{"id":217942,"date":"2023-12-13T06:30:00","date_gmt":"2023-12-13T06:30:00","guid":{"rendered":"https:\/\/datascientest.com\/?p=217942"},"modified":"2023-12-08T13:33:58","modified_gmt":"2023-12-08T13:33:58","slug":"tensorboard-tout-savoir","status":"publish","type":"post","link":"https:\/\/datascientest.com\/tensorboard-tout-savoir","title":{"rendered":"TensorBoard : Comment mieux comprendre les mod\u00e8les ML\u00a0?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"217942\" class=\"elementor elementor-217942\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e6022d5 e-flex e-con-boxed e-con\" data-id=\"e6022d5\" 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-3ac5ad4 elementor-widget elementor-widget-heading\" data-id=\"3ac5ad4\" 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\">Si les algorithmes d'apprentissage automatique sont de plus en plus utilis\u00e9s, ils repr\u00e9sentent \u00e9galement des co\u00fbts non n\u00e9gligeables pour les organisations qui les utilisent. Il est donc primordial d\u2019analyser les performances des mod\u00e8les de Machine Learning. C\u2019est justement possible avec TensorBoard. <\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b48f7c9 elementor-widget elementor-widget-text-editor\" data-id=\"b48f7c9\" 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><span style=\"font-weight: 400;\">D\u00e9couvrez cet outil de visualisation, ses fonctionnalit\u00e9s et son utilisation avec TensorFlow ou PyTorch.\u00a0<\/span><\/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-47f854d e-flex e-con-boxed e-con\" data-id=\"47f854d\" 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-195875e elementor-widget elementor-widget-heading\" data-id=\"195875e\" 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\">C\u2019est quoi TensorBoard ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dc54a8b elementor-widget elementor-widget-text-editor\" data-id=\"dc54a8b\" 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><span style=\"font-weight: 400;\">TensorBoard est une<\/span><b> suite d\u2019applications web pour inspecter et comprendre vos mod\u00e8les de r\u00e9seaux neuronaux<\/b><span style=\"font-weight: 400;\"> cr\u00e9\u00e9s sur <a href=\"https:\/\/datascientest.com\/formation-tensorflow\">TensorFlow<\/a>. Pour rappel, TensorFlow est une biblioth\u00e8que open source utilis\u00e9e pour cr\u00e9er des <a href=\"https:\/\/datascientest.com\/machine-learning-tout-savoir\">mod\u00e8les de Machine Learning<\/a> et de <a href=\"https:\/\/datascientest.com\/deep-learning-definition\">Deep Learning<\/a>.<\/span><\/p><p><span style=\"font-weight: 400;\">\u00c0 travers TensorBoard, les d\u00e9veloppeurs et Data Scientists peuven<\/span><b>t interpr\u00e9ter beaucoup plus facilement les visualisations<\/b><span style=\"font-weight: 400;\"> fournies par TensorFlow. Ils peuvent ainsi ajuster des hyperparam\u00e8tres, d\u00e9tecter des probl\u00e8mes potentiels ou encore suivre la progression de l&rsquo;apprentissage.<\/span><\/p><p><span style=\"font-weight: 400;\">In fine, cet outil<\/span><b> facilite le processus de d\u00e9veloppement de mod\u00e8les d&rsquo;apprentissage automatique<\/b><span style=\"font-weight: 400;\">, afin qu\u2019ils soient plus performants et plus robustes.<\/span><\/p><p><span style=\"font-weight: 400;\"><span style=\"text-decoration: underline;\"><strong>Bon \u00e0 savoir<\/strong><\/span> : TensorBoard fonctionne aussi bien en ligne qu\u2019hors ligne (c\u2019est-\u00e0-dire , sans acc\u00e8s \u00e0 Internet). Vous pouvez ainsi l\u2019installer sur une machine locale.<\/span><\/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-9c388ca elementor-widget elementor-widget-image\" data-id=\"9c388ca\" 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\t\t\t<img decoding=\"async\" width=\"900\" height=\"500\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/tensorboard-machine-learning.jpg\" class=\"attachment-full size-full wp-image-217954\" alt=\"\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/tensorboard-machine-learning.jpg 900w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/tensorboard-machine-learning-300x167.jpg 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/tensorboard-machine-learning-768x427.jpg 768w\" sizes=\"(max-width: 900px) 100vw, 900px\" \/>\t\t\t\t\t\t\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-310d99f elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"310d99f\" 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-machine-learning-engineer\">\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\">En apprendre plus sur TensorBoard<\/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-7f79405 e-flex e-con-boxed e-con\" data-id=\"7f79405\" 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-29b25fc elementor-widget elementor-widget-heading\" data-id=\"29b25fc\" 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\">Quelles sont les fonctionnalit\u00e9s de TensorBoard ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-368fe8e elementor-widget elementor-widget-heading\" data-id=\"368fe8e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">La visualisation<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-48950a1 elementor-widget elementor-widget-text-editor\" data-id=\"48950a1\" 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<span style=\"font-weight: 400;\">En tant que kit d\u2019outils de visualisation, TensorBoard vous permet de visualiser diff\u00e9rents types de donn\u00e9es:\u00a0<\/span>\n<ul>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Les graphes Scalars<\/b><span style=\"font-weight: 400;\"> : l\u2019objectif est de visualiser les statistiques scalaires qui \u00e9voluent\u00a0 dans le temps. \u00c0 travers cette fonctionnalit\u00e9, il est possible de suivre le taux d&rsquo;apprentissage, les loss, de comparer plusieurs ex\u00e9cutions, d\u2019organiser les balises, etc.\u00a0<\/span><\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Les histogrammes <\/b><span style=\"font-weight: 400;\">: ces graphiques affichent la fa\u00e7on dont la distribution statistique \u00e9volue au fil du temps. L\u2019id\u00e9e est alors de visualiser des \u00ab\u00a0tranches\u00a0\u00bb temporelles de donn\u00e9es et d\u2019identifier les biais \u00e0 chaque \u00e9poque.<\/span><\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Les distributions :<\/b><span style=\"font-weight: 400;\"> ce graphique analyse la distribution des poids des mod\u00e8les. Ainsi, chaque ligne repr\u00e9sente un centile de la distribution des donn\u00e9es.\u00a0 Cette fonctionnalit\u00e9 peut \u00eatre utile pour d\u00e9tecter des probl\u00e8mes tels que l&rsquo;explosion ou la disparition des gradients.<\/span><\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Les images <\/b><span style=\"font-weight: 400;\">: il affiche des images au format png (celles qui ont \u00e9t\u00e9 sauvegard\u00e9es via un tf.summary.image). Ce tableau de bord peut \u00eatre utilis\u00e9 pour int\u00e9grer des visualisations personnalis\u00e9es.\u00a0<\/span><\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Les audios<\/b><span style=\"font-weight: 400;\"> : si vous souhaitez int\u00e9grer des widgets audio pour vos donn\u00e9es audio, c\u2019est ce dashboard qu\u2019il faudra utiliser.\u00a0<\/span><\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>L\u2019explorateur de graphiques <\/b><span style=\"font-weight: 400;\">: vous pouvez visualiser les graphes de calcul des mod\u00e8les TensorFlow. Cela vous permet ainsi de mieux comprendre la <a href=\"https:\/\/datascientest.com\/graph-neural-networks-tout-savoir\">structure des r\u00e9seaux neuronaux<\/a> (que ce soit au niveau des couches, des op\u00e9rations ou des connexions entre les neurones).\u00a0<\/span><\/li>\n \t<li><b>L\u2019exploration des embeddings <\/b><span style=\"font-weight: 400;\">: les embeddings correspondent aux repr\u00e9sentations vectorielles des donn\u00e9es apprises par le mod\u00e8le. En explorant les embeddings, vous pourrez visualiser les donn\u00e9es \u00e0 haute dimension. <\/span><\/li>\n<\/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-a26301a elementor-widget elementor-widget-text-editor\" data-id=\"a26301a\" 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><span style=\"font-weight: 400;\">Comme il vous permet de visualiser tous types de donn\u00e9es, Tensorflow est particuli\u00e8rement utile pour<\/span><b> inspecter les donn\u00e9es d&rsquo;entra\u00eenement, les activations des neurones <\/b><span style=\"font-weight: 400;\">et d&rsquo;autres aspects du mod\u00e8le ML.