{"id":38573,"date":"2019-10-31T22:24:37","date_gmt":"2019-10-31T19:24:37","guid":{"rendered":"https:\/\/prohoster.info\/blog\/vypusk-sistemy-mashinnogo-obucheniya-tensorflow-2-0\/"},"modified":"2019-10-31T22:24:37","modified_gmt":"2019-10-31T19:24:37","slug":"vypusk-sistemy-mashinnogo-obucheniya-tensorflow-2-0","status":"publish","type":"post","link":"https:\/\/prohoster.info\/hu\/blog\/news\/vypusk-sistemy-mashinnogo-obucheniya-tensorflow-2-0","title":{"rendered":"A TensorFlow 2.0 g\u00e9pi tanul\u00e1si rendszer kiad\u00e1sa","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p><noindex><a rel=\"nofollow\" href=\"https:\/\/medium.com\/tensorflow\/tensorflow-2-0-is-now-available-57d706c2a9ab\">Bemutatva<\/a><\/noindex> jelent\u0151s g\u00e9pi tanul\u00e1si platform kiad\u00e1sa <noindex><a rel=\"nofollow\" href=\"http:\/\/tensorflow.org\/\">TensorFlow 2.0<\/a><\/noindex>, amely k\u00e9sz megval\u00f3s\u00edt\u00e1sokat k\u00edn\u00e1l k\u00fcl\u00f6nb\u00f6z\u0151 m\u00e9ly g\u00e9pi tanul\u00e1si algoritmusokhoz, egyszer\u0171 programoz\u00e1si interf\u00e9szt biztos\u00edt modellek l\u00e9trehoz\u00e1s\u00e1ra Python nyelven \u00e9s alacsony szint\u0171 interf\u00e9szt C++ nyelven, amely lehet\u0151v\u00e9 teszi a sz\u00e1m\u00edt\u00e1si grafikonok fel\u00e9p\u00edt\u00e9s\u00e9nek \u00e9s v\u00e9grehajt\u00e1s\u00e1nak kezel\u00e9s\u00e9t. A rendszer k\u00f3dja C++ \u00e9s Python nyelveken \u00edr\u00f3dott \u00e9s <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/tensorflow\/tensorflow\">terjeszt\u00e9sre ker\u00fcl<\/a><\/noindex> az Apache licenc alatt \u00e9rhet\u0151 el. <\/p>\n<p>A platformot eredetileg a Google Brain csapata fejlesztette ki \u00e9s haszn\u00e1lj\u00e1k a Google szolg\u00e1ltat\u00e1saiban besz\u00e9dfelismer\u00e9shez, arcfelismer\u00e9shez f\u00e9nyk\u00e9peken, k\u00e9pek hasonl\u00f3s\u00e1g\u00e1nak meghat\u00e1roz\u00e1s\u00e1hoz, spam sz\u0171r\u00e9shez a Gmailben, <noindex><a rel=\"nofollow\" href=\"https:\/\/www.blog.google\/products\/news\/new-google-news-ai-meets-human-intelligence\/\">h\u00edrek kiv\u00e1laszt\u00e1s\u00e1hoz a Google News-ban \u00e9s a jelent\u00e9s alap\u00fa ford\u00edt\u00e1s megszervez\u00e9s\u00e9hez. Elosztott g\u00e9pi tanul\u00e1si rendszerek \u00e9p\u00edthet\u0151k tipikus hardveren, k\u00f6sz\u00f6nhet\u0151en a TensorFlow be\u00e9p\u00edtett t\u00e1mogat\u00e1s\u00e1nak a sz\u00e1m\u00edt\u00e1sok t\u00f6bb CPU vagy GPU k\u00f6z\u00f6tti megoszt\u00e1s\u00e1hoz.<\/a><\/noindex> A TensorFlow egy k\u00e9sz sz\u00e1m\u00edt\u00e1si algoritmus k\u00f6nyvt\u00e1rat biztos\u00edt, amelyet adatfolyam grafikonokon (data flow graphs) kereszt\u00fcl val\u00f3s\u00edtanak meg. Az ilyen grafikonok csom\u00f3pontjai matematikai m\u0171veleteket vagy bemeneti\/kimeneti pontokat val\u00f3s\u00edtanak meg, m\u00edg a grafikon \u00e9lei t\u00f6bbdimenzi\u00f3s adat-t\u00f6mb\u00f6ket (tenzorokat) k\u00e9pviselnek, amelyek a csom\u00f3pontok k\u00f6z\u00f6tt \u00e1ramlanak. <\/p>\n<p>A csom\u00f3pontok