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Gradient Descent (Kudzika Kwemutsara)
Basics
Basics GUIDE
Stochastic Gradient Descent ine Momentum
Momentum ndeye dhizaini kune gradient kudzika iyo inounganidza inomhanya avhareji yeakapfuura gradients, ichirega optimization ichimhanya nekukurumidza nemumipata uye nekunyorovesa oscillations.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Pa peji ino2 min verenga
Pfupiso
It is one of the most widely used training tricks in deep learning.
Kudzika Kwakadzika
Plain stochastic gradient descent (SGD) inogadziridza paramita nekukwira munzira yakatarisana neyazvino mini-batch gradient. Munzvimbo dzakaita semakoronga marefu, akamanikana, aya anomonereka achiyambuka madziro ane materu achikambaira pauriri hwakapfava. Momentum, yakakurumbira naPolyak uye gare gare naRumelhart nevamwe vaanoshanda navo, inogadzirisa izvi nekuchengetedza velocity vector: nhanho imwe neimwe inosanganisa iyo nyowani gradient nechikamu (iyo yekukurumidza coefficient, kazhinji 0.9) yevelocity yapfuura. Inopindirana gradient madhairekitori anosimbisa uye nekumhanyisa, ukuwo oscillating zvikamu zvinodzima zvishoma. Enzaniso yemuviri ibhora rinorema rinokunguruka richidzika: rinovaka kukurumidza munzira dzakatsiga uye harina kudzoserwa nemapundu ane ruzha, richipa nekukurumidza, kutsvedzerera kuchinjika kupfuura vanilla SGD.
Kuitwa muNyika yeChokwadi
Kudzidzira zvakadzika convolutional network seResNet, uko SGD ine simba 0.9 iri resipi yakajairwa.
Inopfavisa ruzha gradient fungidziro paunenge uchishandisa madiki mabhechi.
Kutiza nzvimbo dzakadzika dzisina kudzika nekutakura kumhanya munzvimbo dzakati sandara.
Kushanda senge nguva yekumhanya mukati me adapta optimizers senge Adam uye RMSprop akasiyana.
Ramangwana reStochastic Gradient Descent neMomentum
Momentum inoramba iri hwaro: anogadzirisa optimizers saAdhama uye akasiyana-siyana anodzika fungidziro yekukurumidza-maitiro ekutanga-nguva, uye SGD ine simba ichiri hwaro hwakasimba hunowanzo wedzera zvirinani pane inochinja nzira pamhando huru dzechiratidzo. Tsvagiridzo inoenderera mberi pakuronga kwekukurumidza, kuora kwehuremu kwakadzikwa, uye kudyidzana kwayo neyakakura kwazvo batch kudzidziswa. Tarisira kusimba kuti urambe uri chinhu chakakosha sezvo optimizers inoshanduka kune inogara yakakura modhi.
Kuzvishandisa
Nei zvichikosha
- Sarudzo dzakajeka. Inokubatsira kuparadzanisa zvakajeka zvichemo zvehunyanzvi kubva mumutauro wekushambadzira.
- Mutengo uye bhajeti. Iwe unogona kubvunza zvirinani kuita mibvunzo usati washandisa mari kana nguva.
- Team uye workflow. Zvikwata zvine nzwisiso yakagovaniswa inoita zvirinani chigadzirwa, mutemo, uye sarudzo dzekudzidza.
Chenjera
- Zvikwata zvakasiyana zvinogona kushandisa izwi rimwechete zvakasiyana, saka tsanangura nzvimbo nekukurumidza.
- Benchmarks inogona kutaridzika yakasimba nepo chaiyo-yenyika kuita isina kuenzana.
- Kuregeredza mhando yedata uye zvirongwa zvekuongorora zvinowanzogadzira mhedzisiro isina kusimba.
Matanho anotevera
- Tanga netsanangudzo yemutauro wakajeka yemhedzisiro yaunoda.
- Sarudza metric imwe yekubudirira uye imwe yekutadza mamiriro usati waedzwa.
- Mhanya mutyairi mudiki ane data remumiriri, kwete demo rakakwenenzverwa.
- Gwaro uko Stochastic Gradient Descent ine Momentum inobatsira uye uko nzira dzakareruka dziri nani.
Ramba Uchiongorora
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Take the Stochastic Gradient Descent with Momentum quiz
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Mibvunzo inowanzo bvunzwa
What is Stochastic Gradient Descent with Momentum?
Momentum ndeye dhizaini kune gradient kudzika iyo inounganidza inomhanya avhareji yeakapfuura gradients, ichirega optimization ichimhanya nekukurumidza nemumipata uye nekunyorovesa oscillations. Ndiyo imwe yeanonyanya kushandiswa maitiro ekudzidzisa mukudzidza kwakadzama.
Chii chinonzi nguva yekusimudzira inounganidza panguva yekudzidziswa?
Momentum inochengetedza velocity vector iri exponentially huremu inofamba avhareji yeazvino gradients, inotsvedzerera iyo yekuvandudza nzira.
Mumupata wakareba, wakamanikana, idambudziko ripi rinosangana neSGD iro simba rinobatsira kugadzirisa?
Pasina simba, SGD inotenderera ichiyambuka mawere emupata. Momentum inodzima aya maoscillations uye inomhanyisa pamwe nekupfava kwemupata pasi.
Ndeipi fananidzo yemuviri inowanzoshandiswa kutsanangura kukurumidza?
Momentum inofananidzwa nebhora rinorema rinovaka kumhanya munzira dzakafanana uye rinoramba kutsauka kubva pamapundu ane ruzha.
Zvingangove zvakadii kusimba kunosimudzira nhanho inoshanda munzira inowirirana kana beta = 0.9?
Iyo amplification factor inosvika 1/(1 - beta), uye 1/(1 - 0.9) = 10, saka nzira dzinoenderana dzinokwidziridzwa zvakapetwa kagumi.
Ndeipi yemazuva ano optimizer inosanganisira yekukurumidza-maitiro ekutanga-nguva fungidziro?
Adamu anosanganisa nhanho-seyekutanga-nguva (zvinoreva) fungidziro yemagradients ine kechipiri-nguva (kusiyana) fungidziro yeadaptive scaling.
Ramba uchidzidza
Mazano anoenderana
Mamwe madhairekitori akasarudzirwa nyaya iyi