<\/span><\/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-84662fc elementor-widget elementor-widget-heading\" data-id=\"84662fc\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">Le suivi des m\u00e9triques d'apprentissage<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9747e4b elementor-widget elementor-widget-text-editor\" data-id=\"9747e4b\" 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><span style=\"font-weight: 400;\">Les diff\u00e9rentes possibilit\u00e9s de visualisation permettent aux Data Scientists de <\/span><b>suivre les m\u00e9triques d&rsquo;apprentissage<\/b><span style=\"font-weight: 400;\">. Ils pourront ainsi analyser :\u00a0<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">les courbes d&rsquo;apprentissage ;\u00a0<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">les courbes de perte ;\u00a0<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">les taux d&rsquo;apprentissage ;\u00a0<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">les pr\u00e9cisions et les rappels.<\/span><\/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-97679ed elementor-widget elementor-widget-text-editor\" data-id=\"97679ed\" 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><span style=\"font-weight: 400;\">Et ce, tout au long de leur travail de mod\u00e9lisation. Ce faisant, ils peuvent <\/span><b>am\u00e9liorer en continu leur mod\u00e8les de Machine Learning<\/b>.<\/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-57ccd9c elementor-widget elementor-widget-image\" data-id=\"57ccd9c\" 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\t\t\t<img decoding=\"async\" width=\"803\" height=\"500\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/tensorboard-machine-learning1.jpg\" class=\"attachment-full size-full wp-image-217955\" alt=\"\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/tensorboard-machine-learning1.jpg 803w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/tensorboard-machine-learning1-300x187.jpg 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/tensorboard-machine-learning1-768x478.jpg 768w\" sizes=\"(max-width: 803px) 100vw, 803px\" \/>\t\t\t\t\t\t\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-7e42716 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"7e42716\" 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-machine-learning-engineer\">\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\">Ma\u00eetriser les fonctionnalit\u00e9s de TensorBoard<\/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<div class=\"elementor-element elementor-element-aeb7742 elementor-widget elementor-widget-heading\" data-id=\"aeb7742\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">TensorBoard.dev<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4b0e4e9 elementor-widget elementor-widget-text-editor\" data-id=\"4b0e4e9\" 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><span style=\"font-weight: 400;\">Il ne s\u2019agit pas d\u2019une fonctionnalit\u00e9 propre \u00e0 TensorBoard, mais plut\u00f4t d\u2019une extension totalement gratuite. TensorBoard.dev pour permet alors d\u2019<\/span><b>h\u00e9berger, de suivre et de partager vos exp\u00e9riences d\u2019<\/b><a href=\"https:\/\/datascientest.com\/apprentissage-automatique\">apprentissage automatique<\/a>. Vous pourrez y visualiser des graphiques, des histogrammes, des distributions, des tableaux de bord, etc.<\/p><p><span style=\"font-weight: 400;\">Mais attention, d\u00e8s lors qu\u2019ils sont charg\u00e9s, les TensorBoards sont visibles par tous. Alors si vous avez des donn\u00e9es sensibles, ne les y int\u00e9grez pas.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Pour utiliser cet outil, voici les d\u00e9marches \u00e0 suivre :\u00a0<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Pr\u00e9parez vos logs TensorBoard ;\u00a0<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">T\u00e9l\u00e9chargez les logs (pour cela, mieux vaut installer la derni\u00e8re version de TensorBoard) ;\u00a0<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Visualisez vos exp\u00e9riences sur TensorBoard.dev.