hozz\u00e1rendelhet\u0151k sz\u00e1m\u00edt\u00e1si eszk\u00f6z\u00f6kh\u00f6z \u00e9s a sz\u00e1m\u00edt\u00e1sokat aszinkron m\u00f3don hajthatj\u00e1k v\u00e9gre, p\u00e1rhuzamosan feldolgozva az \u00f6sszes hozz\u00e1juk illeszked\u0151 tenzort, ami lehet\u0151v\u00e9 teszi a csom\u00f3pontok egyidej\u0171 m\u0171k\u00f6d\u00e9s\u00e9t egy neur\u00e1lis h\u00e1l\u00f3zatban, hasonl\u00f3an az agy neuronjainek egyidej\u0171 aktiv\u00e1l\u00e1s\u00e1hoz.<br \/>\nAz \u00faj verzi\u00f3 el\u0151k\u00e9sz\u00edt\u00e9se sor\u00e1n a f\u0151 figyelem a egyszer\u0171s\u00edt\u00e9sre \u00e9s a haszn\u00e1lat k\u00f6nnyeds\u00e9g\u00e9re ir\u00e1nyult.<\/p>\n<p>A modellek fel\u00e9p\u00edt\u00e9s\u00e9re \u00e9s betan\u00edt\u00e1s\u00e1ra egy \u00faj, magas szint\u0171 API-t aj\u00e1nlanak <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/tensorflow\/tensorflow\/releases\/tag\/v2.0.0\">N\u00e9h\u00e1ny<\/a><\/noindex> <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/effective_tf2\">\u00fajdons\u00e1gok<\/a><\/noindex>:<\/p>\n<ul>\n<li class=\"l\"> Keras <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/beta\/guide\/keras\/overview\">, amely t\u00f6bb interf\u00e9szv\u00e1ltozatot biztos\u00edt a modellek fel\u00e9p\u00edt\u00e9s\u00e9hez (Szekvenci\u00e1lis, Funkcion\u00e1lis, Aloszt\u00e1lyoz\u00e1s), lehet\u0151v\u00e9 t\u00e9ve azok<\/a><\/noindex>azonnali v\u00e9grehajt\u00e1s\u00e1t <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/eager\">(el\u0151zetes ford\u00edt\u00e1s n\u00e9lk\u00fcl) \u00e9s egy egyszer\u0171 hibakeres\u00e9si mechanizmust;<\/a><\/noindex> tf.distribute.Strategy\n<li class=\"l\"> Hozz\u00e1adva az API <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/beta\/guide\/distribute_strategy\">a<\/a><\/noindex> modellek elosztott tanul\u00e1s\u00e1nak megszervez\u00e9s\u00e9re a megl\u00e9v\u0151 k\u00f3d minim\u00e1lis m\u00f3dos\u00edt\u00e1s\u00e1val. A sz\u00e1m\u00edt\u00e1sok t\u00f6bb GPU-ra val\u00f3 megoszt\u00e1s\u00e1nak lehet\u0151s\u00e9ge mellett k\u00eds\u00e9rleti t\u00e1mogat\u00e1st k\u00edn\u00e1lnak a tanul\u00e1si folyamat megoszt\u00e1s\u00e1ra t\u00f6bb f\u00fcggetlen feldolgoz\u00f3val \u00e9s a felh\u0151alap\u00fa <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/distributed_training\">TPU<\/a><\/noindex> (Tensorfeldolgoz\u00f3 egys\u00e9g) bevon\u00e1s\u00e1ra; <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/gpu\">t\u00f6bb GPU<\/a><\/noindex>, el\u00e9rhet\u0151 k\u00eds\u00e9rleti t\u00e1mogat\u00e1s a tanul\u00e1si folyamatok t\u00f6bb f\u00fcggetlen feldolgoz\u00f3egys\u00e9gre t\u00f6rt\u00e9n\u0151 eloszt\u00e1s\u00e1ra \u00e9s a felh\u0151alap\u00fa eszk\u00f6z\u00f6k bevon\u00e1s\u00e1ra <noindex><a rel=\"nofollow\" href=\"https:\/\/ru.wikipedia.org\/wiki\/%D0%A2%D0%B5%D0%BD%D0%B7%D0%BE%D1%80%D0%BD%D1%8B%D0%B9_%D0%BF%D1%80%D0%BE%D1%86%D0%B5%D1%81%D1%81%D0%BE%D1%80_Google\">TPU<\/a><\/noindex> (Tensor feldolgoz\u00f3 egys\u00e9g);\n<li class=\"l\"> A deklarat\u00edv