Ngicela unginike uhlu olufushane olunezinhlamvu lwezingxenye/izakhiwo eziyinhloko kunethiwekhi yezinzwa ehlanganisiwe.
Context:Inethiwekhi ye-convolutional ye-neural iqukethe ungqimba lokufaka, izingqimba ezifihliwe kanye nengqimba yokukhipha. Kunoma iyiphi inethiwekhi ye-feed-forward ye-neural, noma yiziphi izingqimba eziphakathi zibizwa ngokuthi zifihliwe ngoba okokufaka nokuphuma kwazo kufihliwe ngumsebenzi wokusebenza kanye nokuhlanganiswa kokugcina. Eqenjini le-convolutional neural network, izingqimba ezifihliwe zihlanganisa izingqimba ezenza ukuhlangana. Ngokuvamile lokhu kuhlanganisa ungqimba olwenza umkhiqizo we-dot we-convolution kernel nge-matrix yokufaka yesendlalelo. Lo mkhiqizo uvame ukuba umkhiqizo wangaphakathi we-Frobenius, futhi umsebenzi wawo wokusebenza uvame ukuba yi-ReLU. Njengoba i-convolution kernel ihamba nge-matrix yokufaka yesendlalelo, ukusebenza kwe-convolution kudala imephu yesici, okubuye kube nomthelela ekufakweni kwesendlalelo esilandelayo. Lokhu kulandelwa ezinye izingqimba ezifana nezingqimba zokuhlanganisa, izingqimba ezixhumeke ngokuphelele, nezingqimba zokujwayelekile.  Izendlalelo eziyindilinga  Izendlalelo ze-convolutional zihlanganisa okokufaka bese zidlulisela umphumela wazo ezingeni elilandelayo. Lokhu kufana nempendulo ye-neuron ese-cortex ebonakalayo lapho izwa okuthile okukhethekile. Ingqamuzana ngayinye ye-convolutional neuron icubungula idatha yenkambu yayo yokwamukela kuphela. Nakuba ukuxhumana okuphelele kwe-feedforward neural network kungasetshenziselwa ukufunda izici nokuhlukanisa idatha, lo mklamo ngokuvamile awusebenzi ekufakweni okukhulu (isib., izithombe eziphezulu), okuzodinga inani elikhulu lama-neurons ngoba i-pixel ngayinye iyisici sokufaka esifanelekile. Isendlalelo esixhunywe ngokuphelele sesithombe esinesayizi engu-100 × 100 sinesisindo esingu-10,000 se-neuron ngayinye esendlaleleni yesibili. I-convolution inciphisa inani lamapharamitha akhululekile, okuvumela inethiwekhi ukuba ibe ejulile. Ngokwesibonelo, ukusebenzisa isifunda samathayili esingu-5 × 5, ngasinye sinesisindo esifanayo, kudinga kuphela ama-neurons angu-25. Ukusebenzisa izisindo ezihleliwe ngaphezu kwamapharamitha ambalwa kugwema ama-gradients anyamalalayo kanye nezinkinga zokuqhuma kwama-gradients ezibonwe ngesikhathi sokusabalalisa emuva kumanethiwekhi we-neural wangaphambilini.  Ukusheshisa ukucubungula, izingqimba ezijwayelekile ze-convolutional zingashintshwa yizingqimba ezihlukaniswa ngokujulile ze-convolutional, ezisekelwe ekujuleni kokujula okulandelwa yi-convolution ephezulu. I-convolution ejulile i-convolution yendawo esetshenziswa ngokuzimela phezu kwesiteshi ngasinye se-input tensor, kuyilapho i-convolution ye-pointwise i-convolution ejwayelekile evinjelwe ekusetshenzisweni kwe  Izingqimba ezihlanganisayo Amanethiwekhi we-convolutional angahlanganisa izingqimba zokuhlanganisa zendawo kanye/noma zomhlaba kanye nezingqimba zendabuko ze-convolutional. Ukuhlanganisa izingqimba kunciphisa ubukhulu bemininingwane ngokuhlanganisa okuphuma emaqoqweni ezinzwa ezingqimbeni ezilodwa kube i-neuron eyodwa ezingqimbeni ezilandelayo. Ukuhlanganiswa kwendawo kuhlanganisa amaqoqo amancane, osayizi be-tile abanjengo-2 × 2 bavame ukusetshenziswa. Ukuhlanganiswa komhlaba wonke kusebenza kuwo wonke amangqamuzana ezinzwa ebalazweni lezici. Kunezinhlobo ezimbili ezivamile zokuhlanganisa ezisetshenziswa kakhulu: okuphezulu nokuphakathi. Ukuhlanganisa okuphezulu kusebenzisa inani eliphakeme lenqwaba ngayinye yendawo yama-neurons kumephu yezici, kuyilapho ukuhlanganisa okujwayelekile kuthatha inani eliphakathi.  