{"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\/sv\/blog\/news\/vypusk-sistemy-mashinnogo-obucheniya-tensorflow-2-0","title":{"rendered":"Utg\u00e5van av maskininl\u00e4rningssystemet 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\">Presenteras<\/a><\/noindex> betydande lansering av maskininl\u00e4rningsplattformen <noindex><a rel=\"nofollow\" href=\"http:\/\/tensorflow.org\/\">TensorFlow 2.0<\/a><\/noindex>, som erbjuder en klar implementation av olika algoritmer f\u00f6r djup maskininl\u00e4rning, ett enkelt programmeringsgr\u00e4nssnitt f\u00f6r modellering p\u00e5 Python och ett l\u00e5gniv\u00e5gr\u00e4nssnitt f\u00f6r C++, vilket m\u00f6jligg\u00f6r hantering av skapande och exekvering av ber\u00e4kningsgrafen. Systemets kod \u00e4r skriven i C++ och Python och <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/tensorflow\/tensorflow\">sprids<\/a><\/noindex> under Apache-licensen. <\/p>\n<p>plattformen utvecklades ursprungligen av Google Brain-teamet och anv\u00e4nds i Google-tj\u00e4nster f\u00f6r taligenk\u00e4nning, ansiktsigenk\u00e4nning p\u00e5 foton, bildlikhetsidentifiering, skr\u00e4ppostfiltrering i Gmail, <noindex><a rel=\"nofollow\" href=\"https:\/\/www.blog.google\/products\/news\/new-google-news-ai-meets-human-intelligence\/\">nyhetsurval<\/a><\/noindex> i Google News och organisations\u00f6vers\u00e4ttningar med h\u00e4nsyn till betydelse. Distribuerade maskininl\u00e4rningssystem kan skapas p\u00e5 standardutrustning tack vare inbyggt st\u00f6d i TensorFlow f\u00f6r att f\u00f6rdela ber\u00e4kningar \u00f6ver flera CPU:er eller GPU:er. <\/p>\n<p>TensorFlow tillhandah\u00e5ller ett bibliotek med f\u00e4rdiga algoritmer f\u00f6r numeriska ber\u00e4kningar, implementerade genom datastr\u00f6mmars graf (data flow graphs). Noder i s\u00e5dana grafer representerar matematiska operationer eller in-\/utg\u00e5ngspunkter, medan grafens kanter representerar flerdimensionella datam\u00e4ngder (tensorer) som fl\u00f6dar mellan noder.<br \/>\nNoder kan knytas till ber\u00e4kningsenheter och exekveras asynkront, vilket g\u00f6r att de kan bearbeta alla l\u00e4mpliga tensorer parallellt, vilket m\u00f6jligg\u00f6r samtidig aktivitet hos noderna i det neuronala n\u00e4tverket, likt samtidigt aktivering av neuroner i hj\u00e4rnan.<\/p>\n<p>Huvudfokus vid f\u00f6rberedelsen av den nya versionen var att f\u00f6renkla och \u00f6ka anv\u00e4ndarv\u00e4nligheten. <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/tensorflow\/tensorflow\/releases\/tag\/v2.0.0\">Vissa<\/a><\/noindex> <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/effective_tf2\">nyheter<\/a><\/noindex>:<\/p>\n<ul>\n<li class=\"l\"> F\u00f6r att bygga och tr\u00e4na modeller har en ny h\u00f6g niv\u00e5 API f\u00f6reslagits <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/beta\/guide\/keras\/overview\">Keras<\/a><\/noindex>, som erbjuder flera alternativ f\u00f6r gr\u00e4nssnitt f\u00f6r modellbyggande (Sequential, Functional, Subclassing) med m\u00f6jlighet till <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/eager\">omedelbar exekvering<\/a><\/noindex> (utan f\u00f6rkompilering) och med en enkel felavhj\u00e4lpningsmekanism;\n<li class=\"l\"> API har lagts till <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/beta\/guide\/distribute_strategy\">tf.distribute.Strategy<\/a><\/noindex> f\u00f6r organisering av <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/distributed_training\">distribuerad tr\u00e4ning<\/a><\/noindex> av modeller med minimal f\u00f6r\u00e4ndring av befintlig kod. F\u00f6rutom m\u00f6jligheten att f\u00f6rdela ber\u00e4kningar \u00f6ver <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/gpu\">flera GPU:er<\/a><\/noindex>, finns experimentellt st\u00f6d f\u00f6r att f\u00f6rdela tr\u00e4ningsprocessen \u00f6ver flera oberoende enheter och m\u00f6jlighet att anv\u00e4nda moln <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 processing unit);\n<li class=\"l\"> Ist\u00e4llet f\u00f6r en deklarativ modell f\u00f6r att bygga grafer med k\u00f6rning via tf.Session har m\u00f6jligheten att skriva vanliga funktioner i Python introducerats, vilka kan omvandlas till grafer med hj\u00e4lp av tf.function och sedan k\u00f6ras p\u00e5 distans, serialiseras eller optimeras f\u00f6r att \u00f6ka prestandan;\n<li class=\"l\"> \u00d6vers\u00e4ttare tillagd <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/function\">AutoGraph<\/a><\/noindex>, som omvandlar fl\u00f6det av Python-kommandon till TensorFlow-uttryck, vilket m\u00f6jligg\u00f6r anv\u00e4ndning av Python-kod inuti funktioner dekorerade med tf.function, tf.data, tf.distribute och tf.keras;\n<li class=\"l\"> I SavedModel har formatet f\u00f6r modellutbyte enhetliggjorts och st\u00f6d f\u00f6r att spara och \u00e5terst\u00e4lla modellernas tillst\u00e5nd har lagts till. Modeller som \u00e4r samlade f\u00f6r TensorFlow kan nu anv\u00e4ndas i <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/lite\">TensorFlow Lite<\/a><\/noindex> (p\u00e5 mobila enheter), <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/js\">TensorFlow JS<\/a><\/noindex>  (i webbl\u00e4saren eller Node.js), <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/tensorflow\/serving\">TensorFlow Serving<\/a><\/noindex> och <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/hub\">TensorFlow Hub<\/a><\/noindex>;\n<li class=\"l\"> API:erna tf.train.Optimizers och tf.keras.Optimizers har enhetliggjorts, ist\u00e4llet f\u00f6r compute_gradients f\u00f6r att ber\u00e4kna gradienter har en ny klass f\u00f6reslagits  <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/GradientTape\">GradientTape<\/a><\/noindex>;\n<li class=\"l\"> Prestandan har \u00f6kat avsev\u00e4rt vid anv\u00e4ndning av GPU.<br \/>\nInl\u00e4rningshastigheten f\u00f6r modeller p\u00e5 system med NVIDIA Volta- och Turing-GPU:er har \u00f6kat upp till tre g\u00e5nger;<\/p>\n<li class=\"l\"> <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/effective_tf2\">Genomf\u00f6rt<\/a><\/noindex> st\u00f6rre rensning av API:et, m\u00e5nga anrop har d\u00f6pts om eller tagits bort, st\u00f6det f\u00f6r globala variabler i hj\u00e4lpfunktioner har upph\u00f6rt. Ist\u00e4llet f\u00f6r tf.app, tf.flags, tf.logging har ett nytt API absl-py f\u00f6reslagits. F\u00f6r fortsatt anv\u00e4ndning av det gamla API:et har modulen compat.v1 f\u00f6rberetts.\n<\/ul>\n<p><noindex><a rel=\"nofollow\" name=\"link\"><\/a><\/noindex><\/p>\n<p>K\u00e4lla: <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 5.0.4 - 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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