gr\u00e1f\u00e9p\u00edt\u00e9si modell helyett, amelyet tf.Session seg\u00edts\u00e9g\u00e9vel hajtottak v\u00e9gre, lehet\u0151s\u00e9g van norm\u00e1l Python-funkci\u00f3k \u00edr\u00e1s\u00e1ra, amelyek tf.function h\u00edv\u00e1s seg\u00edts\u00e9g\u00e9vel gr\u00e1fokk\u00e1 alak\u00edthat\u00f3k, \u00e9s ezeket t\u00e1volr\u00f3l v\u00e9grehajthatj\u00e1k, sorozatba \u00e1ll\u00edthatj\u00e1k vagy optimaliz\u00e1lhatj\u00e1k a teljes\u00edtm\u00e9ny n\u00f6vel\u00e9se \u00e9rdek\u00e9ben;\n<li class=\"l\"> Ford\u00edt\u00f3 hozz\u00e1adva <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/function\">AutoGraph<\/a><\/noindex>, amely a Python parancsok folyam\u00e1t TensorFlow kifejez\u00e9sekk\u00e9 alak\u00edtja, lehet\u0151v\u00e9 t\u00e9ve a Python k\u00f3d haszn\u00e1lat\u00e1t tf.function dekor\u00e1l\u00e1s\u00fa funkci\u00f3kban, tf.data, tf.distribute \u00e9s tf.keras belsej\u00e9ben;\n<li class=\"l\"> A SavedModel egys\u00e9ges\u00edti a modellek cser\u00e9j\u00e9nek form\u00e1tum\u00e1t, \u00e9s t\u00e1mogat\u00e1st kapott a modellek \u00e1llapot\u00e1nak ment\u00e9s\u00e9hez \u00e9s vissza\u00e1ll\u00edt\u00e1s\u00e1hoz. A TensorFlow sz\u00e1m\u00e1ra gy\u0171jt\u00f6tt modellek most felhaszn\u00e1lhat\u00f3k a <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/lite\">TensorFlow Lite<\/a><\/noindex> (mobil eszk\u00f6z\u00f6k\u00f6n), <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/js\">TensorFlow JS<\/a><\/noindex>  (b\u00f6ng\u00e9sz\u0151ben vagy Node.js-ben), <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/tensorflow\/serving\">TensorFlow Serving<\/a><\/noindex> \u00e9s <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/hub\">TensorFlow Hub<\/a><\/noindex>;\n<li class=\"l\"> Az tf.train.Optimizers \u00e9s tf.keras.Optimizers API-k egys\u00e9ges\u00edtve lettek, a compute_gradients helyett egy \u00faj oszt\u00e1lyt javasoltak  <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/GradientTape\">GradientTape<\/a><\/noindex>;\n<li class=\"l\"> Jelent\u0151sen megn\u00f6velt\u00e9k a teljes\u00edtm\u00e9nyt GPU haszn\u00e1lata eset\u00e9n.<br \/>\nA modellek tanul\u00e1si sebess\u00e9ge NVIDIA Volta \u00e9s Turing rendszereken ak\u00e1r h\u00e1romszoros\u00e1ra n\u0151tt;<\/p>\n<li class=\"l\"> <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/effective_tf2\">Elv\u00e9gezve<\/a><\/noindex> az API jelent\u0151s tiszt\u00edt\u00e1sa, sok h\u00edv\u00e1s \u00e1tnevez\u00e9sre ker\u00fclt vagy elt\u00e1vol\u00edt\u00e1sra, a glob\u00e1lis v\u00e1ltoz\u00f3k t\u00e1mogat\u00e1sa a seg\u00e9dmet\u00f3dusokban megsz\u0171nt. A tf.app, tf.flags, tf.logging helyett egy \u00faj API-t javasoltak, az absl-py-t. A r\u00e9gi API folytat\u00e1s\u00e1hoz a compat.v1 modult k\u00e9sz\u00edtett\u00e9k el\u0151.