{"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\/pl\/blog\/news\/vypusk-sistemy-mashinnogo-obucheniya-tensorflow-2-0","title":{"rendered":"Wydanie systemu uczenia maszynowego 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\">Przedstawiony<\/a><\/noindex> znacz\u0105ca wersja platformy uczenia maszynowego <noindex><a rel=\"nofollow\" href=\"http:\/\/tensorflow.org\/\">TensorFlow 2.0<\/a><\/noindex>, oferuj\u0105ca gotowe implementacje r\u00f3\u017cnych algorytm\u00f3w g\u0142\u0119bokiego uczenia maszynowego, prosty interfejs programistyczny do budowania modeli w j\u0119zyku Python oraz niski poziom interfejsu w j\u0119zyku C++, umo\u017cliwiaj\u0105cy zarz\u0105dzanie budow\u0105 i wykonywaniem graf\u00f3w obliczeniowych. Kod systemu napisany jest w j\u0119zykach C++ i Python i <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/tensorflow\/tensorflow\">rozpowszechniany<\/a><\/noindex> na licencji Apache. <\/p>\n<p>Platforma zosta\u0142a pierwotnie opracowana przez zesp\u00f3\u0142 Google Brain i jest wykorzystywana w us\u0142ugach Google do rozpoznawania mowy, wykrywania twarzy na zdj\u0119ciach, okre\u015blania podobie\u0144stwa obraz\u00f3w, filtrowania spamu w Gmailu, <noindex><a rel=\"nofollow\" href=\"https:\/\/www.blog.google\/products\/news\/new-google-news-ai-meets-human-intelligence\/\">dobierania<\/a><\/noindex> wiadomo\u015bci w Google News i organizowania t\u0142umacze\u0144 z uwzgl\u0119dnieniem kontekstu. Rozproszone systemy uczenia maszynowego mo\u017cna tworzy\u0107 na standardowym sprz\u0119cie, dzi\u0119ki wbudowanemu wsparciu w TensorFlow dla rozdzielania oblicze\u0144 na wiele CPU lub GPU. <\/p>\n<p>TensorFlow zapewnia bibliotek\u0119 gotowych algorytm\u00f3w oblicze\u0144 numerycznych, zrealizowanych za pomoc\u0105 graf\u00f3w przep\u0142ywu danych (data flow graphs). W\u0119z\u0142y w takich grafach realizuj\u0105 operacje matematyczne lub punkty wej\u015bcia\/wyj\u015bcia, podczas gdy kraw\u0119dzie grafu reprezentuj\u0105 wielowymiarowe tablice danych (tenzory), kt\u00f3re p\u0142yn\u0105 mi\u0119dzy w\u0119z\u0142ami.<br \/>\nW\u0119z\u0142y mog\u0105 by\u0107 przypisane do urz\u0105dze\u0144 obliczeniowych i wykonywane asynchronicznie, r\u00f3wnolegle przetwarzaj\u0105c wszystkie odpowiednie tenzory, co pozwala na r\u00f3wnoczesne dzia\u0142anie w\u0119z\u0142\u00f3w w sieci neuronowej, podobnie jak r\u00f3wnoczesna aktywacja neuron\u00f3w w m\u00f3zgu.<\/p>\n<p>G\u0142\u00f3wna uwaga podczas przygotowywania nowej wersji zosta\u0142a po\u015bwi\u0119cona uproszczeniu i \u0142atwo\u015bci u\u017cycia. <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/tensorflow\/tensorflow\/releases\/tag\/v2.0.0\">Niekt\u00f3re<\/a><\/noindex> <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/effective_tf2\">nowo\u015bci<\/a><\/noindex>:<\/p>\n<ul>\n<li class=\"l\"> Do budowy i treningu modeli zaproponowano nowy, wysokopoziomowy API <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/beta\/guide\/keras\/overview\">Keras<\/a><\/noindex>, oferuj\u0105cy kilka opcji interfejs\u00f3w do budowy modeli (Sequential, Functional, Subclassing) z mo\u017cliwo\u015bci\u0105 ich <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/eager\">natychmiastowego wykonania<\/a><\/noindex> (bez wcze\u015bniejszej kompilacji) i z prostym mechanizmem debugowania;\n<li class=\"l\"> Dodano API <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/beta\/guide\/distribute_strategy\">tf.distribute.Strategy<\/a><\/noindex> do organizacji <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/distributed_training\">rozproszonego uczenia<\/a><\/noindex> modeli z minimaln\u0105 zmian\u0105 istniej\u0105cego kodu. Opr\u00f3cz mo\u017cliwo\u015bci rozdzielania oblicze\u0144 na <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/gpu\">wiele GPU<\/a><\/noindex>, dost\u0119pne jest eksperymentalne wsparcie dla podzia\u0142u procesu uczenia na wiele niezale\u017cnych procesor\u00f3w i mo\u017cliwo\u015b\u0107 wykorzystania chmurowych <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> (jednostka przetwarzania tenzor\u00f3w);\n<li class=\"l\"> Zamiast deklaratywnego modelu budowy grafu z wykonaniem za pomoc\u0105 tf.Session