Izendlalelo ezixhunywe ngokugcwele Izendlalelo ezixhunywe ngokuphelele zixhuma zonke izinzwa ezisesendleni esisodwa kuzo zonke izinzwa ezisesendleni esilandelayo. Kufana nenethiwekhi ye-multilayer perceptron neural network (MLP). I-matrix ebushelelezi idlula engxenyeni exhunywe ngokuphelele ukuze ihlukanise izithombe.  Inkambu yokwamukela Ezingxenyeni zezinzwa, i-neuron ngayinye ithola ukwaziswa okuvela ezindaweni eziningana ezingxenyeni ezandulele. Engxenyeni ye-convolutional, i-neuron ngayinye ithola ukwaziswa okuvela endaweni ethile kuphela engxenyeni eyandulele ebizwa ngokuthi i-receptive field ye-neuron. Ngokuvamile indawo iyisikwele (isib. 5 by 5 neurons). Kodwa-ke, ezingxenyeni ezixhumene ngokuphelele, insimu yokwamukela iyona yonke ingqimba yangaphambilini. Ngakho-ke, esakhiweni ngasinye sokuhlanganiswa, i-neuron ngayinye ithatha okufakwayo kusuka endaweni enkulu ekufakweni kunezingqimba zangaphambilini. Lokhu kungenxa yokusebenzisa i-convolution ngokuphindaphindiwe, okucabangela inani le-pixel, kanye namaphikseli ayo azungezile. Lapho usebenzisa izingqimba ezivulekile, inani lamaphikseli emkhakheni owamukelayo lihlala lingaguquguquki, kodwa insimu igcwele kancane njengoba ubukhulu bayo bukhula lapho kuhlanganiswa umphumela wezingqimba eziningana.  Ukuze ushintshe ubukhulu benkambu yokwamukela njengoba ufisa, kunezinye izindlela ezingena esikhundleni sohlaka olujwayelekile lwe-convolution. Isibonelo, ukuguquguquka kwe-atrous noma kwe-dilated kwandisa usayizi wenkambu yokwamukela ngaphandle kokukhulisa inani lamapharamitha ngokuhlanganisa izindawo ezibonakalayo nezingaboni. Ngaphezu kwalokho, ungqimba olulodwa olwandisiwe lwe-convolutional lungahlanganisa izihlungi ezinamanani amaningi okwanda, ngaleyo ndlela zibe nosayizi wenkambu owamukelekayo.  Izisindo Ingqamuzana ngayinye ye-neuron enethiwekhi ye-neural ibala inani lokukhipha ngokusebenzisa umsebenzi othize kumazinga okufaka atholakele emkhakheni wokwamukela ungqimba lwangaphambilini. Umsebenzi osetshenziselwa amanani okufaka unqunywa nge-vector yezilinganiso kanye ne-bias (ngokuvamile izinombolo zangempela). Ukufunda kuhilela ukulungisa ngokuphindaphindiwe lokhu kuncika nokulinganisela.  Ama-vector ezisindo nokuthambekela abizwa ngokuthi izihlungi futhi amelela izici ezithile zokufaka (isib., ukuma okuthile). Isici esivelele sama-CNN ukuthi izinzwa eziningi zingakwazi ukuhlanganyela isihlungi esifanayo. Lokhu kunciphisa imemori ngoba kusetshenziswa i-bias eyodwa kanye ne-vector eyodwa ye-weights kuyo yonke imikhakha yokwamukela ehlanganyela leso sihlungi, ngokungafani nomkhakha ngamunye wokwamukela onokuthambekela kwawo kanye nesisindo se-vector.
Izingxenye ezibalulekile / ukwakhiwa kwenethiwekhi ye-convolutional ye-neural kufaka phakathi:  Izendlalelo eziyindilinga: Lezi zindlalelo zenza i-matrix yokufaka futhi zinciphise inani lamapharamitha wamahhala futhi zivumela inethiwekhi ukuthi ibe ngokujulile.  Izingqimba ezihlanganisayo: Lezi zingqimba zinciphisa ubukhulu bemininingwane ngokuhlanganisa okukhiphayo kwamaqoqo ezinzwa ezingqimbeni ezilodwa zibe yinye inzwa engqimbeni elandelayo.  Izendlalelo ezixhunywe ngokuphelele: Lezi zindlalelo zixhuma zonke izinzwa ezisesendleni esisodwa kuzo zonke izinzwa ezisesendleni esilandelayo.  Inkambu yokwamukela: Ingqamuzana ngayinye yezinzwa ithola ukwaziswa okuvela endaweni ethile engxenyeni engaphambili ebizwa ngokuthi inkambu yokwamukela ye-neuron. Engxenyeni ye-convolutional, i-neuron ngayinye ithatha ukwaziswa okuvela endaweni enkulu kunengxenyeni yangaphambilini.  Izisindo: Ama-vector we-weights ne-bias asetshenziswa umsebenzi we-neural ukubala inani lokukhipha kusuka kunani lokufaka.