{"id":175333,"date":"2023-12-17T11:03:16","date_gmt":"2023-12-17T10:03:16","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=175333"},"modified":"2023-12-17T12:22:05","modified_gmt":"2023-12-17T11:22:05","slug":"theano-description-and-use-of-this-python-library","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/theano-description-and-use-of-this-python-library","title":{"rendered":"Theano: Description and use of this Python library"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"175333\" class=\"elementor elementor-175333\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-9b3d341 e-flex e-con-boxed e-con\" data-id=\"9b3d341\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ba96c35 elementor-widget elementor-widget-heading\" data-id=\"ba96c35\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Theano is a library that allows solving problems involving large datasets through multi-dimensional arrays. Below, you will find more details on how to accelerate your research using Theano: etymology, creation, features, and its inclusion in the Python pipeline...<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-2926804 e-flex e-con-boxed e-con\" data-id=\"2926804\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-833f36c elementor-widget elementor-widget-text-editor\" data-id=\"833f36c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>Theano is primarily the name of a Greek mathematician and philosopher from the 6th century BCE, known as the exemplary woman of Pythagoras.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-85e60df e-flex e-con-boxed e-con\" data-id=\"85e60df\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3b26e86 elementor-widget elementor-widget-heading\" data-id=\"3b26e86\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What is Theano?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-455ffb5 elementor-widget elementor-widget-text-editor\" data-id=\"455ffb5\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Returning to our main concern, Theano is a <a href=\"https:\/\/datascientest.com\/en\/tpot-all-about-this-machine-learning-python-library\">Python library<\/a> developed by Yoshua Bengio at the University of Montreal in 2007. It is based on and written in Python. Theano utilizes multi-dimensional arrays to define, optimize, and evaluate mathematical expressions, <a href=\"https:\/\/datascientest.com\/en\/unleashing-artistic-expression-with-deep-learning-style-transfer\">especially in the context of Deep Learning<\/a> or problems involving a substantial amount of data. It is notably well-received in academic research and development.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-0164360 e-flex e-con-boxed e-con\" data-id=\"0164360\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e214c62 elementor-widget elementor-widget-heading\" data-id=\"e214c62\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How do you code with Theano?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c12062c elementor-widget elementor-widget-text-editor\" data-id=\"c12062c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul><li style=\"font-weight: 400;\" aria-level=\"1\">It is installed using pip install Theano from the terminal.<\/li><li style=\"font-weight: 400;\" aria-level=\"1\">Theano&#8217;s &#8220;tensor&#8221; sub-library is often abbreviated to T.<\/li><li style=\"font-weight: 400;\" aria-level=\"1\">A Theano function and variables are declared as follows:<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cee35dd elementor-widget elementor-widget-shortcode\" data-id=\"cee35dd\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/sarahlunette\/6e45b7d30710402ee8b304e39ad2b842.js\"><\/script><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-24584a5 elementor-widget elementor-widget-text-editor\" data-id=\"24584a5\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>As you can see, the <strong>Theano function<\/strong> requires an array of variables [a, b] and a predefined variable c as the result, which is an operation (in this case, addition) of a and b. The displayed result when the function is called is 10 in this case.<\/p><p>The graph of the function is shown below, generated by a Theano command, which we will discuss in the next paragraph. It&#8217;s evident that the first two blocks (in green) correspond to the two variables a and b, which are of type float and defined as tensor.dscalar().<\/p><p>The middle block (in red) represents the Theano function defined by c (add for addition). The blue block corresponds to the result.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2178479 elementor-widget elementor-widget-image\" data-id=\"2178479\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/03\/Fichier-122.png\" title=\"\" alt=\"schema 1 theano\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Graph of the Theano f function<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-244e464 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"244e464\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formation-deep-learning\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Apprendre \u00e0 coder avec Theano<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-33320a0 e-flex e-con-boxed e-con\" data-id=\"33320a0\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4225234 elementor-widget elementor-widget-heading\" data-id=\"4225234\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What makes it special?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b5826ad elementor-widget elementor-widget-text-editor\" data-id=\"b5826ad\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>From a functional and performance perspective, this library:<\/p><p>1. Creates an expression that is evaluated by a Theano function.<br \/>2. Allows for differentiation of multiple variables, matrix computations, and gradient calculations with ease.<br \/>3. Automatically generates symbolic graphs that can be visualized using a library and the pydotprint function.<\/p><p>These graphs can be highly complex, and Theano applies many advanced optimization techniques (algebra and compilation) to them.<\/p><p>These techniques consider the overall graph to generate highly efficient code, especially in the context of repetitive calculations.<\/p><p>It doesn&#8217;t rely solely on local optimization techniques but can also reorganize calculations (order, repetitions) to make them faster, similar to<a href=\"https:\/\/datascientest.com\/en\/classification-algorithms-definition-and-main-models\"> different algorithms<\/a> for searching a word in a text that can vary in speed (O(n), O(n\u00b2)).<\/p><p>The Theano function acts as a bridge to inject compiled C code into the runtime (the software responsible for program execution).