{"id":30028,"date":"2019-10-31T21:33:18","date_gmt":"2019-10-31T18:33:18","guid":{"rendered":"https:\/\/prohoster.info\/blog\/rabotaem-s-nejrosetyami-chek-list-dlya-otladki\/"},"modified":"2019-10-31T21:33:18","modified_gmt":"2019-10-31T18:33:18","slug":"rabotaem-s-nejrosetyami-chek-list-dlya-otladki","status":"publish","type":"post","link":"https:\/\/prohoster.info\/ro\/blog\/rabotaem-s-nejrosetyami-chek-list-dlya-otladki","title":{"rendered":"Lucr\u0103m cu re\u021bele neuronale: lista de verificare pentru depanare","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p><img decoding=\"async\" alt=\"Lucr\u0103m cu re\u021bele neuronale: lista de verificare pentru depanare\" src=\"\/wp-content\/uploads\/2019\/03\/8ce45093bfe44092cb25d947c32bb0b5.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n <br \/>\nCodul produselor software pentru \u00eenv\u0103\u021barea automat\u0103 este adesea complex \u0219i destul de confuz. Identificarea \u0219i eliminarea erorilor din acesta este o sarcin\u0103 consumatoare de resurse. Chiar \u0219i cele mai simple <noindex><a rel=\"nofollow\" href=\"https:\/\/cs.stanford.edu\/people\/eroberts\/courses\/soco\/projects\/neural-networks\/Architecture\/feedforward.html\">re\u021bele neuronale cu leg\u0103turi directe<\/a><\/noindex> necessit\u0103 o abordare serioas\u0103 \u00een ceea ce prive\u0219te arhitectura re\u021belei, ini\u021bializarea greut\u0103\u021bilor \u0219i optimizarea re\u021belei. O mic\u0103 gre\u0219eal\u0103 poate duce la apari\u021bia unor probleme nepl\u0103cute.<\/p>\n<p>Acest articol este dedicat algoritmului de depanare a re\u021belelor neuronale.<br \/>\n<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<blockquote><p><b>Skillbox recomand\u0103:<\/b> Curs practic <noindex><a rel=\"nofollow\" href=\"https:\/\/skillbox.ru\/python\/?utm_source=skillbox.media&amp;utm_medium=habr.com&amp;utm_campaign=PTNDEV&amp;utm_content=articles&amp;utm_term=neuronet\">Dezvoltator Python de la zero<\/a><\/noindex>.<\/p>\n<p><b>V\u0103 reamintim:<\/b> <i>pentru to\u021bi cititorii \u201eHabr\u201d \u2014 reducere de 10.000 de ruble la \u00eenscrierea la orice curs Skillbox cu codul de promovare \u201eHabr\u201d.<\/i><\/p><\/blockquote>\n<p><\/p>\n<h3>Algoritmul const\u0103 \u00een cinci etape:<\/h3>\n<p><\/p>\n<ul>\n<li>\u00eencepere simpl\u0103;<\/li>\n<li>confirmarea pierderilor;<\/li>\n<li>verificarea rezultatelor intermediare \u0219i a conexiunilor;<\/li>\n<li>diagnosticarea parametrilor;<\/li>\n<li>monitorizarea func\u021bion\u0103rii.<\/li>\n<\/ul>\n<p>\nDac\u0103 ceva \u021bi se pare mai interesant dec\u00e2t restul, po\u021bi s\u0103 treci imediat la aceste sec\u021biuni. <\/p>\n<h3>\u00cencepere simpl\u0103<\/h3>\n<p>\nDepanarea unei re\u021bele neuronale cu o arhitectur\u0103 complex\u0103, regularizare \u0219i un planificator al vitezei de \u00eenv\u0103\u021bare este mai dificil\u0103 dec\u00e2t cea a unei re\u021bele obi\u0219nuite. Ne folosin un pic de subtilitate aici, deoarece acest punct are o rela\u021bie indirect\u0103 cu depanarea, dar este totu\u0219i o recomandare important\u0103.<\/p>\n<p>\u00cenceperea simpl\u0103 const\u0103 \u00een crearea unui model simplificat \u0219i \u00eenv\u0103\u021barea acestuia pe un singur set (punct) de date.<\/p>\n<p><b>Primul pas este crearea unui model simplificat<\/b><\/p>\n<p>Pentru a \u00eencepe rapid, cre\u0103m o re\u021bea mic\u0103 cu un singur strat ascuns \u0219i ne asigur\u0103m c\u0103 totul func\u021bioneaz\u0103 corect. Apoi, complic\u0103m treptat modelul, verific\u00e2nd fiecare nou aspect al structurii sale (strat suplimentar, parametru etc.) \u0219i continu\u0103m.