<\/span><\/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-0294207 e-flex e-con-boxed e-con\" data-id=\"0294207\" 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-7c2eaa5 elementor-widget elementor-widget-heading\" data-id=\"7c2eaa5\" 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\">Comment utiliser TensorBoard ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-33ed02e elementor-widget elementor-widget-heading\" data-id=\"33ed02e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">Avec TensorFlow<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c257963 elementor-widget elementor-widget-text-editor\" data-id=\"c257963\" 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><span style=\"font-weight: 400;\">Avant d\u2019utiliser TensorBoard, il est primordial d\u2019installer TensorFlow. Si ce n\u2019est pas d\u00e9j\u00e0 fait, vous pouvez simplement utiliser la commande : <\/span><span style=\"font-weight: 400;\"><strong>pip install tensorflow.<\/strong>\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">D\u00e8s lors, il vous suffit de suivre ces \u00e9tapes :\u00a0<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Importez TensorBoard <\/b><span style=\"font-weight: 400;\">depuis le module TensorFlow : soit dans votre script Python, soit dans votre notebook Jupyter.\u00a0<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Cr\u00e9ez un Callback TensorBoard <\/b><span style=\"font-weight: 400;\">: la cr\u00e9ation d\u2019un ordre de rappel TensorBoard permet de connecter l\u2019outil \u00e0 votre mod\u00e8le. Ce callback va alors collecter les donn\u00e9es d&rsquo;entra\u00eenement et les enregistrer dans un r\u00e9pertoire de journalisation sp\u00e9cifi\u00e9.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Entra\u00eenez votre mod\u00e8le<\/b><span style=\"font-weight: 400;\"> : pour cela, appelez le Callback TensorBoard et assurez-vous que le mod\u00e8le soit correctement configur\u00e9 pour l&rsquo;entra\u00eenement.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Ex\u00e9cutez TensorBoard<\/b><span style=\"font-weight: 400;\"> : il vous suffit d\u2019ouvrir un terminal, d\u2019acc\u00e9der au r\u00e9pertoire o\u00f9 les donn\u00e9es TensorBoard sont enregistr\u00e9es, puis de taper la commande suivante<\/span><span style=\"font-weight: 400;\"> :<strong> tensorboard &#8211;logdir=.\/logs<\/strong>.<\/span><span style=\"font-weight: 400;\"> Ici<\/span><span style=\"font-weight: 400;\"> .\/logs <\/span><span style=\"font-weight: 400;\">correspond au chemin du r\u00e9pertoire de stockage des donn\u00e9es. Ce faisant, vous verrez une sortie indiquant l&rsquo;URL locale<\/span><\/li><li><b>Acc\u00e9dez \u00e0 l&rsquo;interface TensorBoard :<\/b><span style=\"font-weight: 400;\"> il suffit d\u2019ouvrir votre navigateur web et de taper l&rsquo;URL locale indiqu\u00e9e dans la sortie.<\/span><\/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-e50b0dd elementor-widget elementor-widget-text-editor\" data-id=\"e50b0dd\" 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><span style=\"font-weight: 400;\">\u00c0 travers ces manipulations, vous pourrez visualiser et surveiller l&rsquo;entra\u00eenement de vos mod\u00e8les TensorFlow.<\/span><\/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-3b542bd elementor-widget elementor-widget-heading\" data-id=\"3b542bd\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">Avec PyTorch<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c93cca7 elementor-widget elementor-widget-text-editor\" data-id=\"c93cca7\" 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><span style=\"font-weight: 400;\">En plus de TensorFlow, TensorBoard peut aussi \u00eatre utilis\u00e9e en sortie de l\u2019application PyTorch.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Pour cela, il faut d\u2019abord importer les modules n\u00e9cessaires, comme PyTorch et TensorFlow dans le script Python. Par la suite, vous devrez <\/span><b>d\u00e9finir un Writer<\/b><span style=\"font-weight: 400;\"> en utilisant la classe Summarywriter. Cela permet ainsi de cr\u00e9er un <\/span><b>r\u00e9pertoire de sauvegarde <\/b><span style=\"font-weight: 400;\">pour les donn\u00e9es TensorBoard. Ensuite, vous pouvez d\u00e9finir les ordres de rappels en fonction des r\u00e9sultats que vous souhaitez visualiser.