{"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\/ro\/blog\/news\/vypusk-sistemy-mashinnogo-obucheniya-tensorflow-2-0","title":{"rendered":"Lansarea sistemului de \u00eenv\u0103\u021bare automat\u0103 TensorFlow 2.0","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\">Prezentat<\/a><\/noindex> lansare semnificativ\u0103 a platformei de \u00eenv\u0103\u021bare automat\u0103 <noindex><a rel=\"nofollow\" href=\"http:\/\/tensorflow.org\/\">TensorFlow 2.0<\/a><\/noindex>, care ofer\u0103 implement\u0103ri gata f\u0103cute ale diverselor algoritmi de \u00eenv\u0103\u021bare profund\u0103, o interfa\u021b\u0103 de programare simpl\u0103 pentru construirea modelelor \u00een Python \u0219i o interfa\u021b\u0103 de nivel sc\u0103zut pentru C++, permi\u021b\u00e2nd gestionarea construirii \u0219i execu\u021biei grafurilor computa\u021bionale. Codul sistemului este scris \u00een limbajele C++ \u0219i Python \u0219i <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/tensorflow\/tensorflow\">se r\u0103sp\u00e2nde\u0219te<\/a><\/noindex> sub licen\u021ba Apache. <\/p>\n<p>Platforma a fost ini\u021bial dezvoltat\u0103 de echipa Google Brain \u0219i este folosit\u0103 \u00een serviciile Google pentru recunoa\u0219terea vorbirii, detectarea fe\u021belor \u00een fotografii, determinarea similitudinii imaginilor, filtrarea spamului \u00een Gmail, <noindex><a rel=\"nofollow\" href=\"https:\/\/www.blog.google\/products\/news\/new-google-news-ai-meets-human-intelligence\/\">selec\u021bia<\/a><\/noindex> \u0219tirilor \u00een Google News \u0219i organizarea traducerii av\u00e2nd \u00een vedere sensul. Sistemele distribuite de \u00eenv\u0103\u021bare automat\u0103 pot fi construite pe echipamente standard, datorit\u0103 suportului \u00eencorporat \u00een TensorFlow pentru dispersarea calculelor pe mai multe CPU sau GPU. <\/p>\n<p>TensorFlow ofer\u0103 o bibliotec\u0103 de algoritmi gata f\u0103cu\u021bi pentru calcule numerice, implementa\u021bi prin grafuri de flux de date (data flow graphs). Nodurile din aceste grafuri implementeaz\u0103 opera\u021bii matematice sau puncte de intrare\/ie\u0219ire, \u00een timp ce muchiile grafului reprezint\u0103 array-uri multidimensionale de date (tenzori) care curg \u00eentre noduri.<br \/>\nNodurile pot fi legate de dispozitive de calcul \u0219i pot fi executate asincron, proces\u00e2nd simultan toate tenzorii care se potrivesc cu ele, ceea ce permite organizarea activit\u0103\u021bii simultane a nodurilor \u00eentr-o re\u021bea neural\u0103, analog cu activarea simultan\u0103 a neuronilor \u00een creier.<\/p>\n<p>Aten\u021bia principal\u0103 \u00een preg\u0103tirea noii versiuni a fost acordat\u0103 simplific\u0103rii \u0219i u\u0219urin\u021bei de utilizare. <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/tensorflow\/tensorflow\/releases\/tag\/v2.0.0\">C\u00e2teva<\/a><\/noindex> <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/effective_tf2\">nout\u0103\u021bi<\/a><\/noindex>:<\/p>\n<ul>\n<li class=\"l\"> Pentru construirea \u0219i antrenarea modelelor a fost propus un nou API de nivel \u00eenalt <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/beta\/guide\/keras\/overview\">Keras<\/a><\/noindex>, care ofer\u0103 mai multe op\u021biuni de interfe\u021be pentru construirea modelelor (Secven\u021bial, Func\u021bional, Subclassing) cu posibilitatea de <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/eager\">executare imediat\u0103<\/a><\/noindex> (f\u0103r\u0103 compilare prealabil\u0103) \u0219i cu un mecanism simplu de depanare;\n<li class=\"l\"> A fost ad\u0103ugat API <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/beta\/guide\/distribute_strategy\">tf.distribute.Strategy<\/a><\/noindex> pentru organizarea <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/distributed_training\">\u00eenv\u0103\u021b\u0103rii distribuite<\/a><\/noindex> modelelor cu modific\u0103ri minime ale codului existent. Pe l\u00e2ng\u0103 posibilitatea de a dispersa calculele pe <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/gpu\">mai multe GPU<\/a><\/noindex>, este disponibil\u0103 o suport experimental pentru dispersarea procesului de \u00eenv\u0103\u021bare pe mai multe procesatoare independente \u0219i posibilitatea de a folosi <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> (unitate de procesare a tensului);\n<li class=\"l\"> \u00cen locul modelului declara\u021bional de construc\u021bie a graficului executat prin tf.Session, a fost introdus\u0103 posibilitatea de a scrie func\u021bii obi\u0219nuite \u00een limbajul Python, care prin apelarea tf.function pot fi transformate \u00een grafice \u0219i apoi executate la distan\u021b\u0103, serializate sau optimizate pentru a cre\u0219te performan\u021ba;\n<li class=\"l\"> A fost ad\u0103ugat traduc\u0103torul <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/function\">AutoGraph<\/a><\/noindex>, care transform\u0103 fluxul de comenzi Python \u00een expresii TensorFlow, ceea ce permite utilizarea codului \u00een limbajul Python \u00een cadrul func\u021biilor decorate cu tf.function, tf.data, tf.distribute \u0219i tf.keras;\n<li class=\"l\"> \u00cen SavedModel s-a unificat formatul de schimb al modelelor \u0219i a fost ad\u0103ugat suportul pentru salvarea \u0219i restaurarea st\u0103rii modelelor. Modelele construite pentru TensorFlow pot fi acum utilizate \u00een <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/lite\">TensorFlow Lite<\/a><\/noindex> (pe dispozitive mobile), <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/js\">TensorFlow JS<\/a><\/noindex>  (\u00een browser sau Node.js), <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/tensorflow\/serving\">TensorFlow Serving<\/a><\/noindex> \u0219i <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/hub\">TensorFlow Hub<\/a><\/noindex>;\n<li class=\"l\"> API-urile tf.train.Optimizers \u0219i tf.keras.Optimizers au fost unificate, \u00een loc de compute_gradients pentru calcularea gradientelor a fost propus\u0103 o nou\u0103 clas\u0103  <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/GradientTape\">GradientTape<\/a><\/noindex>;\n<li class=\"l\"> Performan\u021ba utiliz\u0103rii GPU-ului a crescut semnificativ.<br \/>\nRata de \u00eenv\u0103\u021bare a modelelor pe sisteme cu GPU NVIDIA Volta \u0219i Turing a crescut de p\u00e2n\u0103 la trei ori;<\/p>\n<li class=\"l\"> <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/effective_tf2\">Realizat<\/a><\/noindex> o cur\u0103\u021bare major\u0103 a API-ului, multe apeluri au fost redenumite sau eliminate, suportul pentru variabile globale \u00een metodele auxiliare a fost \u00eentrerupt. \u00cen loc de tf.app, tf.flags, tf.logging a fost propus un nou API absl-py. Pentru a continua utilizarea vechiului API, a fost preg\u0103tit modul compat.v1.\n<\/ul>\n<p><noindex><a rel=\"nofollow\" name=\"link\"><\/a><\/noindex><\/p>\n<p>Sursa: <a \ncontent=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/www.opennet.ru\/opennews\/art.shtml?num=51595\">opennet.ro<\/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 5.0.2.1 - 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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