<\/p>\n<p><b>\u00cenv\u0103\u021barea modelului pe un singur set (punct) de date<\/b><\/p>\n<p>Ca verificare rapid\u0103 a func\u021bionalit\u0103\u021bii proiectului t\u0103u, po\u021bi folosi un sau dou\u0103 puncte de date pentru a confirma dac\u0103 sistemul func\u021bioneaz\u0103 corect. Re\u021beaua neuronal\u0103 ar trebui s\u0103 arate o precizie de 100% la \u00eenv\u0103\u021bare \u0219i testare. Dac\u0103 nu este a\u0219a, fie modelul este prea mic, fie deja exist\u0103 o eroare.<\/p>\n<p>Chiar dac\u0103 totul este bine, preg\u0103te\u0219te modelul pentru a parcurge una sau mai multe epoci \u00eenainte de a merge mai departe.<\/p>\n<h3>Evaluarea pierderilor<\/h3>\n<p>\nEvaluarea pierderilor este metoda principal\u0103 pentru a rafina performan\u021ba modelului. Trebuie s\u0103 te asiguri c\u0103 pierderea corespunde sarcinii \u0219i c\u0103 func\u021biile de pierdere sunt evaluate pe o scal\u0103 corect\u0103. Dac\u0103 folose\u0219ti mai mult de un tip de pierdere, asigur\u0103-te c\u0103 toate sunt de acela\u0219i ordin \u0219i corect scalate.<\/p>\n<p>Este important s\u0103 fii atent la pierderile ini\u021biale. Verific\u0103 c\u00e2t de aproape este rezultatul real de cel a\u0219teptat, dac\u0103 modelul a pornit de la o presupunere aleatoare. \u00cen <noindex><a rel=\"nofollow\" href=\"http:\/\/cs231n.github.io\/neural-networks-3\/#baby\">munca lui Andrei Karpaty se propune urm\u0103toarea<\/a><\/noindex>: \u201eAsigura\u021bi-v\u0103 c\u0103 ob\u021bine\u021bi rezultatul a\u0219teptat atunci c\u00e2nd \u00eencepe\u021bi s\u0103 lucra\u021bi cu un num\u0103r mic de parametrii. Cel mai bine este s\u0103 verifica\u021bi imediat pierderile de date (cu gradul de regularizare setat la zero). De exemplu, pentru CIFAR-10 cu clasificatorul Softmax, ne a\u0219tept\u0103m ca pierderile ini\u021biale s\u0103 fie 2.302, deoarece probabilitatea difuz\u0103 a\u0219teptat\u0103 este de 0.1 pentru fiecare clas\u0103 (deoarece exist\u0103 10 clase), iar pierderea Softmax este logaritmul negativ al probabilit\u0103\u021bii clasei corecte, adic\u0103 -ln(0.1) = 2.302\u201d.<\/p>\n<p>Pentru exemplul binar, se efectueaz\u0103 o calculare similar\u0103 pentru fiecare dintre clase. Iat\u0103, de exemplu, datele: 20% 0 \u0219i 80% 1. Pierderea ini\u021bial\u0103 a\u0219teptat\u0103 va fi de p\u00e2n\u0103 la \u20130,2ln (0,5) \u20130,8ln (0,5) = 0,693147. Dac\u0103 rezultatul este mai mare de 1, acest lucru poate indica faptul c\u0103 greut\u0103\u021bile re\u021belei neuronale nu sunt echilibrate corespunz\u0103tor sau datele nu sunt normalizate.<\/p>\n<h3>Verific\u0103m rezultatele intermediare \u0219i conexiunile <\/h3>\n<p>\nPentru a debuga re\u021beaua neuronal\u0103, este necesar s\u0103 \u00een\u021belegem dinamica proceselor din interiorul re\u021belei \u0219i rolul fiec\u0103rei straturi intermediare, deoarece acestea sunt interconectate. Iat\u0103 gre\u0219elile tipice cu care s-ar putea s\u0103 te confrun\u021bi:<\/p>\n<ul>\n<li>expresiile gre\u0219ite pentru actualiz\u0103rile gradientului;<\/li>\n<li>nu se aplic\u0103 actualiz\u0103rile greut\u0103\u021bii;<\/li>\n<li>gradienti care dispar sau explod\u00e2nd (exploding gradients).