{"id":182321,"date":"2024-03-22T01:18:00","date_gmt":"2024-03-22T00:18:00","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=182321"},"modified":"2024-03-09T14:27:13","modified_gmt":"2024-03-09T13:27:13","slug":"tensorboard-how-can-i-better-understand-ml-models","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/tensorboard-how-can-i-better-understand-ml-models","title":{"rendered":"TensorBoard: How can I better understand ML models?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"182321\" class=\"elementor elementor-182321\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-cc22009 e-flex e-con-boxed e-con\" data-id=\"cc22009\" 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-db7a6b0 elementor-widget elementor-widget-heading\" data-id=\"db7a6b0\" 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\">While machine learning algorithms are becoming more and more widely used, they also represent significant costs for the organizations that use them. It is therefore essential to analyze the performance of Machine Learning models. TensorBoard makes it possible.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5934fcc elementor-widget elementor-widget-text-editor\" data-id=\"5934fcc\" 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>Discover this visualization tool, its features and how to use it with TensorFlow or <a href=\"https:\/\/datascientest.com\/en\/pytorch-all-about-this-framework\">PyTorch<\/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-f3d7599 e-flex e-con-boxed e-con\" data-id=\"f3d7599\" 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-a877553 elementor-widget elementor-widget-heading\" data-id=\"a877553\" 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 TensorBoard?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e5df90c elementor-widget elementor-widget-text-editor\" data-id=\"e5df90c\" 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><strong>TensorBoard<\/strong> is a suite of web applications for inspecting and understanding your neural network models created on TensorFlow.<\/p><p>As a reminder, TensorFlow is an open source library used to create Machine Learning and <a href=\"https:\/\/datascientest.com\/en\/all-about-deep-learning\">Deep Learning<\/a> models.<\/p><p><strong>TensorBoard<\/strong> makes it much easier for developers and data scientists to interpret the visualizations provided by TensorFlow. They can adjust hyperparameters, detect potential problems and monitor learning progress.<\/p><p>Ultimately, this tool facilitates the process of developing machine learning models, so that they perform better and are more robust.<\/p><p>Good to know: TensorBoard works both online and offline (i.e., without Internet access). You can also install it on a local machine.<\/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-d837912 elementor-widget elementor-widget-image\" data-id=\"d837912\" 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\/10\/tensorboard-machine-learning.jpg\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/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-e5fd55b elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"e5fd55b\" 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\/machine-learning-engineer-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\">Learn more about 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-24ae1c6 e-flex e-con-boxed e-con\" data-id=\"24ae1c6\" 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-f293616 elementor-widget elementor-widget-heading\" data-id=\"f293616\" 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 are TensorBoard's features?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b008608 elementor-widget elementor-widget-heading\" data-id=\"b008608\" 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\">Visualization<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7f70807 elementor-widget elementor-widget-text-editor\" data-id=\"7f70807\" 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 a visualization toolkit, <strong>TensorBoard<\/strong> lets you visualize different types of data:<\/p><ul><li><strong>Scalar graphs:<\/strong> the aim is to visualize scalar statistics that evolve over time. With this feature, you can track learning rates, losses, compare multiple executions, organize tags, etc.<\/li><li><strong>Histograms:<\/strong> these graphs show how the statistical distribution evolves over time. The idea here is to visualize temporal &#8220;slices&#8221; of data and identify biases at each point in time.<\/li><li><strong>Distributions:<\/strong> this graph analyzes the distribution of model weights. Each line represents a percentile of the data distribution. This feature can be useful for detecting problems such as exploding or disappearing gradients.<\/li><li><strong>Images:<\/strong> displays images in png format (those saved via a tf.summary.image). This dashboard can be used to integrate customized visualizations.<\/li><li><strong>Audios:<\/strong> if you want to integrate audio widgets for your audio data, this is the dashboard to use.<\/li><li><strong>Graph explorer:<\/strong> you can visualize the calculation graphs of TensorFlow models. This allows you to better understand the structure of neural networks (whether at the level of layers, operations or connections between neurons).<\/li><li><strong>Exploring embeddings:<\/strong> embeddings are vector representations of the data learned by the model. By exploring embeddings, you can visualize high-dimensional data.<\/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-76ccd97 elementor-widget elementor-widget-text-editor\" data-id=\"76ccd97\" 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>Because it lets you visualize all types of data, Tensorflow is particularly useful for inspecting training data, neuron activations and other aspects of the ML model.<\/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-72644af elementor-widget elementor-widget-heading\" data-id=\"72644af\" 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\">Tracking learning metrics<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4294a28 elementor-widget elementor-widget-text-editor\" data-id=\"4294a28\" 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 various visualization options enable Data Scientists to track learning metrics.