{"id":14866,"date":"2018-05-22T06:25:02","date_gmt":"2018-05-22T06:25:02","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=14866"},"modified":"2021-05-14T11:00:18","modified_gmt":"2021-05-14T05:30:18","slug":"tensorflow-mobile","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/tensorflow-mobile\/","title":{"rendered":"TensorFlow Mobile | TensorFlow Lite: A Learning Solution"},"content":{"rendered":"<p><span style=\"font-weight: 400\">In this <strong>TensorFlow<\/strong> tutorial, we will see TensorFlow Mobile and TensorFlow Lite. First, we will see the meaning of TensorFlow Lite and TensorFlow Mobile, then we will move towards cases of using Mobile Machine learning. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Moreover, we will also see a comparison of TensorFlow Mobile vs TensorFlow Lite and the advantages of both mobile &amp; Lite in TensorFlow.<\/span><\/p>\n<p><span style=\"font-weight: 400\">We will be getting to know about the use of TensorFlow as a good <strong>deep learning<\/strong> solution in the field of mobile platforms. There are currently two ways of deploying<strong> machine learning<\/strong> applications on mobile devices; one is TensorFlow mobile and other is TensorFlow Lite.\u00a0<\/span><\/p>\n<p>So, let&#8217;s start TensorFlow Mobile and Lite Tutorial.<\/p>\n<h2><span style=\"font-weight: 400\">What is TensorFlow Mobile?<\/span><\/h2>\n<p><span style=\"font-weight: 400\">TensorFlow Mobile is used for a mobile platform such as iOS and Android. This is for those developers who have a successful TensorFlow model and want to integrate their model into a mobile environment. <\/span><\/p>\n<p><span style=\"font-weight: 400\">This is also for those who are not able to use TensorFlow Lite. Basic challenges one can find in integrating their desktop environment model into the mobile environment are:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">To see how to use TensorFlow mobile.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Building their model for a mobile platform.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Adding the <em>TensorFlow libraries<\/em> into their mobile application.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Preparing the model file.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Optimising binary size, file size, RAM usage etc.<\/span><\/li>\n<\/ul>\n<h2><span style=\"font-weight: 400\">Cases For Using Mobile Machine Learning<\/span><\/h2>\n<p><span style=\"font-weight: 400\">Generally, the developer\u2019s associated with TensorFlow use it on high powered <strong>GPU\u2019s<\/strong>. But it is very time consuming and a very expensive way to send all device data to across a network connection, running it on a mobile can be an easy way to do it.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Commonly used cases for on-device deep learning:<\/span><\/p>\n<div id=\"attachment_14871\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/Cases-for-Mobile-Machine-Learning-01.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-14871\" class=\"wp-image-14871 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/Cases-for-Mobile-Machine-Learning-01.jpg\" alt=\"TensorFlow Mobile\" width=\"1200\" height=\"628\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/Cases-for-Mobile-Machine-Learning-01.jpg 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/Cases-for-Mobile-Machine-Learning-01-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/Cases-for-Mobile-Machine-Learning-01-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/Cases-for-Mobile-Machine-Learning-01-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/Cases-for-Mobile-Machine-Learning-01-1024x536.jpg 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-14871\" class=\"wp-caption-text\">TensorFlow Mobile- Cases for Mobile Machine Learning<\/p><\/div>\n<h3><span style=\"font-weight: 400\">a. Image Recognition in TensorFlow<\/span><\/h3>\n<p><span style=\"font-weight: 400\">A useful way to detect or get a sense of the image captured with a mobile. If the users are taking photos getting to know what\u2019s in there can be a way to apply appropriate filters or label them so, as to find them whenever necessary. <\/span><\/p>\n<p><span style=\"font-weight: 400\"><strong>TensorFlow image recognition <\/strong>comes with a wide range of examples of detecting the types of objects inside of images. It also consists of a variety of pre-trained models which can be used to run on mobile devices.<\/span><\/p>\n<h3><span style=\"font-weight: 400\">b. TensorFlow Speech Recognition<\/span><\/h3>\n<p><span style=\"font-weight: 400\">There are various applications which can build with a speech-driven interface. Many times a user won\u2019t be giving instructions so streaming it continuously to a server would create a lot of problems.<\/span><\/p>\n<p><span style=\"font-weight: 400\"> To solve this, it\u2019s good to have a <strong>neural network<\/strong> running on a device for a particular word rather than listening to the whole conversation.<\/span><\/p>\n<h3><span style=\"font-weight: 400\">c. Gesture Recognition in TensorFlow<\/span><\/h3>\n<p><span style=\"font-weight: 400\">It is useful to control applications with the help of hands or other gestures, through analysing sensor data. You can do this with the help of TensorFlow.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Other examples are Optical Character Recognition (OCR), Translation, Text classification, Voice recognition etc.<\/span><\/p>\n<h2><span style=\"font-weight: 400\">What is TensorFlow Lite?<\/span><\/h2>\n<p><span style=\"font-weight: 400\">TensorFlow Lite is the lightweight version which is specifically designed for the mobile platform and embedded devices. It provides machine learning solution to mobile with low latency and small binary size.<\/span><\/p>\n<p><span style=\"font-weight: 400\">TensorFlow supports set of core operators which have been tuned for mobile platforms. It also supports custom operations in models.