<\/li>\n<\/ul>\n<p>\nDac\u0103 valorile gradientului sunt zero, \u00eenseamn\u0103 c\u0103 rata de \u00eenv\u0103\u021bare a optimizatorului este prea mic\u0103 sau c\u0103 te confrun\u021bi cu o expresie incorect\u0103 pentru actualizarea gradientului.<\/p>\n<p>\u00cen plus, este necesar s\u0103 monitoriz\u0103m valorile func\u021biilor de activare, greut\u0103\u021bilor \u0219i actualiz\u0103rilor fiec\u0103rei straturi. De exemplu, magnitudinea actualiz\u0103rilor parametrilor (greut\u0103\u021bilor \u0219i biasurilor) <noindex><a rel=\"nofollow\" href=\"https:\/\/cs231n.github.io\/neural-networks-3\/#summary\">ar trebui s\u0103 fie de 1-e3<\/a><\/noindex>.<\/p>\n<p>Exist\u0103 un fenomen cunoscut sub numele de \u201eDying ReLU\u201d sau <noindex><a rel=\"nofollow\" href=\"https:\/\/en.wikipedia.org\/wiki\/Vanishing_gradient_problem\">\u201eproblema gradientului care dispare\u201d<\/a><\/noindex>, c\u00e2nd neuronii ReLU vor returna zero dup\u0103 ce au \u00eenv\u0103\u021bat o valoare negativ\u0103 mare (bias) pentru greut\u0103\u021bile lor. Ace\u0219ti neuroni nu se mai activeaz\u0103 niciodat\u0103 \u00een date.<\/p>\n<p>Pute\u021bi folosi verificarea gradientului pentru a identifica aceste erori prin aproximarea gradientului utiliz\u00e2nd o abordare numeric\u0103. Dac\u0103 este aproape de gradientele calculate, atunci propagarea invers\u0103 a fost implementat\u0103 corect. Pentru a crea o verificare a gradientului, consulta\u021bi aceste resurse minunate din CS231 <noindex><a rel=\"nofollow\" href=\"http:\/\/cs231n.github.io\/neural-networks-3\/#gradcheck\">aici<\/a><\/noindex> \u0219i <noindex><a rel=\"nofollow\" href=\"http:\/\/cs231n.github.io\/optimization-1\/#gradcompute\">aici<\/a><\/noindex>, precum \u0219i <noindex><a rel=\"nofollow\" href=\"https:\/\/www.youtube.com\/watch?v=P6EtCVrvYPU\">lec\u021bia<\/a><\/noindex> lui Andrew Ng pe aceast\u0103 tem\u0103.<\/p>\n<p><noindex>Faizan Sheikh<\/noindex> identific\u0103 trei metode principale de vizualizare a re\u021belelor neuronale:<\/p>\n<ul>\n<li>Vizualiz\u0103ri preliminare \u2014 metode simple care ne arat\u0103 structura general\u0103 a modelului antrenat. Acestea includ ie\u0219irea formelor sau a filtrelor straturilor individuale ale re\u021belei neuronale \u0219i parametrii din fiecare strat.<\/li>\n<li> Bazate pe activare. Acestea decodeaz\u0103 activ\u0103rile neuronilor individuali sau ale grupurilor de neuroni pentru a \u00een\u021belege func\u021biile acestora.<\/li>\n<li> Bazate pe gradienti. Aceste metode tind s\u0103 manipuleze gradientii forma\u021bi din trecerea \u00eenainte \u0219i \u00eenapoi \u00een timpul antren\u0103rii modelului (inclusiv h\u0103r\u021bile de importan\u021b\u0103 \u0219i h\u0103r\u021bile de activare pentru clas\u0103).<\/li>\n<\/ul>\n<p>\nExist\u0103 c\u00e2teva instrumente utile pentru vizualizarea activ\u0103rilor \u0219i conexiunilor din straturile individuale, cum ar fi <noindex><a rel=\"nofollow\" href=\"https:\/\/conx.readthedocs.io\/en\/latest\/Getting%20Started%20with%20conx.html#What-is-ConX?\">ConX<\/a><\/noindex> \u0219i <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/tensorboard_histograms\">Tensorboard<\/a><\/noindex>.<\/p>\n<p><img decoding=\"async\" alt=\"Lucr\u0103m cu re\u021bele neuronale: lista de verificare pentru depanare\" src=\"\/wp-content\/uploads\/2019\/03\/56e7e88983d6772ef482bc8396dd52a2.