wprowadzono mo\u017cliwo\u015b\u0107 pisania zwyk\u0142ych funkcji w j\u0119zyku Python, kt\u00f3re dzi\u0119ki wywo\u0142aniu tf.function mog\u0105 by\u0107 przekszta\u0142cane w grafy, a nast\u0119pnie wykonywane zdalnie, serializowane lub optymalizowane w celu zwi\u0119kszenia wydajno\u015bci;\n<li class=\"l\"> Dodano t\u0142umacz <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/function\">AutoGraph<\/a><\/noindex>, przekszta\u0142caj\u0105cy strumie\u0144 polece\u0144 Pythona w wyra\u017cenia TensorFlow, co pozwala na u\u017cycie kodu w j\u0119zyku Python wewn\u0105trz funkcji ozdobionych tf.function, tf.data, tf.distribute i tf.keras;\n<li class=\"l\"> W SavedModel ujednolicono format wymiany modeli oraz dodano wsparcie dla zapisywania i przywracania stanu modeli. Zbudowane dla TensorFlow modele mog\u0105 teraz by\u0107 u\u017cywane w <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/lite\">TensorFlow Lite<\/a><\/noindex> (na urz\u0105dzeniach mobilnych), <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/js\">TensorFlow JS<\/a><\/noindex>  (w przegl\u0105darce lub Node.js), <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/tensorflow\/serving\">TensorFlow Serving<\/a><\/noindex> i <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/hub\">TensorFlow Hub<\/a><\/noindex>;\n<li class=\"l\"> Ujednolicono API tf.train.Optimizers i tf.keras.Optimizers, zamiast compute_gradients do obliczania gradient\u00f3w zaproponowano now\u0105 klas\u0119  <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/GradientTape\">GradientTape<\/a><\/noindex>;\n<li class=\"l\"> Znacz\u0105co zwi\u0119kszona wydajno\u015b\u0107 przy u\u017cyciu GPU.<br \/>\nSzybko\u015b\u0107 uczenia si\u0119 modeli na systemach z GPU NVIDIA Volta i Turing wzros\u0142a do trzech razy;<\/p>\n<li class=\"l\"> <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/effective_tf2\">Przeprowadzono<\/a><\/noindex> du\u017ce porz\u0105dki w API, wiele wywo\u0142a\u0144 zosta\u0142o przemianowanych lub usuni\u0119tych, zaprzestano wsparcia dla zmiennych globalnych w metodach pomocniczych. Zamiast tf.app, tf.flags, tf.logging wprowadzono nowe API absl-py. Aby kontynuowa\u0107 korzystanie ze starego API, przygotowano modu\u0142 compat.v1.\n<\/ul>\n<p><noindex><a rel=\"nofollow\" name=\"link\"><\/a><\/noindex><\/p>\n<p>\u0179r\u00f3d\u0142o: <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 5.0.1.1 - 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\" href=\"https:\/\/prohoster.info\/pl\/blog\/news\/vypusk-sistemy-mashinnogo-obucheniya-tensorflow-2-0\" \/>\n\t<meta name=\"generator\" content=\"All in One SEO (AIOSEO) 5.0.1.1\" \/>\n\t\t<meta property=\"og:locale\" content=\"pl_PL\" \/>\n\t\t<meta property=\"og:site_name\" content=\"ProHoster | \u041a\u0443\u043f\u0438\u0442\u044c \u043d\u0430\u0434\u0435\u0436\u043d\u044b\u0439 \u0445\u043e\u0441\u0442\u0438\u043d\u0433 \u0434\u043b\u044f \u0441\u0430\u0439\u0442\u043e\u0432 \u0441 \u0437\u0430\u0449\u0438\u0442\u043e\u0439 \u043e\u0442 DDoS, VPS VDS \u0441\u0435\u0440\u0432\u0435\u0440\u044b\" \/>\n\t\t<meta property=\"og:type\" content=\"article\" \/>\n\t\t<meta property=\"og:title\" content=\"\ud83e\udd47\u0412\u044b\u043f\u0443\u0441\u043a \u0441\u0438\u0441\u0442\u0435\u043c\u044b \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f TensorFlow 2.0 | ProHoster\" \/>\n\t\t<meta property=\"og: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\t<meta property=\"og:url\" content=\"https:\/\/prohoster.info\/pl\/blog\/news\/vypusk-sistemy-mashinnogo-obucheniya-tensorflow-2-0\" \/>\n\t\t<meta property=\"og:image\" content=\"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg\" \/>\n\t\t<meta property=\"og:image:secure_url\" content=\"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg\" \/>\n\t\t<meta property=\"og:image:width\" content=\"350\" \/>\n\t\t<meta property=\"og:image:height\" content=\"350\" \/>\n\t\t<meta property=\"article:published_time\" content=\"2019-10-31T19:24:37+00:00\" \/>\n\t\t<meta property=\"article:modified_time\" content=\"2019-10-31T19:24:37+00:00\" \/>\n\t\t<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/prohoster\" \/>\n\t\t<meta property=\"article:author\" content=\"https:\/\/www.facebook.com\/prohoster\" \/>\n\t\t<!