[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121667-en":3,"doc-seo-121667-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121667,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",6,"Technology","MANIMML - Communicating Machine Learning Architectures with Animation","Rapid advances in machine learning have outpaced the availability of tools that explain and visualize emerging ML methods. Animation can support engaging, time-dynamic communication, yet many current systems require handcrafting or complex general animation software. MANIMML is an open-source Python library that generates ML visualizations directly from code. It uses familiar PyTorch-like network syntax, converts an existing architecture into an animation specification, and automatically composes primitive layer animations into a complete visualization of the forward pass or training components. ","MANIMML: Communicating Machine Learning Architectures with  \nAnimation  \nAlec Helbling* Georgia Institute of Technology  \nDuen Horng (Polo) Chau † Georgia Institute of Technology  \narXiv :2306 . 17108v1 [ cs .LG] 29 Jun 2023  \n Specify & Add Model to Scene  \nmodel = NeuralNetwork([ ImageLayer(image),  \nConvolutional2DLayer(num_feature_maps=1, feature_map_size=7  \nConvolutional2DLayer(num_feature_maps=5, feature_map_size=5 FeedForwardLayer(num_nodes=3 , activation_function=\"ReLU\" ),  \nFeedForwardLayer(num_nodes=2 , activation_function=\"Sigmoid\"])  \nself.add(model)  \n),, filter_size=3 ),),  \nSpecify & Play an Animation  \n[self.play](self.play) (ForwardPass(model))  \n3  \nReLU Sigmoid  \n7  \n 5   \nFigure 1: MANIMML rendering an animation of the forward pass of a convolutional neural network. (A) First a user specifies the neural network architecture with code using a familiar Pytorch-like syntax. Parameters are passed to each layer, specifying various information like the number of convolutional feature maps, the activation function, etc. The model is added to the scene and can be rendered as a single still frame (Bottom Left) . (B) The user can then specify an animation they wish to play with code. Frames for the animation of the forward pass for the specified neural network are shown. An animation is automatically constructed by combining primitive animations for each pair of layers. First a convolution animation is shown, followed by a feed forward neural network animation.  \nABSTRACT  \nThere has been an explosion in interest in machine learning (ML) in recent years due to its applications to science and engineering. However, as ML techniques have advanced, tools for explaining and visualizing novel ML algorithms have lagged behind. Animation has been shown to be a powerful tool for making engaging visualizationsof systems that dynamically change over time, which makes it well suited to the task of communicating ML algorithms. However, the current approach to animating ML algorithms is to handcraft applications that highlight specific algorithms or use complex generalized animation software. We developed MANIMML, an open-source Python library for easily generating animations of ML algorithms directly from code. We sought to leverage ML practitioners' preexisting knowledge of programming rather than requiring them to learn complex animation software. MANIMML has a familiar syntax for specifying neural networks that mimics popular deep learning frameworks like Pytorch. A user can take a preexisting neural network architecture and easily write a specification for an animation in MANIMML, which will then automatically compose animations for different components of the system into a final animation of the entire neural network. MANIMML is open source and available at [https://github.com/helblazer811/ManimML](https://github.com/helblazer811/ManimML).  \n* e-mail: [alechelbling@gatech.edu](alechelbling@gatech.edu)[ ](alechelbling@gatech.edu)†e-mail: [polo@gatech.edu](polo@gatech.edu)  \nIndex Terms: Meachine Learning—Visualization—Deep Learning—Visualization Systems and Tools;  \n1 INTRODUCTION  \nMachine learning (ML) has garnered substantial interest in recent years due to its successful application to many areas of science. This explosion in interest in ML techniques has led to the development of tools for visualizing and explaining ML algorithms. A powerful technique for communicating the dynamic behavior of algorithms is animation, the efficacy of which has long been of interest to researchers [2] . Animation has been shown to effectively increase learner engagement [1, 7], and can be especially helpful for showing transitions between the states of a system [4] . This makes it especially well suited to the task of communicating ML algorithms, which often involve many interacting components with complex sequential dependencies. Despite its studied efficacy, it remains difficult for ML practitioners to easily design faithful animations for expl","cbCaiqCJTBMVcpNV","https://ap.wps.com/l/cbCaiqCJTBMVcpNV","pdf",682375,1,3,"English","en",105,"# Introduction\n## Motivation: visualization gap in ML tools\n## MANIMML overview and contributions\n## Syntax for specifying models\n## Composing animations from layer operations","[{\"question\":\"What problem does MANIMML address in machine learning visualization?\",\"answer\":\"Existing ML visualization tools often lag behind new algorithms and require handcrafting or complex animation software, making accurate animation design time-consuming. MANIMML aims to generate animations directly from code to reduce this burden.\"},{\"question\":\"How does MANIMML let users specify a neural network for animation?\",\"answer\":\"Users describe the neural network architecture as an ordered sequence of layers using a PyTorch-like Python syntax. The specified model parameters are then mapped to visual components in the scene.\"},{\"question\":\"How are animations constructed in MANIMML after the model is specified?\",\"answer\":\"After the model and desired operation are provided, MANIMML automatically composes a final animation by combining primitive animations for each layer pair. It then renders the full sequence, such as forward-pass visualization or training-related steps like dropout.\"}]","MANIMML - Communicating Machine Learning Architectures with Animation | PDF",1785806091,8,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":28},"manimml-communicating-machine-learning-architectures-with-animation","",{"@graph":36,"@context":84},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":21},"https://docshare.wps.com/document/technology/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/manimml-communicating-machine-learning-architectures-with-animation/121667/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does MANIMML address in machine learning visualization?","Question",{"text":74,"@type":75},"Existing ML visualization tools often lag behind new algorithms and require handcrafting or complex animation software, making accurate animation design time-consuming. MANIMML aims to generate animations directly from code to reduce this burden.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does MANIMML let users specify a neural network for animation?",{"text":79,"@type":75},"Users describe the neural network architecture as an ordered sequence of layers using a PyTorch-like Python syntax. The specified model parameters are then mapped to visual components in the scene.",{"name":81,"@type":72,"acceptedAnswer":82},"How are animations constructed in MANIMML after the model is specified?",{"text":83,"@type":75},"After the model and desired operation are provided, MANIMML automatically composes a final animation by combining primitive animations for each layer pair. 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