<\/span><\/p>\n<p><span style=\"font-weight: 400\">TensorFlow Lite tutorial defines a new file format based on FlatBuffers which is an open source platform serialization library. It consists of a new mobile interpreter which is used to keep apps small and faster. It uses a custom memory allocator for minimal load and execution latency.<\/span><\/p>\n<h3><span style=\"font-weight: 400\">a. TensorFlow Lite Architecture<\/span><\/h3>\n<div id=\"attachment_14867\" style=\"width: 1090px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/Architecture-1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-14867\" class=\"wp-image-14867 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/Architecture-1.png\" alt=\"TensorFlow Mobile\" width=\"1080\" height=\"1080\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/Architecture-1.png 1080w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/Architecture-1-150x150.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/Architecture-1-300x300.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/Architecture-1-768x768.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/Architecture-1-1024x1024.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/Architecture-1-100x100.png 100w\" sizes=\"auto, (max-width: 1080px) 100vw, 1080px\" \/><\/a><p id=\"caption-attachment-14867\" class=\"wp-caption-text\">TensorFlow Lite Architecture<\/p><\/div>\n<p><span style=\"font-weight: 400\">The above diagram you see is of TensorFlow Lite architecture. The trained TensorFlow model on the disk will convert into TensorFlow Lite file format (.tflite) using the TensorFlow Lite converter. Then we can use that converted file in the mobile application.<\/span><\/p>\n<p><span style=\"font-weight: 400\">For deploying Lite model file:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Java API:<\/strong> A wrapper around C++ API on Android.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>C++ API:<\/strong> It loads the Lite model and calls the interpreter.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Interpreter:<\/strong> It executes the model. It uses selective kernel loading which is a unique feature of Lite in Tensorflow. <\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">You can also implement custom kernels using the C++ API.<\/span><br \/>\nSome of the highlights of TensorFlow Lite are as follows:<\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">It supports a set of core operators which have been tuned for mobile platforms. TensorFlow also supports custom operations in models.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">A new file format based on FlatBuffers.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">On device interpreter which uses selective loading technique.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">When all supported operators are linked TensorFlow Lite is smaller than 300kb.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Java<\/strong> and C++ API support.<\/span><\/li>\n<\/ul>\n<h2><span style=\"font-weight: 400\">TensorFlow Lite Vs TensorFlow Mobile<\/span><\/h2>\n<p><span style=\"font-weight: 400\">As you saw what TensorFlow Lite and TensorFlow Mobile are, and how they support TensorFlow in a mobile environment and in embedded systems, you will know how they differ from each other. The differences between TensorFlow Lite and TensorFlow Mobile are as follows:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">It is the next version of TensorFlow mobile. Generally, applications developed on TensorFlow Lite will have better performance and less binary file size than TensorFlow mobile.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">It is still in early stages so not all the cases cover which is not the case for TensorFlow mobile.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">TensorFlow Lite supports selective sets of operators, therefore not all the models will work on TensorFlow Lite by default. Whereas, TensorFlow mobile has fully covered functionality.<\/span><\/li>\n<\/ul>\n<p>So, this was all about TensorFlow Mobile and TensorFlow Lite. Hope you like our explanation.<\/p>\n<h2><span style=\"font-weight: 400\">Conclusion<\/span><\/h2>\n<p><span style=\"font-weight: 400\">Hence, in this TensorFlow mobile and Lite tutorial, we discussed what is TensorFlow Mobile and TensorFlow Lite are. Moreover, we saw different cases for Mobile Machine Learning, such as Image recognition, speech recognition, Gesture recognition. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Lastly, we discussed TensorFlow architecture and also a comparison of TensorFlow Mobile and TensorFlow Lite. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Finally, we conclude, Lite in TensorFlow has a better performance ratio and small binary file size than its predecessor TensorFlow Mobile. Furthermore, if you have any query, feel free to ask in the comment section.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this TensorFlow tutorial, we will see TensorFlow Mobile and TensorFlow Lite. First, we will see the meaning of TensorFlow Lite and TensorFlow Mobile, then we will move towards cases of using Mobile Machine&#46;&#46;&#46;<\/p>\n","protected":false},"author":6,"featured_media":16379,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[73],"tags":[5073,6468,8431,8761,8764,8765,13189,14569,14570,14577,14578,16015,16016],"class_list":["post-14866","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tensorflow","tag-gesture-recognition","tag-image-recognition","tag-machine-learning","tag-mobile-environment","tag-mobile-machine-learning","tag-mobile-platforms","tag-speech-recognition","tag-tensorflow-lite","tag-tensorflow-lite-architecture","tag-tensorflow-mobile","tag-tensorflow-mobile-vs-tensorflow-lite","tag-what-is-tensorflow-lite","tag-what-is-tensorflow-mobile"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>TensorFlow Mobile | TensorFlow Lite: A Learning Solution - 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