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n <\/p>\n<h3>Diagnosticarea parametrilor<\/h3>\n<p>\nRe\u021belele neuronale au o mul\u021bime de parametri care interac\u021bioneaz\u0103 \u00eentre ei, ceea ce complic\u0103 optimizarea. De fapt, aceast\u0103 sec\u021biune este subiectul unor cercet\u0103ri active, a\u0219a c\u0103 recomand\u0103rile de mai jos ar trebui considerate doar ca sfaturi, puncte de plecare de la care se poate porni.<\/p>\n<p><b>Dimensiunea lotului<\/b> (batch size) \u2014 este necesar ca dimensiunea lotului s\u0103 fie suficient de mare pentru a ob\u021bine estim\u0103ri precise ale gradientului erorii, dar suficient de mic\u0103 pentru ca gradientul stocastic descendent (SGD) s\u0103 poat\u0103 organiza re\u021beaua dumneavoastr\u0103. Dimensiunile mici ale lotului vor duce la o convergen\u021b\u0103 rapid\u0103 din cauza zgomotului din procesul de antrenare \u0219i, ulterior, la dificult\u0103\u021bi \u00een optimizare. Aceasta este descris\u0103 mai detaliat <noindex><a rel=\"nofollow\" href=\"https:\/\/arxiv.org\/abs\/1609.04836\">aici<\/a><\/noindex>.<\/p>\n<p><b>Rata de \u00eenv\u0103\u021bare<\/b> \u2014 prea mic\u0103 va duce la o convergen\u021b\u0103 lent\u0103 sau va risca s\u0103 r\u0103m\u00e2n\u0103 blocat\u0103 \u00een minime locale. \u00cen acela\u0219i timp, o rat\u0103 de \u00eenv\u0103\u021bare mare va provoca divergen\u021ba optimiz\u0103rii, deoarece risca\u021bi s\u0103 \"s\u0103ri\u021bi\" peste o parte ad\u00e2nc\u0103, dar \u00eengust\u0103 a func\u021biei de pierdere. \u00cencerca\u021bi s\u0103 folosi\u021bi planificarea ratei pentru a o reduce \u00een timpul antren\u0103rii re\u021belei neuronale. \u00cen cursul CS231n <noindex><a rel=\"nofollow\" href=\"http:\/\/cs231n.github.io\/neural-networks-3\/\">exist\u0103 o sec\u021biune mare dedicat\u0103 acestei probleme<\/a><\/noindex>.<\/p>\n<p><b>T\u0103ierea gradientului<\/b>\u200a\u2014 t\u0103ierea parametrilor gradientului \u00een timpul propag\u0103rii invers cumulate la valoarea maxim\u0103 sau norma limit\u0103. Este util pentru rezolvarea problemelor cu orice gradient exploziv cu care te po\u021bi confrunta \u00een al treilea punct.<\/p>\n<p><b>Normalizarea pe loturi<\/b> \u2014 este folosit\u0103 pentru normalizarea datelor de intrare ale fiec\u0103rei straturi, ajut\u00e2nd la rezolvarea problemei de deplasare a covarian\u021bei interne. Dac\u0103 folose\u0219ti Dropout \u0219i Batch Normalization \u00eempreun\u0103, <noindex><a rel=\"nofollow\" href=\"https:\/\/towardsdatascience.com\/pitfalls-of-batch-norm-in-tensorflow-and-sanity-checks-for-training-networks-e86c207548c8\">consult\u0103 acest articol<\/a><\/noindex>.<\/p>\n<p><b>\u00cenv\u0103\u021barea gradientului stocastic (SGD)<\/b> \u2014 exist\u0103 mai multe variante de SGD care utilizeaz\u0103 impuls, viteze de \u00eenv\u0103\u021bare adaptative \u0219i metoda Nesterov. Cu toate acestea, nimeni dintre ele nu are un avantaj clar \u00een ceea ce prive\u0219te eficien\u021ba \u00eenv\u0103\u021b\u0103rii sau generalizarea (<noindex><a rel=\"nofollow\" href=\"http:\/\/ruder.io\/optimizing-gradient-descent\/\">detalii aici<\/a><\/noindex>).<\/p>\n<p><b>Regularizare<\/b> \u2014 este crucial\u0103 pentru construirea unui model generalizabil, deoarece adaug\u0103 o penalizare pentru complexitatea modelului sau valorile extreme ale parametrilor. Este o modalitate de a reduce dispersia modelului f\u0103r\u0103 a cre\u0219te semnificativ prejudiciul acestuia. Mai <noindex><a rel=\"nofollow\" href=\"http:\/\/cs231n.github.io\/neural-networks-3\/#ratio\">informa\u021bii detaliate \u2013 aici<\/a><\/noindex>.