{"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\/novosti-interneta\/vypusk-sistemy-mashinnogo-obucheniya-tensorflow-2-0","title":{"rendered":"Uscita del sistema di apprendimento automatico 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> lancio significativo della piattaforma di machine learning <noindex><a rel=\"nofollow\" href=\"http:\/\/tensorflow.org\/\">TensorFlow 2.0<\/a><\/noindex>, che offre implementazioni pronte di vari algoritmi di deep learning, un'interfaccia di programmazione semplice per costruire modelli in Python e un'interfaccia a basso livello per C++, consentendo 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\">distribuito<\/a><\/noindex> \u00e8 sotto licenza Apache. <\/p>\n<p>La piattaforma \u00e8 stata originariamente sviluppata dal team di Google Brain ed \u00e8 utilizzata nei servizi Google per il riconoscimento vocale, l'identificazione dei volti nelle fotografie, il riconoscimento delle somiglianze tra immagini, il filtraggio 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 delle traduzioni 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 offre una libreria di algoritmi predefiniti per il calcolo numerico, implementati tramite grafi di flusso di dati. I nodi in questi grafi eseguono operazioni matematiche o fungono da 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 associati a dispositivi di calcolo e operare in modo asincrono, elaborando contemporaneamente tutti i tensori ad essi associati, permettendo cos\u00ec un funzionamento simultaneo dei nodi in una rete neurale, simile all'attivazione simultanea dei neuroni nel cervello.<\/p>\n<p>Nella preparazione della nuova versione, si \u00e8 posta particolare attenzione alla semplicit\u00e0 e alla 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\"> \u00c8 stato proposto un nuovo API di alto livello per la costruzione e l'addestramento di modelli <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/beta\/guide\/keras\/overview\">Keras<\/a><\/noindex>, che offre diverse opzioni di interfaccia per la costruzione dei modelli (Sequential, Functional, Subclassing) con la possibilit\u00e0 di <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/eager\">esecuzione immediata<\/a><\/noindex> (senza compilazione preventiva) e con un semplice meccanismo di debug;\n<li class=\"l\"> Aggiunto 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'apprendimento distribuito<\/a><\/noindex> modelli con minime modifiche al 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 un supporto sperimentale per la distribuzione del processo di apprendimento su pi\u00f9 gestori indipendenti e la possibilit\u00e0 di utilizzare <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 del modello dichiarativo per la costruzione del grafo eseguito tramite tf.Session, \u00e8 ora possibile scrivere normali funzioni in Python, che attraverso la chiamata a tf.function possono essere convertite in grafi e quindi eseguite, serializzate o ottimizzate per aumentare le prestazioni;\n<li class=\"l\"> Aggiunto il traduttore <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/function\">AutoGraph<\/a><\/noindex>, che traduce il flusso di comandi Python in espressioni TensorFlow, consentendo di utilizzare il codice Python all'interno di funzioni decorate con tf.function, tf.data, tf.distribute e tf.keras;\n<li class=\"l\"> Nel SavedModel \u00e8 stato uniformato il formato di scambio dei modelli e aggiunto il supporto per il salvataggio e il ripristino dello stato dei modelli. I modelli creati 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\"> Le API unificati tf.train.Optimizers e tf.keras.Optimizers, invece di compute_gradients per calcolare i gradienti, offrono una nuova classe.  <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/GradientTape\">GradientTape<\/a><\/noindex>;\n<li class=\"l\"> Le prestazioni sono state significativamente aumentate durante l'uso della GPU.<br \/>\nLa velocit\u00e0 di allenamento dei modelli su 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\">\u00c8 stato effettuato<\/a><\/noindex> pulizia significativa dell'API, molte chiamate sono state rinominate o rimosse, interrotta la supporto alle variabili globali nei metodi ausiliari. Invece di tf.app, tf.flags, tf.logging \u00e8 stato proposto un nuovo API absl-py. Un modulo compat.v1 \u00e8 stato preparato per continuare a utilizzare l'old API.\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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[&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-novosti-interneta"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 4.9.10 - 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 TensorFlow 2.0, \u043f\u0440\u0435\u0434\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u044e\u0449\u0435\u0439 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