<\/span><\/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-7f34bad e-flex e-con-boxed e-con\" data-id=\"7f34bad\" 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-79d83b3 elementor-widget elementor-widget-heading\" data-id=\"79d83b3\" 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\">Rejoignez DataScientest pour approfondir vos connaissances en Machine Learning<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b88fccd elementor-widget elementor-widget-text-editor\" data-id=\"b88fccd\" 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><span style=\"font-weight: 400;\">Si TensorBoard facilite l\u2019analyse des performances des mod\u00e8les de Machine Learning,\u00a0 encore faut-il savoir les pr\u00e9parer, les mod\u00e9liser et les entra\u00eener. Or, pour cela, il est plus que n\u00e9cessaire de se former. <\/span><b>DataScientest<\/b><span style=\"font-weight: 400;\"> vous propose justement sa formation en science des donn\u00e9es qui vous aidera \u00e0 ma\u00eetriser tous les aspects de l\u2019intelligence artificielle : de la programmation au Machine Learning, en passant par l\u2019analyse de donn\u00e9es ou la data visualisation.<\/span><\/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-58bb524 elementor-widget__width-initial elementor-widget elementor-widget-video\" data-id=\"58bb524\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=pVNzVvHV1No&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.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-video .elementor-widget-container{overflow:hidden;transform:translateZ(0)}.elementor-widget-video .elementor-wrapper{aspect-ratio:var(--video-aspect-ratio)}.elementor-widget-video .elementor-wrapper iframe,.elementor-widget-video .elementor-wrapper video{height:100%;width:100%;display:flex;border:none;background-color:#000}@supports not (aspect-ratio:1\/1){.elementor-widget-video .elementor-wrapper{position:relative;overflow:hidden;height:0;padding-bottom:calc(100% \/ var(--video-aspect-ratio))}.elementor-widget-video .elementor-wrapper iframe,.elementor-widget-video .elementor-wrapper video{position:absolute;top:0;right:0;bottom:0;left:0}}.elementor-widget-video .elementor-open-inline .elementor-custom-embed-image-overlay{position:absolute;top:0;right:0;bottom:0;left:0;background-size:cover;background-position:50%}.elementor-widget-video .elementor-custom-embed-image-overlay{cursor:pointer;text-align:center}.elementor-widget-video .elementor-custom-embed-image-overlay:hover .elementor-custom-embed-play i{opacity:1}.elementor-widget-video .elementor-custom-embed-image-overlay img{display:block;width:100%;aspect-ratio:var(--video-aspect-ratio);-o-object-fit:cover;object-fit:cover;-o-object-position:center center;object-position:center center}@supports not (aspect-ratio:1\/1){.elementor-widget-video .elementor-custom-embed-image-overlay{position:relative;overflow:hidden;height:0;padding-bottom:calc(100% \/ var(--video-aspect-ratio))}.elementor-widget-video .elementor-custom-embed-image-overlay img{position:absolute;top:0;right:0;bottom:0;left:0}}.elementor-widget-video .e-hosted-video .elementor-video{-o-object-fit:cover;object-fit:cover}.e-con-inner>.elementor-widget-video,.e-con>.elementor-widget-video{width:var(--container-widget-width);--flex-grow:var(--container-widget-flex-grow)}<\/style>\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-46a326a elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"46a326a\" 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-machine-learning-engineer\">\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\">Se former au Machine 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-4c2ed02 e-flex e-con-boxed e-con\" data-id=\"4c2ed02\" 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-9266cd9 elementor-widget elementor-widget-html\" data-id=\"9266cd9\" 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\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"C\u2019est quoi TensorBoard ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"TensorBoard est une suite d\u2019applications web pour inspecter et comprendre vos mod\u00e8les de r\u00e9seaux neuronaux cr\u00e9\u00e9s sur TensorFlow. Pour rappel, TensorFlow est une biblioth\u00e8que open source utilis\u00e9e pour cr\u00e9er des mod\u00e8les de Machine Learning et de Deep Learning.