<\/p>\n<p>Pentru a evalua totul singur, este necesar s\u0103 dezactivezi regularizarea \u0219i s\u0103 verifici gradientul pierderii datelor singur.<\/p>\n<p><b>Dropout <\/b>\u2014 este o alt\u0103 metod\u0103 de organizare a re\u021belei tale pentru a preveni supra\u00eenc\u0103rcarea. \u00cen timpul antrenamentului, Dropout se realizeaz\u0103 men\u021bin\u00e2nd activitatea neuronului cu o probabilitate p (un hiperparametru) sau stabilindu-l la zero \u00een caz contrar. Ca urmare, re\u021beaua trebuie s\u0103 utilizeze un alt subset de parametri pentru fiecare grup\u0103 de antrenament, ceea ce reduce varia\u021biile unor parametri care devin dominan\u021bi.<\/p>\n<p>Important: dac\u0103 folose\u0219ti at\u00e2t dropout, c\u00e2t \u0219i normalizarea pe loturi, fii atent la ordinea acestor opera\u021bii sau chiar la utilizarea lor \u00eempreun\u0103. Toate acestea sunt \u00eenc\u0103 active \u00een discu\u021bii \u0219i complet\u0103ri. Iat\u0103 dou\u0103 discu\u021bii importante pe aceast\u0103 tem\u0103 <noindex><a rel=\"nofollow\" href=\"https:\/\/stackoverflow.com\/questions\/39691902\/ordering-of-batch-normalization-and-dropout\">pe Stackoverflow<\/a><\/noindex> \u0219i <noindex><a rel=\"nofollow\" href=\"https:\/\/arxiv.org\/abs\/1801.05134\">Arxiv<\/a><\/noindex>.<\/p>\n<h3>Monitorizarea performan\u021bei<\/h3>\n<p>\nEste vorba despre documentarea proceselor de lucru \u0219i experimentelor. Dac\u0103 nu se documenteaz\u0103 nimic, exist\u0103 riscul de a uita, de exemplu, care este viteza de \u00eenv\u0103\u021bare sau greutatea claselor. Datorit\u0103 controlului, este posibil s\u0103 vizualiza\u021bi \u0219i s\u0103 reproduce\u021bi f\u0103r\u0103 probleme experimentele anterioare. Acest lucru ajut\u0103 la reducerea num\u0103rului de experimente duplicate.<\/p>\n<p>Adev\u0103rat, documentarea manual\u0103 poate deveni o sarcin\u0103 complex\u0103 \u00een cazul unui volum mare de munc\u0103. Aici intervin instrumente precum Comet.ml, care ajut\u0103 la logarea automat\u0103 a seturilor de date, modific\u0103rilor de cod, istoricului experimentelor \u0219i modelelor de produc\u021bie, inclusiv informa\u021bii esen\u021biale despre modelul dumneavoastr\u0103 (hiperparametrii, m\u0103sur\u0103torile performan\u021bei modelului \u0219i detalii despre mediu).<\/p>\n<p>Re\u021belele neuronale pot fi foarte sensibile la mici modific\u0103ri, ceea ce poate duce la o sc\u0103dere a performan\u021bei modelului. Monitorizarea \u0219i documentarea func\u021bion\u0103rii sunt primul pas pe care ar trebui s\u0103 \u00eel face\u021bi pentru a standardiza mediul \u0219i modelarea.<\/p>\n<p><img decoding=\"async\" alt=\"Lucr\u0103m cu re\u021bele neuronale: lista de verificare pentru depanare\" src=\"\/wp-content\/uploads\/2019\/03\/37b3e4ef97ea39a3d28ffca5c1dbf1e5.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n <br \/>\nSper c\u0103 acest post poate deveni punctul de plecare de la care ve\u021bi \u00eencepe s\u0103 debuga\u021bi re\u021beaua dumneavoastr\u0103 neural\u0103.<\/p>\n<blockquote><p><b>Skillbox recomand\u0103:<\/b><\/p>\n<ul>\n<li>Curs practic de doi ani <noindex><a rel=\"nofollow\" href=\"https:\/\/iamwebdev.skillbox.ru\/?utm_source=skillbox.media&amp;utm_medium=habr.com&amp;utm_campaign=WEBDEVPRO&amp;utm_content=articles&amp;utm_term=neuronet\">\u201eSunt un dezvoltator web PRO\u201d<\/a><\/noindex>.<\/li>\n<li>Curs online <noindex><a rel=\"nofollow\" href=\"https:\/\/skillbox.ru\/c-sharp\/?utm_source=skillbox.media&amp;utm_medium=habr.com&amp;utm_campaign=CSHDEV&amp;utm_content=articles&amp;utm_term=neuronet\">\u201eDezvoltator C# cu 0\u201d<\/a><\/noindex>.