OkulandelayoUmhlahlandlela olandelayo
I-Softmax Regression for Multiclass Classification
Ubuchwepheshe
UMHLAHLANDLELA Wobuchwepheshe
I-YAMNet iyinethiwekhi ye-neural eqeqeshwe kusengaphambili yokuhlukanisa imicimbi yomsindo kusetshenziswa isilulumagama sekilasi le-AudioSet.
Ikhiqiza amaphuzu ekilasi elizimele kanye nokushumekiwe okungasekela ukusesha okuzwakalayo noma ukudlulisa ukufunda, kodwa izibikezelo zayo zibonisa intela yokuqeqeshwa futhi akulona uhlelo lokuqonda umsindo jikelele.
I-YAMNet iyisihlukanisi somcimbi womsindo esiqeqeshelwe kusengaphambili esikhishwe kunqolobane yamamodeli e-TensorFlow. Imodeli yayo ebhaliwe ibikezela amakilasi omcimbi we-AudioSet angu-521 futhi isebenzisa i-MobileNetV1-isitayela se-MobileNetV1 esihlukaniseka ngokujula kwezakhiwo. Ingakwazi ukubuyisela amaphuzu kumakilasi ozimele abalalelwayo abalandelanayo, kanye nokushumeka okumaphakathi. Le miphumela ikwenza kube usizo ekuhloleni umsindo wendawo kanye nanjengesethulo sokuqala somsebenzi omncane ongezansi womfula. Amaphuzu ekilasi aboshelwe ku-Ontology ye-AudioSet, ehlanganisa izigaba ezifana nenkulumo, umculo, izilwane, nezehlakalo zemvelo. Uhlu lwekilasi lulolonga lokho imodeli engakuveza: uma iphrojekthi idinga umehluko ongekho noma obanzi ku-taxonomy, imodeli ayikwazi ngokuthembekile ukunikeza leyo lebula ngqo ngenxa nje yokuthi kukhona isigaba esifanayo. Hlola amamephu nokungaqondakali esikhundleni sokuphatha amagama okukhiphayo njengeqiniso eliqondene nephrojekthi. Ukugeleza komsebenzi okuvamile kulayisha umsindo, kuwuguqulele esilinganisweni esilindelekile sesampula nefomethi yesiteshi, kuqalise imodeli, futhi kuhlanganise izikolo zozimele uma kudingeka umphumela weleveli yesiqeshana. Ukusampula kabusha kufanele kube ukusampula kabusha kwangempela, kungashintshi inkambu yemethadatha. Ukuguqulwa kwe-Mono, ubude bokunqampuna, nokuhlanganisa amaphuzu kuthinta okukhiphayo. Umcimbi owodwa onomsindo ungase ulawule isilinganiso sesiqeshana, kuyilapho umkhawulo ungagcizelela ngokweqile iphozithivu emfushane engamanga. Ngokufunda kokudlulisa, ukushumeka kungaphakela isigaba esiqeqeshwe ngezibonelo ezilebulwe ngomsebenzi oqondiwe. Ngaphandle kwalokho, ukulungisa kahle kubuyekeza ezinye izisindo zemodeli. Inketho efanele incike kusayizi wedatha nekhwalithi yelebula. Hlukanisa okurekhodiwe ngomthombo noma ngeseshini ngaphambi kokukhipha okuhlukile okuthuthukisiwe, futhi uhlole ekurekhodweni okuzimele okuzimele. Qaphela amaphutha aqondene nekilasi kanye nokulinganisa uma amaphuzu eshayela ama-threshold. I-YAMNet iyimodeli, hhayi idathasethi ekhethiwe noma ukuqinisekiswa kokukhiqiza. Ukuqeqeshwa kwayo kwangaphambili okubanzi kungase kungamele izinto eziyisipesheli, izindawo zokuqopha, noma imicimbi eyivelakancane. Hlola ukulayisensa, ukuvela kwemodeli, ukusebenzisana kwedivayisi, nokusebenza ngenani labantu elihlosiwe. Ukubuyekezwa komuntu kungase kudingeke lapho amalebula omcimbi eqala izinqumo.
Izinqumo zezakhiwo ziqhuba ukusebenza kanye nezindleko zokusebenza iminyaka.
Imfundo yobuchwepheshe isiza amaqembu ukuthi akhethe isitaki esifanele, hhayi nje esisha.
