InoteveraGaidhi rinotevera
Gradient Descent (Kudzika Kwemutsara)
Basics
Basics GUIDE
Nesterov Yakawedzera Gradient (NAG) inzira ine hungwaru yekumhanya inotarira mberi isati yaita komputa gradient, ichipa kururamisa kutarisa-mberi.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Inowanzo sangana nekukurumidza uye zvakanyanya kugadzikana kupfuura classical kasi.
Classical momentum inokokorodza gradient panzvimbo yazvino, yobva yawedzera yakaunganidzwa velocity. Maonero aNesterov, kubva kuYurii Nesterov's 1983 basa rekusimudzira convex optimization, ndeyekutanga kutora danho rekusimudzira kuenda kunzvimbo yekutarisa-mberi uye kuongorora gradient ipapo. Izvi zvinoita kuti optimizer ifungidzire kuti iko kusimba kurikuitakura uye isa gadziriso isati yapfura, semumhanyi anoona curve kumberi ogadzirisa nekukurumidza kwete mushure. Nekuda kwematambudziko akatsetseka convex nzira yaNesterov inowana yakaringana kuchinjika mwero weodha 1/k^2 muhuwandu hwematanho, budiriro inogoneka pamusoro pe plain gradient descent's 1/k. Mukudzidza kwakadzama kunopihwa sechisarudzo chakareruka mumafuremu mazhinji uye kazhinji chinoburitsa nekukurumidza zvishoma, kushoma kudzidziswa kusinganzwisisike pane kusimuka kwakajairwa pane imwechete coefficient.
Kugonesa iyo nesterov=Chokwadi mureza muPyTorch kana TensorFlow SGD yekukurumidza, yakapfava kudzidziswa.
Kuwedzera kusanganisa pamatambudziko akatsetseka convex senge hombe-chiyero chekugadzirisa zvinhu.
Kuderedza overshoot uye oscillation paunenge uchidzidzira zvakadzika network padhuze yakapinza minima.
Kupa simba iyo Nadam optimizer, iyo inowedzera Nesterov kutarisa-mberi kuna Adamu.
Nesterov kukurumidza mureza wakavakirwa-mukati mune optimizers mhiri kwePyTorch, TensorFlow, uye nevamwe, uye Nesterov musiyano waAdam (Nadam) unosanganisa kutarisa-kumberi nekuyera kunoenderana. Dzidziso yaro yekusimudzira inoenderera mberi nekukurudzira kutsvagisa munzira dzekumhanya, kutangazve zvirongwa, uye ongororo yekuti nei kukurumidza kunobatsira mune isina-convex yakadzika network. Tarisira Nesterov-maitiro ekutarisa-mberi kuti arambe akanyarara akajairika kune varapi vari kudzingirira nekukurumidza, kwakasimba kuchinjika.
Tsamba yemahara
Nhatu dzakasimbiswa dzeAI nyaya mangwanani ega ega pakati pevhiki, dzakanyorwa neChirungu chakajeka. Yemahara zvachose, hapana ads.
Imwe email zuva rega rega revhiki. Bvisa kunyoresa kamwe chete. Hatimbotengese kana kugovera kero yako.
Zviedze iwe pachako
Mhinduro yepakarepo pamhinduro yega yega, uye chitupa chinogovaniswa chine ID inobatika kana wapedza kosi.
Tsigira dzidzo yemahara yeAI. AI Understanding ndeye 501(c)(3) isingabatsiri - hapana ads, hapana paywall, zvachose. Ita chipo
Nesterov Yakawedzera Gradient (NAG) inzira ine hungwaru yekumhanya inotarira mberi isati yaita komputa gradient, ichipa kururamisa kutarisa-mberi. Inowanzo sangana nekukurumidza uye zvakanyanya kugadzikana kupfuura classical kasi.
Nesterov anotanga ashandisa nhanho yekukurumidza kusvika panzvimbo yekutarisa kumberi, obva averengera gradient ipapo, achipa gadziriso yekutarisira.
Nesterov's yekukurumidza nzira inowana yakakwana 1/k^2 mwero wezvakatsetseka convex zvinangwa, nekukurumidza kupfuura gradient descent's 1/k.
Yurii Nesterov akaburitsa iyo yakakwirisa gradient nzira muna 1983 ye convex optimization.
Nekuongorora gradient uko kukurumidza kuri kunanga, Nesterov inogona kururamisa iri kuuya yakakwira kupfuura yekirasi yekusimudzira.
Maframeworks anoisa fomu rakarongwazve kuitira kuti Nesterov awedzere mari isingaite pane yakajairika.
Ramba uchidzidza
Mamwe madhairekitori akasarudzirwa nyaya iyi
InoteveraGaidhi rinotevera
Gradient Descent (Kudzika Kwemutsara)
Basics