<\/li>\n<li>Curs practic anual <noindex><a rel=\"nofollow\" href=\"https:\/\/skillbox.ru\/php\/?utm_source=skillbox.media&amp;utm_medium=habr.com&amp;utm_campaign=PHPDEV&amp;utm_content=articles&amp;utm_term=neuronet\">\u00abDezvoltator PHP de la 0 la PRO\u00bb<\/a><\/noindex>.\n<\/li>\n<\/ul>\n<\/blockquote>\n<p>Sursa: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/skillbox\/blog\/444684\/\">habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u041a\u043e\u0434 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u043d\u044b\u0445 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u043e\u0432 \u0434\u043b\u044f \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0447\u0430\u0441\u0442\u043e \u0431\u044b\u0432\u0430\u0435\u0442 \u0441\u043b\u043e\u0436\u043d\u044b\u043c \u0438 \u0434\u043e\u0432\u043e\u043b\u044c\u043d\u043e \u0437\u0430\u043f\u0443\u0442\u0430\u043d\u043d\u044b\u043c. \u041e\u0431\u043d\u0430\u0440\u0443\u0436\u0435\u043d\u0438\u0435 \u0438 \u043b\u0438\u043a\u0432\u0438\u0434\u0430\u0446\u0438\u044f \u0431\u0430\u0433\u043e\u0432 \u0432 \u043d\u0435\u043c \u2014 \u0440\u0435\u0441\u0443\u0440\u0441\u043e\u0435\u043c\u043a\u0430\u044f \u0437\u0430\u0434\u0430\u0447\u0430. \u0414\u0430\u0436\u0435 \u043f\u0440\u043e\u0441\u0442\u0435\u0439\u0448\u0438\u0435 \u043d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u0438 \u0441 \u043f\u0440\u044f\u043c\u043e\u0439 \u0441\u0432\u044f\u0437\u044c\u044e \u0442\u0440\u0435\u0431\u0443\u044e\u0442 \u0441\u0435\u0440\u044c\u0435\u0437\u043d\u043e\u0433\u043e \u043f\u043e\u0434\u0445\u043e\u0434\u0430 \u043a \u0441\u0435\u0442\u0435\u0432\u043e\u0439 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u0435, \u0438\u043d\u0438\u0446\u0438\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0438 \u0432\u0435\u0441\u043e\u0432, \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u0438 \u0441\u0435\u0442\u0438. \u041d\u0435\u0431\u043e\u043b\u044c\u0448\u0430\u044f \u043e\u0448\u0438\u0431\u043a\u0430 \u043c\u043e\u0436\u0435\u0442 \u043f\u0440\u0438\u0432\u0435\u0441\u0442\u0438 \u043a \u043f\u043e\u044f\u0432\u043b\u0435\u043d\u0438\u044e \u043d\u0435\u043f\u0440\u0438\u044f\u0442\u043d\u044b\u0445 \u043f\u0440\u043e\u0431\u043b\u0435\u043c. \u042d\u0442\u0430 \u0441\u0442\u0430\u0442\u044c\u044f \u043f\u043e\u0441\u0432\u044f\u0449\u0435\u043d\u0430 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0443 \u043e\u0442\u043b\u0430\u0434\u043a\u0438 \u0432\u0430\u0448\u0438\u0445 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u044b\u0445 \u0441\u0435\u0442\u0435\u0439. Skillbox \u0440\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0443\u0435\u0442: [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-30028","post","type-post","status-publish","format-standard","hentry"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.2.1 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u041a\u043e\u0434 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u043d\u044b\u0445 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u043e\u0432 \u0434\u043b\u044f \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e.\" \/>\n\t<meta name=\"robots\" content=\"max-image-preview:large\" \/>\n\t<meta name=\"author\" content=\"Yuri Gagarin\"\/>\n\t<link rel=\"canonical\" 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