<\/p><p>They can analyze :<\/p><ul><li>learning curves ;<\/li><li>loss curves ;<\/li><li>learning rates ;<\/li><li>precision and recall.<\/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-4d6144f elementor-widget elementor-widget-text-editor\" data-id=\"4d6144f\" 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>And this throughout their modeling work. In this way, they can continuously improve their Machine Learning models.<\/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-856fa91 elementor-widget elementor-widget-image\" data-id=\"856fa91\" 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\/10\/tensorboard-machine-learning1.jpg\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/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-8dbff28 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"8dbff28\" 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\/machine-learning-engineer-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\">Master TensorBoard functionalities<\/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-8c75e70 elementor-widget elementor-widget-heading\" data-id=\"8c75e70\" 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-9b11bb3 elementor-widget elementor-widget-text-editor\" data-id=\"9b11bb3\" 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>This is not a feature specific to TensorBoard, but rather a totally free extension.<\/p><p><strong>TensorBoard.dev<\/strong> lets you host, track and share your machine learning experiments. You&#8217;ll be able to view graphs, histograms, distributions, dashboards and more.<\/p><p>But beware: once loaded, <strong>TensorBoards<\/strong> are visible to all. So if you have sensitive data, don&#8217;t integrate them.<\/p><p>To use this tool, follow these steps:<\/p><ol><li>Prepare your TensorBoard logs;<\/li><li>Download the logs (for this, it&#8217;s best to install the latest version of TensorBoard);<\/li><li>View your experiments on TensorBoard.dev.<\/li><\/ol>\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-cc223c3 e-flex e-con-boxed e-con\" data-id=\"cc223c3\" 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-72a3794 elementor-widget elementor-widget-heading\" data-id=\"72a3794\" 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 I use TensorBoard?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2b284aa elementor-widget elementor-widget-heading\" data-id=\"2b284aa\" 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\">With TensorFlow<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8058edb elementor-widget elementor-widget-text-editor\" data-id=\"8058edb\" 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><strong>Before using TensorBoard,<\/strong> it is essential to install TensorFlow. If you haven&#8217;t already done so, simply use the command pip install tensorflow.<\/p><p>Then simply follow these steps:<\/p><ul><li><strong>Import TensorBoard from the TensorFlow module:<\/strong> either in your Python script, or in your Jupyter notebook.<\/li><li><strong>Create a TensorBoard callback:<\/strong> creating a TensorBoard callback command connects the tool to your model. This callback will then collect the training data and save it in a specified log directory.<\/li><li><strong>Train your model:<\/strong> to do this, call the TensorBoard callback and make sure the model is correctly configured for training.<\/li><li><strong>Run TensorBoard:<\/strong> simply open a terminal, access the directory where TensorBoard data is stored and type the following command: tensorboard &#8211;logdir=.\/logs. Here .\/logs is the path to the data storage directory.<\/li><li>When you do so, you&#8217;ll see an output indicating the local URL<\/li><li><strong>Access the TensorBoard interface:<\/strong> simply open your web browser and type the local URL indicated in the output.<\/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-7f68f3a elementor-widget elementor-widget-text-editor\" data-id=\"7f68f3a\" 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>Through these manipulations, you can visualize and monitor the training of your TensorFlow models.<\/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-5cae6b4 elementor-widget elementor-widget-heading\" data-id=\"5cae6b4\" 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\">With PyTorch<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-89f4c95 elementor-widget elementor-widget-text-editor\" data-id=\"89f4c95\" 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>In addition to<a href=\"https:\/\/datascientest.com\/en\/step-by-step-guide-installing-tensorflow-with-anaconda-on-macos\"> TensorFlow<\/a>, TensorBoard can also be used as an output from the PyTorch application.<\/p><p>To do this, you first need to import the necessary modules, such as PyTorch and <a href=\"https:\/\/datascientest.com\/en\/tensorflow-course-where-to-learn-how-to-use-the-framework\">TensorFlow<\/a>, into the Python script. Next, you&#8217;ll need to define a Writer using the Summarywriter class. This allows you to create a backup directory for <strong>TensorBoard<\/strong> data. Next, you can define callback orders based on the results you wish to visualize.<\/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-3960aad e-flex e-con-boxed e-con\" data-id=\"3960aad\" 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-69fe4b0 elementor-widget elementor-widget-heading\" data-id=\"69fe4b0\" 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\">Join DataScientest to deepen your knowledge of Machine Learning<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-36df1a8 elementor-widget elementor-widget-text-editor\" data-id=\"36df1a8\" 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><strong>TensorBoard<\/strong> makes it easy to analyze the performance of <a href=\"https:\/\/datascientest.com\/en\/data-poisoning-a-threat-to-machine-learning-models\">Machine Learning models<\/a>, but you still need to know how to prepare, model and train them. For this, training is essential. 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