{"id":14772,"date":"2018-05-03T07:11:00","date_gmt":"2018-05-03T07:11:00","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=14772"},"modified":"2021-05-14T11:00:27","modified_gmt":"2021-05-14T05:30:27","slug":"tensorflow-api","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/tensorflow-api\/","title":{"rendered":"TensorFlow API Documentation | Use Of TensorFlow API"},"content":{"rendered":"<div class='__iawmlf-post-loop-links' style='display:none;' data-iawmlf-post-links='[{&quot;id&quot;:1958,&quot;href&quot;:&quot;https:\\\/\\\/www.tensorflow.org\\\/api_docs\\\/python\\\/tf\\\/contrib\\\/learn&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20190328043026\\\/https:\\\/\\\/www.tensorflow.org\\\/api_docs\\\/python\\\/tf\\\/contrib\\\/learn&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-10 13:43:23&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2025-12-18 18:35:18&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2025-12-31 02:20:08&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-02-26 11:40:46&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-03-01 21:15:36&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-05-08 14:11:24&quot;,&quot;http_code&quot;:503}],&quot;broken&quot;:true,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-05-08 14:11:24&quot;,&quot;http_code&quot;:503},&quot;process&quot;:&quot;done&quot;}]'><\/div>\n<p>In our last <strong>TensorFlow tutorial<\/strong>, we discussed <strong>TensorFlow Pros and Cons<\/strong>. Today, we will see TensorFlow API Documentation. So, in this TensorFlow API tutorial, we will discuss the meaning of API in TensorFlow. Also, we will look at the use of TensorFlow API.<\/p>\n<p>So, let&#8217;s start TensorFlow API.<\/p>\n<h2><span style=\"font-weight: 400\">What is TensorFlow API?<\/span><\/h2>\n<p><span style=\"font-weight: 400\">TensorFlow is similar to a <strong>python package<\/strong> and a lot of features are similar to that of <strong>Python<\/strong>. But the main core of TensorFlow is \u2013 distributed runtime. This functionality implements in many languages and one of them is Python.<\/span><\/p>\n<div id=\"attachment_14774\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/TensorFlow-2.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-14774\" class=\"wp-image-14774 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/TensorFlow-2.png\" alt=\"TensorFlow API - TensorFlow Runtime Engine\" width=\"1200\" height=\"496\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/TensorFlow-2.png 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/TensorFlow-2-150x62.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/TensorFlow-2-300x124.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/TensorFlow-2-768x317.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/TensorFlow-2-1024x423.png 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-14774\" class=\"wp-caption-text\">TensorFlow API &#8211; TensorFlow Runtime Engine<\/p><\/div>\n<p><span style=\"font-weight: 400\">This is the diagram of TensorFlow\u2019s distributed Execution engine or the runtime engine. The other way to visualize the above diagram is to think of it as a virtual machine whose language is TensorFlow. <\/span><\/p>\n<p><span style=\"font-weight: 400\">The runtime of TensorFlow is written in C++ language but the frontend as you can see can be implemented by using various languages like C, C++, R, <strong>Java<\/strong> etc. The use of these API\u2019s in TensorFlow is explained below.<\/span><\/p>\n<h3><span style=\"font-weight: 400\">a. C API for TensorFlow<\/span><\/h3>\n<p><span style=\"font-weight: 400\">The only APIs having the official backing of TensorFlow are C and Python API (some parts). C APIs should be used whenever you are about to make TensorFlow API for some other languages as lots of languages have ways to connect with C language.<\/span><\/p>\n<h3><span style=\"font-weight: 400\">b. C++ API for TensorFlow<\/span><\/h3>\n<p><span style=\"font-weight: 400\">The runtime of TensorFlow is written in C++ and mostly C++ is connected to TensorFlow through header files in tensorflow\/cc. C++ API still is in experimental stages of development but Google commits to work with C++.<\/span><\/p>\n<h3><span style=\"font-weight: 400\">c. Python API for TensorFlow<\/span><\/h3>\n<p><span style=\"font-weight: 400\">Python is the most recognizable and \u201cthe main\u201d language when it comes to TensorFlow and its development. It was one of the first languages supported by TensorFlow and still supports most of the features. It seems as TensorFlow\u2019s functionality defines in Python and then moved to C++.<\/span><\/p>\n<p><span style=\"font-weight: 400\">The Python API is so diverse in nature that you will have to choose which level of API in TensorFlow you want to work on.<\/span><\/p>\n<h3><span style=\"font-weight: 400\">d. R API for TensorFlow<\/span><\/h3>\n<p><span style=\"font-weight: 400\">The R API for TensorFlow made by RStudio has some different approach than the traditional approach for providing API support. R API fully contains the Python API which is different from what TensorFlow goes with its APIs. But the users of R have all the access to features of Python API.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Further, one can see there are other APIs available for<strong> Java<\/strong>, Go, Rust, Haskel and some of the other unofficial ones outside of TensorFlow project- C# and Julia.<\/span><\/p>\n<h3><span style=\"font-weight: 400\">e. APIs Inside TensorFlow Project<\/span><\/h3>\n<p><span style=\"font-weight: 400\">The API\u2019s inside TensorFlow are still Python-based and they have low-level options for its users such as <\/span><b>tf.manua<\/b><span style=\"font-weight: 400\">l or <\/span><b>tf.nnrelu<\/b><span style=\"font-weight: 400\">\u00a0which are used to build neural network architecture. These APIs also use to aid in designing deep neural network having higher levels of abstraction.