Kordhinta Dalabka Labaad iyo Hababka Newton
Kordhinta dalabka labaad waxay isticmaashaa macluumaadka curvature (matrix Hessian ee derivatives labaad) si loo qaado tillaabooyin xariif ah xagga ugu yar, ma aha oo kaliya jiirada.
Dulmar
It can converge in dramatically fewer iterations than plain gradient descent, but the cost of computing curvature makes it tricky to scale.
quusid qoto dheer
Faracii hore wuxuu kaliya yaqaanaa jiirada meeshaada hadda, sidaa darteed waxay dooranaysaa cabbir go'an ama gacanta lagu hagaajiyay waxayna rajaynaysaa sida ugu wanaagsan. Habka Newton wuu sii socdaa: sidoo kale wuxuu eegayaa sida jiiradadu isu beddelayso (curvature), oo uu qabsaday Hessian, jaantuska dhammaan derajooyinka qaybeed ee labaad. Cusboonaysiinta ayaa ku dhufata Hessian-ga rogan ee isjiidhiyaha, kaas oo si toos ah u cabbiraya jiho kasta oo soo degaya ugu yaraan qiyaas afar-geesood ah. Maddiibad afargees ah oo qumman, habka Newton wuxuu ku gaaraa hoosta hal tallaabo. Qabashadu waa mid naxariis daran: moodeel leh cabbirro N wuxuu leeyahay N-by-N Hessian, markaa kaydinta iyo rogidda waxay ku kacaysaa qiyaas ahaan xusuusta N-squared iyo xisaabinta N-cubed. Shabakado balaayiin-beeg ah oo aan macquul ahayn, waana sababta ay xirfad-yaqaanadu u isticmaalaan qiyaaso ka jaban.
Aragtida Farsamada
Cusboonaysiinta xudunta u ah Newton waa x_new = x - H_inverse times gradient, halka H uu yahay Hessian-ka. Hababka Quasi-Newton sida BFGS iyo L-BFGS waxay iska ilaaliyaan xisaabinta H si toos ah iyada oo la dhisayo qiyaasid soconaysa oo ka soo horjeeda kala duwanaanshiyaha isdaba-joogga ah. L-BFGS waxa ay kaydisaa dhawrkii ugu dambeeyay ee gradient iyo vectors halkii ay ka ahaan lahayd matrix buuxa, iyaga oo ka jaraya xusuusta N-squared ilaa tiro yar oo N ah iyada oo la ilaalinayo inta badan xawaaraha isku xidhka.
Saamaynta Istiraatijiyadeed
Qiimaha iyo miisaaniyada
Go'aamada qaab-dhismeedku waxay horseedaan waxqabadka iyo kharashka hawlgalka sannadaha.
Go'aamo cad
Waxbarashada farsamada waxay ka caawisaa kooxaha inay doortaan xidhmo sax ah, ma aha oo kaliya kan ugu cusub.
Xakamaynta tayada
Doorashooyinka injineernimada ee wanaagsan waxay yareeyaan shilalka la isku halleyn karo ee wax soo saarka.
Mustaqbalka Kordhinta Dalabka Labaad iyo Hababka Newton
Shabakadaha neerfaha ee waaweyn, hababka dalabka labaad ee buuxa ayaa ah kuwo aan waxtar lahayn, laakiin qiyaasaha ayaa sii kordhaya. Hagaajiyayaasha sida K-FAC iyo Shampoo qiyaas qalooca isticmaalaya qaab-dhismeedka block-diagonal ama Kronecker-factored, iyo hababka cusub sida Sophia iyo Muon waxay isticmaalaan qiyaaso qaloocsan oo jaban si ay u dedejiyaan qaabka luuqada weyn ee horudhaca ah. Filo dadaalka sii socda si aad u qabato calaamada qalooca ee faa'iidada leh ee qiimaha u dhow-dalabka koowaad, yaraynta farqiga u dhexeeya Adam iyo tallaabooyinka Newton ee runta ah.
