisa i-Second-Order Optimization and Newton Methods athuthukisa ukwakheka, idatha, nokukhetha kwengqalasizinda ngokumelene nokuthembeka nezindleko. 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.
Izinqumo zezakhiwo ziqhuba ukusebenza kanye nezindleko zokusebenza iminyaka. Ngesikhathi esifanayo, Ukuthuthukisa ibhentshimakhi eyodwa kungafihla ubuthakathaka obubanzi besistimu. 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
Izinqumo zezakhiwo ziqhuba ukusebenza kanye nezindleko zokusebenza iminyaka.
Izinqumo zezakhiwo ziqhuba ukusebenza kanye nezindleko zokusebenza iminyaka. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Imfundo yobuchwepheshe isiza amaqembu ukuthi akhethe isitaki esifanele, hhayi nje esisha.
Imfundo yobuchwepheshe isiza amaqembu ukuthi akhethe isitaki esifanele, hhayi nje esisha. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Izinketho ezingcono zobunjiniyela zinciphisa izehlakalo ezinokwethenjelwa ekukhiqizeni.
Izinketho ezingcono zobunjiniyela zinciphisa izehlakalo ezinokwethenjelwa ekukhiqizeni. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Ukuqaliswa Komhlaba Wangempela
I-L-BFGS ifaka ukuhlehla kwezinto kanye namanye amamodeli we-convex ku-scikit-learn, lapho ivamise ukwehlula ukwehla kwe-gradient kudathasethi emincane ukuya kwamaphakathi.
Ukulungiswa kwenqwaba ekwakhiweni kabusha kwe-3D kanye ne-SLAM, lapho u-Gauss-Newton no-Levenberg-Marquardt becwengisa ukuma kwekhamera kanye nezindawo zamaphoyinti.
Ukuqeqesha amanethiwekhi amancane e-neural anolwazi nge-physics lapho i-L-BFGS ifinyelela ukunemba u-Adamu azabalazela ukufinyelela khona.
I-Shampoo ne-K-FAC zisheshisa ukuqeqeshwa kokufunda okujulile ngokusondeza ukwakheka kwe-Hessian
Amaphethini Okusebenzisa
I-Second-Order Optimization kanye ne-Newton Izindlela ekusebenzeni
I-L-BFGS ifaneleka ukuhlehla kwezinto kanye namanye amamodeli e-convex ku-scikit-learn, lapho ivamise ukwehlula ukwehla kwe-gradient engenalutho kumadathasethi amancane kuya kwamaphakathi.
I-L-BFGS ifaneleka ukuhlehla kwezinto kanye namanye amamodeli we-convex ku-scikit-learn, lapho ivamise ukwehlula ukwehla kwe-gradient kumadathasethi amancane kuya kwamaphakathi Amaqembu ngokuvamile athola imiphumela engcono uma echaza imikhawulo yekhwalithi ngaphambili, agcine indlela yokukhuphuka yomuntu yamacala asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-Second-Order Optimization kanye ne-Newton Izindlela ekusebenzeni
Ukulungiswa kwenqwaba ekwakhiweni kabusha kwe-3D kanye ne-SLAM, lapho u-Gauss-Newton kanye ne-Levenberg-Marquardt belungisa ukuma kwekhamera kanye nezindawo zamaphoyinti.
Ukulungiswa kwenqwaba ekwakhiweni kabusha kwe-3D kanye ne-SLAM, lapho u-Gauss-Newton kanye ne-Levenberg-Marquardt bephucula ukuma kwekhamera kanye nezindawo zamaphoyinti Amaqembu ngokuvamile athola imiphumela engcono uma echaza imingcele yekhwalithi ngaphambili, agcina indlela yokukhuphuka komuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-Second-Order Optimization kanye ne-Newton Izindlela ekusebenzeni
Ukuqeqesha amanethiwekhi amancane e-neural anolwazi nge-physics lapho i-L-BFGS ifinyelela ukunemba u-Adamu azabalazela ukufinyelela khona.
Ukuqeqesha amanethiwekhi amancane e-neural anolwazi nge-physics lapho i-L-BFGS ifinyelela ukunemba okuthi u-Adamu azabalaza ukufinyelela Amathimba ngokuvamile athola imiphumela engcono lapho echaza ikhwalithi ephezulu ngaphambili, egcina indlela yokukhuphuka yomuntu yamacala abucayi, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-Second-Order Optimization kanye ne-Newton Izindlela ekusebenzeni
I-Shampoo ne-K-FAC zisheshisa ukuqeqeshwa kokufunda okujulile ngokulinganisa ukwakheka kwe-Hessian.
I-Shampoo ne-K-FAC zisheshisa ukuqeqeshwa kokufunda okujulile ngokulinganisa Ukwakheka Kwamaqembu e-Hessian ngokuvamile athola imiphumela engcono lapho echaza imingcele yekhwalithi ngaphambili, egcina indlela yokukhuphuka komuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Izingozi & Guardrails
Ukuthuthukisa ibhentshimakhi eyodwa kungafihla ubuthakathaka obubanzi besistimu.
Izindleko zengqalasizinda nezokulungisa zivame ukubukelwa phansi.
Izikhala zokuphepha nokubonakala zingakhula njengoba izinhlelo ziba nzima kakhulu.
Ukuqalisa Umhlahlandlela
Chaza ukubambezeleka, ikhwalithi, nezindleko ezihlosiwe ngaphambi kokuqaliswa.
Chaza ukubambezeleka, ikhwalithi, nezindleko ezihlosiwe ngaphambi kokuqaliswa. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Ibhentshimakhi ngaphansi komthwalo wangempela nezimo zedatha.
Ibhentshimakhi ngaphansi komthwalo wangempela nezimo zedatha. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Ukuqapha amathuluzi amaphutha, ukukhukhuleka, nomthelela wabasebenzisi.
Ukuqapha amathuluzi amaphutha, ukukhukhuleka, nomthelela wabasebenzisi. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Lungiselela izindlela zokuhlehlisa nezigameko ngaphambi kokukala.
Lungiselela izindlela zokuhlehlisa nezigameko ngaphambi kokukala. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.