{"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\/it\/blog\/news\/vypusk-sistemy-mashinnogo-obucheniya-tensorflow-2-0","title":{"rendered":"Pubblicazione del sistema di machine learning 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\">Presentato<\/a><\/noindex> un'importante rilascio della piattaforma di machine learning <noindex><a rel=\"nofollow\" href=\"http:\/\/tensorflow.org\/\">TensorFlow 2.0<\/a><\/noindex>, che fornisce implementazioni pronte di vari algoritmi di machine learning profondo, un'interfaccia software semplice per costruire modelli in Python e un'interfaccia a basso livello per C++, che consente di gestire la costruzione e l'esecuzione di grafi computazionali. Il codice del sistema \u00e8 scritto in C++ e Python e <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/tensorflow\/tensorflow\">\u00e8 distribuito<\/a><\/noindex> \u00e8 sotto licenza Apache. <\/p>\n<p>la piattaforma \u00e8 stata originariamente sviluppata dal team Google Brain e viene utilizzata nei servizi Google per il riconoscimento vocale, l'identificazione dei volti nelle fotografie, la determinazione della somiglianza delle immagini, la filtrazione dello spam in Gmail, <noindex><a rel=\"nofollow\" href=\"https:\/\/www.blog.google\/products\/news\/new-google-news-ai-meets-human-intelligence\/\">la selezione<\/a><\/noindex> di notizie in Google News e l'organizzazione della traduzione tenendo conto del significato. I sistemi distribuiti di machine learning possono essere creati su hardware standard, grazie al supporto integrato in TensorFlow per la distribuzione dei calcoli su pi\u00f9 CPU o GPU. <\/p>\n<p>TensorFlow fornisce una libreria di algoritmi di calcolo numerico pronti, realizzati tramite grafi di flusso dei dati (data flow graphs). I nodi in questi grafi implementano operazioni matematiche o punti di ingresso\/uscita, mentre i bordi del grafo rappresentano array multidimensionali di dati (tensori) che fluiscono tra i nodi.<br \/>\nI nodi possono essere assegnati a dispositivi di calcolo e vengono eseguiti in modo asincrono, elaborando simultaneamente tutti i tensori ad essi associati, consentendo un lavoro simultaneo dei nodi in una rete neurale, in analogia con l'attivazione simultanea dei neuroni nel cervello.<\/p>\n<p>L'attenzione principale nella preparazione della nuova versione \u00e8 stata sulla semplificazione e facilit\u00e0 d'uso. <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/tensorflow\/tensorflow\/releases\/tag\/v2.0.0\">Alcuni<\/a><\/noindex> <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/effective_tf2\">novit\u00e0<\/a><\/noindex>:<\/p>\n<ul>\n<li class=\"l\"> Per la costruzione e l'allenamento dei modelli \u00e8 stato proposto un nuovo API di alto livello <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/beta\/guide\/keras\/overview\">Keras<\/a><\/noindex>, che fornisce diverse opzioni di interfaccia per costruire modelli (Sequential, Functional, Subclassing) con la possibilit\u00e0 di <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/eager\">una loro esecuzione immediata<\/a><\/noindex> (senza compilazione preliminare) e con un semplice meccanismo di debug;\n<li class=\"l\"> \u00c8 stato aggiunto l'API <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/beta\/guide\/distribute_strategy\">tf.distribute.Strategy<\/a><\/noindex> per organizzare <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/distributed_training\">l'addestramento distribuito<\/a><\/noindex> dei modelli con una minima modifica del codice esistente. Oltre alla possibilit\u00e0 di distribuire i calcoli su <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/gpu\">pi\u00f9 GPU<\/a><\/noindex>, \u00e8 disponibile il supporto sperimentale per la distribuzione del processo di addestramento su pi\u00f9 gestori indipendenti e la possibilit\u00e0 di utilizzare le <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> (unit\u00e0 di elaborazione dei tensori);\n<li class=\"l\"> Invece di un modello dichiarativo per la costruzione di grafi eseguiti tramite tf.Session, \u00e8 stata fornita la possibilit\u00e0 di scrivere normali funzioni in Python, che possono essere trasformate in grafi e poi eseguite, serializzate o ottimizzate tramite la chiamata a tf.function per migliorare le prestazioni;\n<li class=\"l\"> Aggiunto il traduttore <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/function\">AutoGraph<\/a><\/noindex>, che trasforma il flusso di comandi Python in espressioni TensorFlow, consentendo di utilizzare il codice Python all'interno di funzioni decorate da tf.function, tf.data, tf.distribute e tf.keras;\n<li class=\"l\"> Nel SavedModel, il formato di scambio dei modelli \u00e8 stato uniformato e supportata la salvataggio e il ripristino dello stato dei modelli. I modelli costruiti per TensorFlow possono ora essere utilizzati in <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/lite\">TensorFlow Lite<\/a><\/noindex> (su dispositivi mobili), <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/js\">TensorFlow JS<\/a><\/noindex>  (nel browser o in 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\"> Uniformati gli API tf.train.Optimizers e tf.keras.Optimizers, al posto di compute_gradients per il calcolo dei gradienti \u00e8 stata proposta una nuova classe  <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/GradientTape\">GradientTape<\/a><\/noindex>;\n<li class=\"l\"> Migliorate significativamente le prestazioni nell'uso della GPU.<br \/>\nLa velocit\u00e0 di apprendimento dei modelli sui sistemi con GPU NVIDIA Volta e Turing \u00e8 aumentata fino a tre volte;<\/p>\n<li class=\"l\"> <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/effective_tf2\">Condotta<\/a><\/noindex> pulizia generale dell'API, molte chiamate sono state rinominate o rimosse, e il supporto per le variabili globali nei metodi ausiliari \u00e8 stato interrotto. Invece di tf.app, tf.flags, tf.logging \u00e8 stata proposta una nuova API absl-py. Per continuare a utilizzare la vecchia API, \u00e8 stato preparato un modulo 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 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\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\" 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