Ndapota ndipei rondedzero pfupi yezvinhu zvinokosha/zvakagadzirwa muconvolutional neural network.
Context:Convolutional neural network ine input layer, hidden layers uye output layer. Mune chero feed-forward neural network, chero layers dzepakati dzinonzi dzakavanzika nekuti zvavanopinda uye zvavanobuda zvinofukidzwa nebasa rekumisikidza uye yekupedzisira convolution. Muconvolutional neural network, materu akavanzika anosanganisira materu anoita convolutions. Kazhinji izvi zvinosanganisira rukoko kuti anoita dot chigadzirwa ari convolution tsanga pamwe rukoko wacho chiyamuro matrix. Chigadzirwa ichi chinowanzova chigadzirwa chomukati cheFrobenius, uye basa racho rokushanda rinowanzonzi ReLU. Sezvo convolution kernel inotsvedza ichitevedza matrix ekupinda kweiyo layer, iyo convolution oparesheni inogadzira mepu yehunhu, iyo inobatsirawo kupinza kweiyo inotevera layer. Izvi zvinoteverwa nedzimwe nhurikidzwa dzakadai sekubatanidza nhurikidzwa, nhurikidzwa dzakabatanidzwa zvizere, uye nhurikidzwa dzekugadzirisa.  Zvikamu zvinotenderera  Mativi anogadzira mashoko anogadzira mashoko uye anopfuudza mugumisiro wacho kumativi anotevera. Izvi zvakafanana nemhinduro inopiwa neuroni iri muuropi hwokuona pane chimwe chinhu chinoita kuti munhu aite chimwe chinhu. Neuron imwe neimwe inochinja mashoko nokuda kwebasa rayo rinogamuchira bedzi. Kunyange zvazvo zvizere zvakabatanidzwa feedforward neural network zvinogona kushandiswa kudzidza zvinhu uye kuronga mashoko, mapurani ichi kazhinji hazviiti kuti makuru pakurima (semuenzaniso, mifananidzo yakakwirira chisarudzo), izvo zvaizoda guru nhamba neurons nokuti mumwe pixel chinhu chinokosha chiyamuro. Chikamu chakabatanidzwa chose nokuda kwechifananidzo cheukuru 100 × 100 chine zviuru gumi zvehuremu nokuda kweimwe neuron iri muchikamu chechipiri. Convolution inoderedza uwandu hweparameter dzakasununguka, ichibvumira network kuti ive yakadzika. Somuenzaniso, kushandisa 5 × 5 tiles, imwe neimwe iine uremu hwakaenzana, kunoda tsinga 25 chete. Kushandisa uremu hwakarongwa pamusoro pemipimo mishoma kunodzivisa kupera kwemapuraneti uye kuputika kwemapuraneti matambudziko anoonekwa panguva yekudzokera kumashure mumambure ekare ehutachiona.  Kuti kukurumidze kubudiswa, mureza convolutional akaturikidzana zvinogona kutsiviwa zvakadzama separable convolutional akaturikidzana, izvo kwakavakirwa zvakadzama convolution zvichiteverwa pointwise convolution. The kudzika convection ari spatial convection kushandiswa vakazvimiririra pamusoro mumwe mugero pamusoro chiyamuro tensor, apo pointwise convection ari mureza convection kungorambidzwa kushandiswa  Mativi anobatanidza Convolutional network dzinogona kusanganisira dzomunharaunda uye / kana pose pooling akaturikidzana pamwe chinyakare convolutional akaturikidzana. Kubatanidza mapoka kunoderedza ukuru hwemashoko nokubatanidza zvinobuda mumapoka emuroni pane rimwe boka kuva neuroni imwe chete iri munharaunda inotevera. Kubatanidza nzvimbo kunobatanidza mapoka maduku, mazamu akaenzana akadai se2 × 2 anowanzoshandiswa. Kubatanidza kwenyika yose kunoshanda pamasero etsinga ose emapu yechimiro. Kune marudzi maviri akajairika ekubatanidza mukushandiswa kwakakurumbira: max uye avhareji. Max pooling inoshandisa kukosha kukuru kweboka rega rega remuno remaneuroni mune mepu yehunhu, nepo avhareji pooling inotora avhareji kukosha.  