\n<\/ul>\n<p><noindex><a rel=\"nofollow\" name=\"link\"><\/a><\/noindex><\/p>\n<p>Forr\u00e1s: <a \ncontent=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/www.opennet.ru\/opennews\/art.shtml?num=51595\">opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u041f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d \u0437\u043d\u0430\u0447\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0439 \u0432\u044b\u043f\u0443\u0441\u043a \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u044b \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f TensorFlow 2.0, \u043f\u0440\u0435\u0434\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u044e\u0449\u0435\u0439 \u0433\u043e\u0442\u043e\u0432\u044b\u0435 \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0438 \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u0445 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u043e\u0432 \u0433\u043b\u0443\u0431\u043e\u043a\u043e\u0433\u043e \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f, \u043f\u0440\u043e\u0441\u0442\u043e\u0439 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u043d\u044b\u0439 \u0438\u043d\u0442\u0435\u0440\u0444\u0435\u0439\u0441 \u0434\u043b\u044f \u043f\u043e\u0441\u0442\u0440\u043e\u0435\u043d\u0438\u044f \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u043d\u0430 \u044f\u0437\u044b\u043a\u0435 Python \u0438 \u043d\u0438\u0437\u043a\u043e\u0443\u0440\u043e\u0432\u043d\u0435\u0432\u044b\u0439 \u0438\u043d\u0442\u0435\u0440\u0444\u0435\u0439\u0441 \u0434\u043b\u044f \u044f\u0437\u044b\u043a\u0430 \u0421++, \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u044e\u0449\u0438\u0439 \u0443\u043f\u0440\u0430\u0432\u043b\u044f\u0442\u044c \u043f\u043e\u0441\u0442\u0440\u043e\u0435\u043d\u0438\u0435\u043c \u0438 \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u0435\u043c \u0432\u044b\u0447\u0438\u0441\u043b\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0445 \u0433\u0440\u0430\u0444\u043e\u0432. \u041a\u043e\u0434 \u0441\u0438\u0441\u0442\u0435\u043c\u044b \u043d\u0430\u043f\u0438\u0441\u0430\u043d \u043d\u0430 \u044f\u0437\u044b\u043a\u0430\u0445 \u0421++ \u0438 Python \u0438 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u044f\u0435\u0442\u0441\u044f \u043f\u043e\u0434 \u043b\u0438\u0446\u0435\u043d\u0437\u0438\u0435\u0439 Apache. \u041f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u0430 \u0438\u0437\u043d\u0430\u0447\u0430\u043b\u044c\u043d\u043e \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0430\u043d\u0430 \u043a\u043e\u043c\u0430\u043d\u0434\u043e\u0439 [&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":[702],"tags":[],"class_list":["post-38573","post","type-post","status-publish","format-standard","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.3 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u041f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d \u0437\u043d\u0430\u0447\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0439 \u0432\u044b\u043f\u0443\u0441\u043a \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u044b \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f\" \/>\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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