-- All in One SEO -->\n\n","aioseo_head_json":{"title":"\ud83e\udd47Wydanie systemu uczenia maszynowego TensorFlow 2.0 | ProHoster","description":"Wprowadzono znaczn\u0105 wersj\u0119 platformy uczenia maszynowego","canonical_url":"https:\/\/prohoster.info\/pl\/blog\/news\/vypusk-sistemy-mashinnogo-obucheniya-tensorflow-2-0","robots":"max-image-preview:large","keywords":"","webmasterTools":{"miscellaneous":""},"schema":null,"og:locale":"pl_PL","og:site_name":"ProHoster | \u041a\u0443\u043f\u0438\u0442\u044c \u043d\u0430\u0434\u0435\u0436\u043d\u044b\u0439 \u0445\u043e\u0441\u0442\u0438\u043d\u0433 \u0434\u043b\u044f \u0441\u0430\u0439\u0442\u043e\u0432 \u0441 \u0437\u0430\u0449\u0438\u0442\u043e\u0439 \u043e\u0442 DDoS, VPS VDS \u0441\u0435\u0440\u0432\u0435\u0440\u044b","og:type":"article","og:title":"\ud83e\udd47\u0412\u044b\u043f\u0443\u0441\u043a \u0441\u0438\u0441\u0442\u0435\u043c\u044b \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f TensorFlow 2.0 | ProHoster","og:description":"\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","og:url":"https:\/\/prohoster.info\/pl\/blog\/news\/vypusk-sistemy-mashinnogo-obucheniya-tensorflow-2-0","og:image":"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg","og:image:secure_url":"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg","og:image:width":350,"og:image:height":350,"article:published_time":"2019-10-31T19:24:37+00:00","article:modified_time":"2019-10-31T19:24:37+00:00","article:publisher":"https:\/\/www.facebook.com\/prohoster","article:author":"https:\/\/www.facebook.com\/prohoster"},"aioseo_meta_data":{"post_id":"38573","title":null,"description":null,"keywords":null,"keyphrases":null,"primary_term":null,"canonical_url":null,"og_title":null,"og_description":null,"og_object_type":"default","og_image_type":"default","og_image_url":null,"og_image_width":null,"og_image_height":null,"og_image_custom_url":null,"og_image_custom_fields":null,"og_video":null,"og_custom_url":null,"og_article_section":null,"og_article_tags":null,"twitter_use_og":false,"twitter_card":"default","twitter_image_type":"default","twitter_image_url":null,"twitter_image_custom_url":null,"twitter_image_custom_fields":null,"twitter_title":null,"twitter_description":null,"schema":{"blockGraphs":[],"customGraphs":[],"default":{"data":{"Article":[],"Course":[],"Dataset":[],"FAQPage":[],"Movie":[],"Person":[],"Product":[],"ProductReview":[],"Car":[],"Recipe":[],"Service":[],"SoftwareApplication":[],"WebPage":[]},"graphName":"","isEnabled":true},"graphs":[]},"schema_type":null,"schema_type_options":null,"pillar_content":false,"robots_default":true,"robots_noindex":false,"robots_noarchive":false,"robots_nosnippet":false,"robots_nofollow":false,"robots_noimageindex":false,"robots_noodp":false,"robots_notranslate":false,"robots_max_snippet":null,"robots_max_videopreview":null,"robots_max_imagepreview":"large","priority":null,"frequency":null,"local_seo":null,"seo_analyzer_scan_date":"2026-01-23 22:35:19","breadcrumb_settings":null,"limit_modified_date":false,"reviewed_by":null,"ai":null,"created":"2021-03-01 01:07:38","updated":"2026-01-23 22:35:19","focus_keyword":null,"additional_keywords":null,"truseo_locale":null},"gt_translate_keys":[{"key":"link","format":"url"}],"_links":{"self":[{"href":"https:\/\/prohoster.info\/pl\/wp-json\/wp\/v2\/posts\/38573","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/prohoster.info\/pl\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/prohoster.info\/pl\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/prohoster.info\/pl\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/prohoster.info\/pl\/wp-json\/wp\/v2\/comments?post=38573"}],"version-history":[{"count":0,"href":"https:\/\/prohoster.info\/pl\/wp-json\/wp\/v2\/posts\/38573\/revisions"}],"wp:attachment":[{"href":"https:\/\/prohoster.info\/pl\/wp-json\/wp\/v2\/media?parent=38573"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/prohoster.info\/pl\/wp-json\/wp\/v2\/categories?post=38573"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/prohoster.info\/pl\/wp-json\/wp\/v2\/tags?post=38573"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}