Zvikamu zvakabatanidzwa zvizere Zvikamu zvakabatanidzwa zvakazara zvinobatanidza tsinga dzose dzomuviri dziri mune chimwe chikamu nesero rimwe nerimwe riri mune chimwe. Iyo yakafanana neyechinyakare multilayer perceptron neural network (MLP). Matrix yakatwasuka inopfuura nomumutsara wakabatanidzwa zvakakwana kuti igovere mifananidzo.  Munda unogamuchira Mumambure etsinga, tsinga imwe neimwe inogamuchira mashoko anobva kunzvimbo dzinoverengeka dziri munhengo yapfuura. Muchikamu checonvolutional, neuron imwe neimwe inogamuchira mashoko anobva munharaunda yakaganhurirwa yechikamu chakapfuura inonzi munda unogamuchira wetsinga. Kazhinji nzvimbo yacho iri sikweya (semuenzaniso 5 ne5 neurons). Nepo, mumutsetse wakabatanidzwa zvizere, munda unogamuchira uri mutsetse wose wapfuura. Nokudaro, mumutsara mumwe nomumwe wokutenderera, neuron imwe neimwe inotora mashoko anobva munharaunda yakakura zvikuru kupfuura mitsara yapfuura. Izvi zvinokonzerwa nokushandisa convolution kakawanda, iyo inorangarira kukosha kwepixel, pamwe chete nemapikisheni ayo akapoteredza. Paunenge uchishandisa akapararira akaturikidzana, nhamba pixels ari kugamuchira munda anoramba rakadzikama, asi munda iri zvishoma vagere sezvo kukura kwayo kukura kana tichibatanidza mugumisiro akaturikidzana akawanda.  Kuti uchinje ukuru hwemunda unogamuchirwa sezvaunoda, pane dzimwe nzira dzinoshandiswa pane imwe nzvimbo yakasarudzwa. Somuenzaniso, atrous kana kuti dilated convolution inowedzera ukuru hwomunda unogamuchira pasina kuwedzera nhamba yemipimo nokubatanidza nzvimbo dzinooneka nedzisingaoneki. Uyezve, imwe chete dilated convolutional rukoko anogona zvinosanganisira filters vane vakawanda dilation ratios, saka vane shanduka kugamuchira munda kukura.  Zvikamu Neuron imwe neimwe iri mumambure etsinga inoverengera kukosha kunobuda nokushandisa basa chairo kumipimo inopinda inogamuchirwa kubva kumunda unogamuchira uri munhevedzano yapfuura. Basa iro rinoshandiswa pamitengo inopinda rinotsanangurwa nechinhu chinorema uye chinokanganisa (kazhinji nhamba chaidzo). Kudzidza kunobatanidza kururamisa uku kusarura nokuremerwa nenzira inodzokororwa.  Zviratidzo zvehuremu uye zvinokanganisa zvinonzi mafirita uye zvinomiririra zvimwe zvinhu zvekuisa (semuenzaniso, chimiro chakati). Chinhu chinosiyanisa CNNs ndechokuti masero etsinga akawanda anogona kugoverana firita imwe cheteyo. Izvi zvinoderedza chiyeuchidzo tsoka nokuti mumwe kusarura uye mumwe vector weights anoshandiswa mhiri zvose vanogamuchira minda kuti kugoverana kuti Sefa, kusiyana nomumwe vanogamuchira munda kuva yayo kusarura uye vector weighting.
Zvikamu zvakakosha / zvivakwa zveconvolutional neural network zvinosanganisira:  Convolutional akaturikidzana: akaturikidzana aya convolve ari chiyamuro matrix uye kuderedza uwandu vakasununguka parameters uye anobvumira samambure kuti yakadzika.  Kubatanidza mapoka: Mapoka aya anoderedza ukuru hwemashoko nokubatanidza zvinobuda mumapoka emuroni pane rimwe boka kuva neuroni imwe chete iri mune rimwe boka rinotevera.  Zvikamu zvakabatanidzwa zvizere: Zvikamu izvi zvinobatanidza tsinga imwe neimwe iri mune chimwe chikamu neimwe tsinga iri mune chimwe chikamu.  Munda unogamuchira: Neuroni imwe neimwe inogamuchira mashoko anobva munharaunda yakaganhurirwa yomutsara wapfuura unonzi munda unogamuchira weuroni. Muchikamu checonvolutional, neuron imwe neimwe inotora mashoko anobva munharaunda yakakura pane inenge yapfuura.  Zviremera: Zviremera zvezviremera uye kusarura zvinoshandiswa nebasa rehuremu kuti riverenge kukosha kwekuburitsa kubva pakukosha kwekupinda.