{"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\/pt\/blog\/news\/vypusk-sistemy-mashinnogo-obucheniya-tensorflow-2-0","title":{"rendered":"Lan\u00e7amento do sistema de aprendizado de m\u00e1quina 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\">Apresentado<\/a><\/noindex> lan\u00e7amento significativo da plataforma de aprendizado de m\u00e1quina <noindex><a rel=\"nofollow\" href=\"http:\/\/tensorflow.org\/\">TensorFlow 2.0<\/a><\/noindex>, fornecendo implementa\u00e7\u00f5es prontas de v\u00e1rios algoritmos de aprendizado profundo, uma interface de programa\u00e7\u00e3o simples para construir modelos em Python e uma interface de baixo n\u00edvel para C++, permitindo gerenciar a constru\u00e7\u00e3o e execu\u00e7\u00e3o de gr\u00e1ficos computacionais. O c\u00f3digo do sistema \u00e9 escrito em C++ e Python e <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/tensorflow\/tensorflow\">\u00e9 distribu\u00eddo<\/a><\/noindex> sob licen\u00e7a Apache. <\/p>\n<p>A plataforma foi inicialmente desenvolvida pela equipe do Google Brain e \u00e9 usada em servi\u00e7os do Google para reconhecimento de voz, detec\u00e7\u00e3o de rostos em fotos, determina\u00e7\u00e3o de semelhan\u00e7a de imagens, filtragem de spam no Gmail, <noindex><a rel=\"nofollow\" href=\"https:\/\/www.blog.google\/products\/news\/new-google-news-ai-meets-human-intelligence\/\">recomenda\u00e7\u00e3o<\/a><\/noindex> de not\u00edcias no Google News e organiza\u00e7\u00e3o de tradu\u00e7\u00f5es com base no contexto. Sistemas de aprendizado de m\u00e1quina distribu\u00eddos podem ser criados em hardware padr\u00e3o, gra\u00e7as ao suporte integrado no TensorFlow para distribuir computa\u00e7\u00f5es em v\u00e1rias CPUs ou GPUs. <\/p>\n<p>O TensorFlow fornece uma biblioteca de algoritmos prontos para c\u00e1lculos num\u00e9ricos, implementados atrav\u00e9s de gr\u00e1ficos de fluxos de dados (data flow graphs). Os n\u00f3s nesses gr\u00e1ficos realizam opera\u00e7\u00f5es matem\u00e1ticas ou pontos de entrada\/sa\u00edda, enquanto as arestas do gr\u00e1fico representam arrays multidimensionais de dados (tensores) que fluem entre os n\u00f3s.<br \/>\nOs n\u00f3s podem estar vinculados a dispositivos computacionais e executados de forma ass\u00edncrona, processando simultaneamente todos os tensores adequados a eles, permitindo a organiza\u00e7\u00e3o do trabalho simult\u00e2neo dos n\u00f3s em uma rede neural, assim como a ativa\u00e7\u00e3o simult\u00e2nea de neur\u00f4nios no c\u00e9rebro.<\/p>\n<p>O foco na prepara\u00e7\u00e3o da nova vers\u00e3o foi a simplifica\u00e7\u00e3o e a facilidade de uso. <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/tensorflow\/tensorflow\/releases\/tag\/v2.0.0\">Alguns<\/a><\/noindex> <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/effective_tf2\">novidades<\/a><\/noindex>:<\/p>\n<ul>\n<li class=\"l\"> Para construir e treinar modelos, foi proposto uma nova API de alto n\u00edvel <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/beta\/guide\/keras\/overview\">Keras<\/a><\/noindex>, que fornece v\u00e1rias op\u00e7\u00f5es de interfaces para constru\u00e7\u00e3o de modelos (Sequential, Functional, Subclassing) com a possibilidade de <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/eager\">execu\u00e7\u00e3o imediata<\/a><\/noindex> (sem compila\u00e7\u00e3o pr\u00e9via) e com um mecanismo de depura\u00e7\u00e3o simples;\n<li class=\"l\"> Um API foi adicionado <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/beta\/guide\/distribute_strategy\">tf.distribute.Strategy<\/a><\/noindex> para organizar <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/distributed_training\">o treinamento distribu\u00eddo<\/a><\/noindex> de modelos com m\u00ednima modifica\u00e7\u00e3o do c\u00f3digo existente. Al\u00e9m da possibilidade de distribuir computa\u00e7\u00f5es em <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/gpu\">v\u00e1rias GPUs<\/a><\/noindex>, h\u00e1 suporte experimental para distribuir o processo de treinamento em v\u00e1rios manipuladores independentes e a possibilidade de utilizar <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\">TPUs<\/a><\/noindex> (unidade de processamento de tensores);\n<li class=\"l\"> Em vez de um modelo declarativo de constru\u00e7\u00e3o de grafos com execu\u00e7\u00e3o atrav\u00e9s de tf.Session, foi possibilitada a escrita de fun\u00e7\u00f5es comuns na linguagem Python, que podem ser transformadas em grafos e, em seguida, executadas remotamente, serializadas ou otimizadas para melhorar o desempenho com o uso da chamada tf.function;\n<li class=\"l\"> Adicionado o tradutor <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/function\">AutoGraph<\/a><\/noindex>, que transforma o fluxo de comandos Python em express\u00f5es TensorFlow, permitindo o uso de c\u00f3digo em Python dentro de fun\u00e7\u00f5es decoradas com tf.function, tf.data, tf.distribute e tf.keras;\n<li class=\"l\"> No SavedModel, foi unificado o formato de troca de modelos e adicionada a compatibilidade para salvar e restaurar o estado dos modelos. Os modelos constru\u00eddos para TensorFlow agora podem ser utilizados em <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/lite\">TensorFlow Lite<\/a><\/noindex> (em dispositivos m\u00f3veis), <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/js\">TensorFlow JS<\/a><\/noindex>  (no navegador ou Node.js), <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/tensorflow\/serving\">TensorFlow Serving<\/a><\/noindex> e <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/hub\">TensorFlow Hub<\/a><\/noindex>;\n<li class=\"l\"> Unificados os APIs tf.train.Optimizers e tf.keras.Optimizers, ao inv\u00e9s de compute_gradients para calcular gradientes, foi proposto uma nova classe  <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/GradientTape\">GradientTape<\/a><\/noindex>;\n<li class=\"l\"> Significativamente aumentada a performance ao usar GPU.<br \/>\nA velocidade de aprendizado de modelos em sistemas com GPU NVIDIA Volta e Turing aumentou at\u00e9 tr\u00eas vezes;<\/p>\n<li class=\"l\"> <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/effective_tf2\">Realizada<\/a><\/noindex> realiza\u00e7\u00e3o de uma grande limpeza na API, muitos chamados foram renomeados ou removidos, o suporte a vari\u00e1veis globais em m\u00e9todos auxiliares foi descontinuado. Em vez de tf.app, tf.flags, tf.logging, foi proposto uma nova API absl-py. Para continuar usando a antiga API, foi preparado um m\u00f3dulo compat.v1.\n<\/ul>\n<p><noindex><a rel=\"nofollow\" name=\"link\"><\/a><\/noindex><\/p>\n<p>Fonte: <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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