Izinketho ezingcono zobunjiniyela zinciphisa izehlakalo ezinokwethenjelwa ekukhiqizeni.
Izifaki khodi zomsindo eziqeqeshwe kusengaphambili zizoqhubeka nokusekela ukufunda kokudlulisa okungasindi kanye nokutholwa kwemicimbi ekudivayisi. Amamodeli amasha angase ahlanganise ama-taxonomies abanzi noma umongo omude, kuyilapho amanethiwekhi ahlangene njenge-YAMNet ehlala ewusizo lapho imikhawulo yensiza ibalulekile. Amaqembu kufanele aqhathanise lezi zinketho kuziqeshana ezifaniswa nesizinda futhi alandelele amashifu kumakrofoni nasezindaweni ezizungezile. Izikolo zamamodeli zisazodinga ukuqoshwa kwemephu ngokucophelela, ukulinganisa, nokubuyekezwa komuntu uma izinqumo zincike emisindweni eyivelakancane. Amaqembu kufanele agcine izibonelo zokuqinisekisa eziqondene nesizinda njengoba amadivayisi nezimo ze-acoustic zishintsha. Phinda ukuhlola ngemva koshintsho lwenzwa.
I-prototype yokuqapha umsindo isebenza i-YAMNet ekurekhodweni okufushane kwemvelo futhi ihlanganisa izikolo zeleveli yozimele ibe isifinyezo sesiqeshana.
Unjiniyela usebenzisa okushumekiwe kwe-YAMNet njengokufakwa kusihlukanisi esincane sesethi emincane yezigaba zomsindo wasendaweni.
Umhlaziyi ubeka amalebula amalebula ephrojekthi okuqondiswe kuwo amakilasi e-AudioSet futhi aqophe izigaba ezingenakho okulinganayo okuqondile.
Ithimba leselula likala ukubambezeleka kwemodeli futhi lihlola ukusampula kabusha nokuguqulwa kwesiteshi kumadivayisi ahlosiwe.
Ukuthuthukisa ibhentshimakhi eyodwa kungafihla ubuthakathaka obubanzi besistimu.
Izindleko zengqalasizinda nezokulungisa zivame ukubukelwa phansi.
Izikhala zokuphepha nokubonakala zingakhula njengoba izinhlelo ziba nzima kakhulu.
Chaza ukubambezeleka, ikhwalithi, nezindleko ezihlosiwe ngaphambi kokuqaliswa.
Ibhentshimakhi ngaphansi komthwalo wangempela nezimo zedatha.
Ukuqapha amathuluzi amaphutha, ukukhukhuleka, nomthelela wabasebenzisi.
Lungiselela izindlela zokuhlehlisa nezigameko ngaphambi kokukala.
Free newsletter
Three verified AI stories every weekday morning, written in plain English. Free forever, no ads.
One email each weekday. Unsubscribe in one click. We never sell or share your address.
Test yourself
Instant feedback on every answer, and a shareable certificate with a verifiable ID once you pass a course.
Support free AI education. AI Understanding is a 501(c)(3) nonprofit — no ads, no paywall, ever. Make a donation
I-YAMNet iyinethiwekhi ye-neural eqeqeshwe kusengaphambili yokuhlukanisa imicimbi yomsindo kusetshenziswa isilulumagama sekilasi le-AudioSet. Ikhiqiza amaphuzu ekilasi elizimele kanye nokushumekiwe okungasekela ukusesha okuzwakalayo noma ukudlulisa ukufunda, kodwa izibikezelo zayo zibonisa intela yokuqeqeshwa futhi akulona uhlelo lokuqonda umsindo jikelele.
I-YAMNet iyisigaba somcimbi womsindo esinemiphumela eboshelwe kumakilasi e-AudioSet.
Imodeli iveza okushumekiwe okungasebenza njengezici ezifundiwe.
Imodeli ayikwazi ukuhlukanisa ngokuqondile izigaba ezingekho kuzo noma ezibanzi kunamalebula okukhiphayo.
Ukusampula kabusha kwangempela kuguqula amanani esampula; ukulebula kabusha imethadatha akwenzi.
Ukushumeka kungase kusebenze njengezici zokufaka zesigaba esingezansi.
Qhubeka ufunda
Imihlahlandlela eyengeziwe yalesi sihloko
OkulandelayoUmhlahlandlela olandelayo
I-Softmax Regression for Multiclass Classification
Ubuchwepheshe