Dhaqangelinta Adduunka-dhabta ah
L-BFGS ku habboon dib-u-celinta saadka iyo moodooyinka kale ee convex ee scikit-bar, halkaas oo ay inta badan ku garaacdo farcanka cad ee xog-ururinta yar iyo midka dhexe
Isku-habaynta xidhmo ee dib-u-dhiska 3D iyo SLAM, halkaas oo Gauss-Newton iyo Levenberg-Marquardt ay sifeeyaan kamaradaha iyo meelaha dhibcaha
Tababarka shabakadaha neerfaha ee xog-ogaal u ah fiisigiska yar halkaas oo L-BFGS ay ku gaadho saxsanaanta uu Adam ku halgamayo inuu gaadho
Shaambo iyo K-FAC oo dardar gelinaya tabobar qoto dheer oo baaxad leh iyadoo la qiyaasayo qaab dhismeedka Hessian
Khatarta & Dariiqyada Ilaalada
Hagaajinta hal bartilmaameed waxay qarin kartaa daciifnimada nidaamka ballaaran.
Kaabayaasha dhaqaalaha iyo dayactirka inta badan waa la dhayalsadaa.
Nabadgelyada iyo daldaloolada u fiirsashada ayaa kori kara marka nidaamyadu noqdaan kuwo aad u adag.
Qorshe Hawleedka Dhaqangelinta
Qeex daahida, tayada, iyo bartilmaameedyada qiimaha ka hor inta aan la hirgelin.
Benchmark marka la eego culeyska dhabta ah iyo xaaladaha xogta.
La socodka qalabka khaladaadka, leexashada, iyo saamaynta isticmaalaha.
U diyaari dib-u-noqoshada iyo dariiqyada jawaab-celinta dhacdada ka hor inta aanad miisaan.
Sii wad Sahaminta
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Horumarinta Siyaasadda Qaraabada ee Kooxda
Su'aalaha soo noqnoqda
What is Second-Order Optimization and Newton Methods?
Kordhinta dalabka labaad waxay isticmaashaa macluumaadka curvature (matrix Hessian ee derivatives labaad) si loo qaado tillaabooyin xariif ah xagga ugu yar, ma aha oo kaliya jiirada. Waxay ku soo ururin kartaa soo noqnoqosho aad uga yar marka loo eego farcanka toosan, laakiin kharashka xisaabinta curvature ayaa ka dhigaya mid adag in la cabbiro.
Macluumaad noocee ah ayuu habka Newton u isticmaalo ee aanu faracyada cad ee farcanka ahi u isticmaalin?
Habka Newton wuxuu ku kordhiyaa qalooca ka imanaya Hessian-ka, isaga oo u oggolaanaya inuu dib u cabbiro jihooyinka oo uu qiyaaso ugu yaraan afar-geesoodka deegaanka.
Ujeeddo afar-geesood ah oo dhammaystiran, imisa tillaabo ayuu habka Newton u baahan yahay si uu u gaadho ugu yaraan?
Marka la eego quadratic-ka saxda ah, qaabka afargeesoodka maxalliga ah wuxuu la mid yahay shaqada runta ah, sidaas darteed hal tallaabo Newton ayaa si toos ah ugu boodaya ugu yaraan.
Waa maxay sababta buuxda ee habka Newton aan waxtar u lahayn shabakadaha neerfaha ee bilyan-parameter?
Marka la eego cabbirada N Hessian waxa uu leeyahay N-squared gelinta oo u rogayaa miisaan sida N-cubed, kaas oo aan la fulin karin balaayiin cabbirro ah.
Maxay sameeyaan hababka quasi-Newton sida BFGS si looga fogaado kharashka Hessian?
BFGS waxay si isdaba joog ah u cusboonaysiisaa qiyaasta Hessian-ga rogan iyadoo adeegsanaysa isbeddellada jaangooyooyinka u dhexeeya tallaabooyinka, iyadoo ka fogaanaysa xisaabinta tooska ah.
Sidee L-BFGS u dhimaysaa xusuusta marka la barbar dhigo BFGS?
'L' waxay u taagan tahay xusuusta xaddidan: L-BFGS waxay haysaa wax yar oo ka mid ah faleebo dhawaanahan, taasoo yaraynaysa kaydinta N-squared ilaa qiyaas yar oo N.