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Quelles sont les fonctionnalit\u00e9s de TensorBoard ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"En tant que kit d\u2019outils de visualisation, TensorBoard vous permet de visualiser diff\u00e9rents types de donn\u00e9es: Les graphes Scalars : l\u2019objectif est de visualiser les statistiques scalaires qui \u00e9voluent  dans le temps. \u00c0 travers cette fonctionnalit\u00e9, il est possible de suivre le taux d'apprentissage, les loss, de comparer plusieurs ex\u00e9cutions, d\u2019organiser les balises, etc. Les histogrammes : ces graphiques affichent la fa\u00e7on dont la distribution statistique \u00e9volue au fil du temps. L\u2019id\u00e9e est alors de visualiser des \\\"tranches\\\" temporelles de donn\u00e9es et d\u2019identifier les biais \u00e0 chaque \u00e9poque.\"\r\n    }\r\n  }]\r\n}\r\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>Si les algorithmes d&rsquo;apprentissage automatique sont de plus en plus utilis\u00e9s, ils repr\u00e9sentent \u00e9galement des co\u00fbts non n\u00e9gligeables pour les organisations qui les utilisent. Il est donc primordial d\u2019analyser les performances des mod\u00e8les de Machine Learning. C\u2019est justement possible avec TensorBoard. D\u00e9couvrez cet outil de visualisation, ses fonctionnalit\u00e9s et son utilisation avec TensorFlow ou PyTorch.&nbsp; [&hellip;]<\/p>\n","protected":false},"author":78,"featured_media":217944,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2497],"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>TensorBoard : Comment mieux comprendre les mod\u00e8les ML\u00a0?<\/title>\n<meta name=\"description\" content=\"Se pr\u00e9sentant comme un kit d\u2019outils de visualisation, TensorBoard vous permet de suivre et d\u2019interpr\u00e9ter efficacement vos mod\u00e8les de Machine Learning.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/tensorboard-tout-savoir\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"TensorBoard : Comment mieux comprendre les mod\u00e8les ML\u00a0?\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/tensorboard-tout-savoir\" \/>\n<meta property=\"og:site_name\" content=\"Formation Data Science | DataScientest.com\" \/>\n<meta property=\"article:published_time\" content=\"2023-12-13T06:30:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-12-08T13:33:58+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/tensorboard-datascientest.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"600\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"J\u00e9r\u00e9my Robert\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"J\u00e9r\u00e9my Robert\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/tensorboard-tout-savoir\",\"url\":\"https:\/\/datascientest.com\/tensorboard-tout-savoir\",\"name\":\"TensorBoard : Comment mieux comprendre les mod\u00e8les ML\u00a0?\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/#website\"},\"datePublished\":\"2023-12-13T06:30:00+00:00\",\"dateModified\":\"2023-12-08T13:33:58+00:00\",\"author\":{\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/7af2361c48e24e79a8aa2360e6e74925\"},\"description\":\"Se pr\u00e9sentant comme un kit d\u2019outils de visualisation, TensorBoard vous permet de suivre et d\u2019interpr\u00e9ter efficacement vos mod\u00e8les de Machine Learning.\",\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/datascientest.com\/tensorboard-tout-savoir\"]}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/datascientest.com\/#website\",\"url\":\"https:\/\/datascientest.com\/\",\"name\":\"Formation Data Science | DataScientest.com\",\"description\":\"Leader de la formation en Data Science\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/datascientest.com\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"fr-FR\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/7af2361c48e24e79a8aa2360e6e74925\",\"name\":\"J\u00e9r\u00e9my Robert\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/jeremyrobert.jpeg\",\"contentUrl\":\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/jeremyrobert.jpeg\",\"caption\":\"J\u00e9r\u00e9my Robert\"},\"description\":\"J\u00e9r\u00e9my Robert est actuellement Directeur p\u00e9dagogique B2B chez DataScientest.com. Avant de rejoindre DataScientest, il a travaill\u00e9 en tant que Consultant Business Intelligence chez DEAPDATA, o\u00f9 il a g\u00e9r\u00e9 des projets dans les domaines de l'assurance, bancaire et industrie. Il a \u00e9galement \u00e9t\u00e9 Co-fondateur et pr\u00e9sident de Tripzer. En tant que consultant ind\u00e9pendant, J\u00e9r\u00e9my a propos\u00e9 des formations sur MicroStrategy pour divers niveaux professionnels. 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