<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c0eae46 elementor-widget elementor-widget-text-editor\" data-id=\"c0eae46\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Below, you can see an example of a more complex graph than the previous one. Here, there is an additional element, the white blocks, which represent predefined functions from other libraries, for example:<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-96f1a7c elementor-widget elementor-widget-image\" data-id=\"96f1a7c\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/03\/Fichier-121.png\" title=\"\" alt=\"schema 2 theano\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Complex Theano function graph<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a121b9e elementor-widget elementor-widget-text-editor\" data-id=\"a121b9e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The calculations are up to 140 times faster on a GPU <a href=\"https:\/\/datascientest.com\/en\/exploring-imageio-a-comprehensive-guide-to-the-python-library\">compared to C on a CPU (which supports only the float32 format) or pure Python.<\/a> Dynamic code generation in C enables faster evaluation of expressions.<\/p><p>A few additional points:<\/p><p>1. It is fast and stable when dealing with calculations of the type log(1+exp(x)) for large values of x.<br \/>2. It includes self-testing tools for diagnosing bugs and issues.<br \/>3. It can be adapted for use <a href=\"https:\/\/datascientest.com\/en\/convolutional-neural-network-everything-you-need-to-know\">with convolutional networks and recurrent networks.<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-749564d e-flex e-con-boxed e-con\" data-id=\"749564d\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d2ff006 elementor-widget elementor-widget-heading\" data-id=\"d2ff006\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How is it linked to other libraries?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ca7a68d elementor-widget elementor-widget-text-editor\" data-id=\"ca7a68d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul><li style=\"font-weight: 400;\" aria-level=\"1\">Theano incorporates numpy.arrays into its internal workings. It is a library sometimes invoked in the internal operations of Keras with TensorFlow (for machine learning models).<\/li><li style=\"font-weight: 400;\" aria-level=\"1\">Theano can serve as a backend in Keras to perform all internal operations. TensorFlow, which serves a similar purpose, can also be found in this context.<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ab45bc4 e-flex e-con-boxed e-con\" data-id=\"ab45bc4\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2be780e elementor-widget elementor-widget-heading\" data-id=\"2be780e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Conclusion<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2c9e2a7 elementor-widget elementor-widget-text-editor\" data-id=\"2c9e2a7\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Theano is, therefore, a tool that optimizes and accelerates your computations in Deep Learning or Machine Learning based on a graph system that encompasses all computation steps. Despite its age, it is not outdated and deserves a place on the list of optimization libraries. If you wish to gain expertise in these fields, DataScientest has designed a curriculum for Deep Learning that you can find by clicking on the previous link.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a54c894 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"a54c894\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/deep-learning-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Find out more about Deep Learning<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-fb04ffc e-flex e-con-boxed e-con\" data-id=\"fb04ffc\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a7bf937 elementor-widget elementor-widget-html\" data-id=\"a7bf937\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [{\n    \"@type\": \"Question\",\n    \"name\": \"What is Theano?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Getting back to our main concern, Theano is a Python library, developed by Yoshua Bengio at the Universit\u00e9 de Montr\u00e9al in 2007, based on and written in Python. It uses multi-dimensional arrays to define, optimize and evaluate mathematical expressions, especially in Deep Learning or data-intensive problems. It is particularly well accepted in academic research and development.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"How do you code with Theano?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"As you can see, the Theano function requires an array of variables [a,b] and a variable result c predefined as an operation (here addition) of a and b. The result displayed when the function is called is 10 in this case. This is the graph of the function below, generated by a Theano command we'll discuss in the next paragraph. We can see that the first two blocks (in green) correspond to the two variables a and b, which are tensor.dscalar() variables of type float. The middle block (in red) corresponds to the Theano function defined by c (add for addition). The blue block corresponds to the result.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"What's special about it?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Automatically creates symbolic graphs that can be displayed using a library and a pydotprint printing function. These graphs can be very complex, and Theano applies a number of advanced optimization techniques (algebra and compilation) to them. These techniques take into account the global graph to generate highly efficient code, especially when repeating calculations, and not just local optimization techniques, for example when training neural networks.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"How is it related to other libraries?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Theano integrates numpy.arrays into its internal workings\"\n    }\n  }]\n}\n<\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Theano is a library that allows solving problems involving large datasets through multi-dimensional arrays. Below, you will find more details on how to accelerate your research using Theano: etymology, creation, features, and its inclusion in the Python pipeline&#8230; Theano is primarily the name of a Greek mathematician and philosopher from the 6th century BCE, known [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":175334,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Theano: Description and use of this Python library<\/title>\n<meta name=\"description\" content=\"Learn all about Theano, a Python library for deep learning and numerical computations. 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