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Using <\/span><strong><a href=\"https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/contrib\/learn\">the Estimators API<\/a><\/strong><span style=\"font-weight: 400\">, you can define an interface and can deliver models to fit into the Estimator system. Canned estimators\u2014pre-defined models are present there which follows the estimator conventions for e.g., LinearRegressor or DNNClassifier. <\/span><\/p>\n<p><span style=\"font-weight: 400\">The functionality available through this collection are as follows &#8211;<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">automatic checkpoints<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">automatic logging<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">separate training\/evaluation\/prediction<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">simplified training distribution<\/span><\/li>\n<\/ul>\n<div id=\"attachment_14797\" style=\"width: 2001px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/tensorflow-data-pipeline-v31.gif\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-14797\" class=\"wp-image-14797 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/tensorflow-data-pipeline-v31.gif\" alt=\"API in TensorFlow- API Inside TensorFlow Project\" width=\"1991\" height=\"495\" \/><\/a><p id=\"caption-attachment-14797\" class=\"wp-caption-text\">API in TensorFlow- API Inside TensorFlow Project<\/p><\/div>\n<p><span style=\"font-weight: 400\">TensorFlow is offering sophisticated multi-thread, multi-queue, and queue-runner design that use for loading data. The developers of TensorFlow delivered <\/span><span style=\"font-weight: 400\">the Dataset API<\/span><span style=\"font-weight: 400\"> to address this issue and provide a candy interface as a bonus. The following diagram is from Google\/IO which has added XLA.<\/span><\/p>\n<div id=\"attachment_14775\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/TensorFlow-1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-14775\" class=\"wp-image-14775 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/TensorFlow-1.png\" alt=\"API Inside TensorFlow Project\" width=\"1200\" height=\"628\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/TensorFlow-1.png 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/TensorFlow-1-150x79.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/TensorFlow-1-300x157.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/TensorFlow-1-768x402.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/TensorFlow-1-1024x536.png 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-14775\" class=\"wp-caption-text\">API Inside TensorFlow Project<\/p><\/div>\n<p><span style=\"font-weight: 400\">Other known options to consider for API\u2019s are TF-Slim, Keras, scikit-learn.<\/span><\/p>\n<h3><span style=\"font-weight: 400\">f. APIs outside TensorFlow Project<\/span><\/h3>\n<p><span style=\"font-weight: 400\">Some other TensorFlow API\u2019s which develope outside of TensorFlow project by <strong>machine learning<\/strong> enthusiasts. Let&#8217;s see some TensorFlow API outside TensorFlow Project:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>TFLearn<\/strong>: This API shouldn\u2019t be seen as TF Learn, which is TensorFlow\u2019s \u00a0<\/span><b>tf.contrib.learn<\/b><span style=\"font-weight: 400\">. It is a separate Python package.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>TensorLayer<\/strong>: It comes as a separate package and is different from what TensorFlow\u2019s layers API has in its bag. <\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Pretty Tensor<\/strong>: It is actually a Google project which offers a fluent interface with chaining.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Sonnet<\/strong>: It is a project of Google\u2019s DeepMind which features a modular approach.<\/span><\/li>\n<\/ul>\n<p>So, this was all about the TensorFlow API Documentation. Hope you like our explanation.<\/p>\n<h2><span style=\"font-weight: 400\">Conclusion &#8211; TensorFlow API<\/span><\/h2>\n<p><span style=\"font-weight: 400\">In this article, we saw what TensorFlow API are and how they work. Moreover, we got to know about the TensorFlow API for different languages. In addition, we also studied how TensorFlow is different from Python and how it got its own identity in machine learning and deep neural network areas. <\/span><\/p>\n<p><span style=\"font-weight: 400\">At last, we discussed APIs Inside and Outside of TensorFlow Project. Next, we will see the working with MNIST in TensorFlow. Furthermore, if you have any query, feel free to ask in the comment section<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In our last TensorFlow tutorial, we discussed TensorFlow Pros and Cons. Today, we will see TensorFlow API Documentation. So, in this TensorFlow API tutorial, we will discuss the meaning of API in TensorFlow. Also,&#46;&#46;&#46;<\/p>\n","protected":false},"author":6,"featured_media":14786,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[73],"tags":[989,993,2260,10363,11149,14522,15270,16014],"class_list":["post-14772","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tensorflow","tag-api-documentation","tag-api-in-tensorflow","tag-c-api-for-tensorflow","tag-python-api-for-tensorflow","tag-r-api-for-tensorflow","tag-tensorflow-api","tag-uses-of-tensorflow-api","tag-what-is-tensorflow-api"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>TensorFlow API Documentation | Use Of TensorFlow API - DataFlair<\/title>\n<meta name=\"description\" content=\"TensorFlow API,API documentation,C API,C++ API for TensorFlow,Python API in TensorFlow,TensorFlow API Uses, ,APIs inside &amp; outside of TensorFlow Project\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/tensorflow-api\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"TensorFlow API Documentation | Use Of TensorFlow API - 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