ManimML is a project focused on providing animations and visualizations of common machine learning concepts with the Manim Community Library.

Last update: Jan 04, 2023

Overview

ManimML

ManimML is a project focused on providing animations and visualizations of common machine learning concepts with the Manim Community Library. We want this project to be a compilation of primitive visualizations that can be easily combined to create videos about complex machine learning concepts. Additionally, we want to provide a set of abstractions which allow users to focus on explanations instead of software engineering.

Getting Started
Examples

Getting Started

First you will want to install manim.

Then install the package form source or pip install manim_ml

Then you can run the following to generate the example videos from python scripts.

manim -pqh src/vae.py VAEScene

Examples

Checkout the examples directory for some example videos with source code.

Neural Networks

This is a visualization of a Variational Autoencoder made using ManimML. It has a Pytorch style list of layers that can be composed in arbitrary order. The following video is made with the code from below.

class VariationalAutoencoderScene(Scene):

    def construct(self):
        embedding_layer = EmbeddingLayer(dist_theme="ellipse").scale(2)
        
        image = Image.open('images/image.jpeg')
        numpy_image = np.asarray(image)
        # Make nn
        neural_network = NeuralNetwork([
            ImageLayer(numpy_image, height=1.4),
            FeedForwardLayer(5),
            FeedForwardLayer(3),
            embedding_layer,
            FeedForwardLayer(3),
            FeedForwardLayer(5),
            ImageLayer(numpy_image, height=1.4),
        ], layer_spacing=0.1)

        neural_network.scale(1.3)

        self.play(Create(neural_network))
        self.play(neural_network.make_forward_pass_animation(run_time=15))

Generative Adversarial Network

This is a visualization of a Generative Adversarial Network made using ManimML.

VAE Disentanglement

This is a visualization of disentanglement with a Variational Autoencoder

You might also like...

Create animations for the optimization trajectory of neural nets

Animating the Optimization Trajectory of Neural Nets loss-landscape-anim lets you create animated optimization path in a 2D slice of the loss landscap

81 Dec 25, 2022

Testing the Facial Emotion Recognition (FER) algorithm on animations

PegHeads-Tutorial-3 Testing the Facial Emotion Recognition (FER) algorithm on animations

2 Jan 3, 2022

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

H2O H2O is an in-memory platform for distributed, scalable machine learning. H2O uses familiar interfaces like R, Python, Scala, Java, JSON and the Fl

6.1k Jan 5, 2023

tsai is an open-source deep learning package built on top of Pytorch & fastai focused on state-of-the-art techniques for time series classification, regression and forecasting.

Time series Timeseries Deep Learning Pytorch fastai - State-of-the-art Deep Learning with Time Series and Sequences in Pytorch / fastai

2.8k Jan 8, 2023

TensorFlow Similarity is a python package focused on making similarity learning quick and easy.

912 Jan 8, 2023

A collection of 100 Deep Learning images and visualizations

A collection of Deep Learning images and visualizations. The project has been developed by the AI Summer team and currently contains almost 100 images.

65 Sep 12, 2022

This is a Machine Learning Based Hand Detector Project, It Uses Machine Learning Models and Modules Like Mediapipe, Developed By Google!

Machine Learning Hand Detector This is a Machine Learning Based Hand Detector Project, It Uses Machine Learning Models and Modules Like Mediapipe, Dev

3 Feb 25, 2022

StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.

3k Jan 8, 2023

The LaTeX and Python code for generating the paper, experiments' results and visualizations reported in each paper is available (whenever possible) in the paper's directory

This repository contains the software implementation of most algorithms used or developed in my research. The LaTeX and Python code for generating the

3 Jan 3, 2023

Comments

PyPi out of date

When I pip install manim_ml it doesn't include any of the examples in the README. It also doesn't have many of the modules you'd expect. For example, manim_ml.neural_networks doesn't exist. As a workaround I've manually installed dependencies and added a clone of the latest commit to my python path. However, it would be nice to be able to install it via pip.

opened by ElPiloto 6
[BUG] update some of the examples

I updated most of the examples, in particular: disentanglement cnn vae.

interpolation still doesn't work, and gan has some positioning issues but at least it renders.

Thanks for the cool library btw! l think having working/updated examples would increase it's visibility and usefulness :)

opened by YannDubs 1
NN scaling issue with Convolutional3DLayer
At some point there was code commited changing the behaviour of the net when scaling it. If I use the code in the pip package everything works fine (0.0.11 seems to contain only code prior to the 7th of may). https://user-images.githubusercontent.com/54776552/198372984-f704cceb-8582-4bf9-bc23-c15ebb836b34.mp4

However I'm forking the repo (with the latest commit from august) because I need to change some internal code and noticed this problem.

https://user-images.githubusercontent.com/54776552/198373792-fd672ec7-708e-4ebe-b353-e291c8a591dd.mp4

Maybe someone can pinpoint the exact commit which causes this behaviour?

Code used:

class Test(Scene): def construct(self): # Make the Layer object l1 = Convolutional3DLayer(4, 2, 2) l2 = Convolutional3DLayer(5, 1, 1) l3 = Convolutional3DLayer(2, 3, 3) layers = [l1, l2, l3] nn = NeuralNetwork(layers) nn.scale(2) nn.move_to(ORIGIN) # Make Animation self.add(nn) #self.play(Create(nn)) forward_propagation_animation = nn.make_forward_pass_animation(run_time=5, passing_flash=True) self.play(forward_propagation_animation)
opened by wand555 1

Releases(v0.0.1)

v0.0.1(Mar 28, 2022)

Basic beta version.
Source code(tar.gz)
Source code(zip)

Owner

GitHub Repository

How Do Adam and Training Strategies Help BNNs Optimization? In ICML 2021.

AdamBNN This is the pytorch implementation of our paper "How Do Adam and Training Strategies Help BNNs Optimization?", published in ICML 2021. In this

47 Sep 20, 2022

git《USD-Seg:Learning Universal Shape Dictionary for Realtime Instance Segmentation》(2020) GitHub: [fig2]

USD-Seg This project is an implement of paper USD-Seg:Learning Universal Shape Dictionary for Realtime Instance Segmentation, based on FCOS detector f

80 Nov 28, 2022

This is code to fit per-pixel environment map with spherical Gaussian lobes, using LBFGS optimization

Spherical Gaussian Optimization This is code to fit per-pixel environment map with spherical Gaussian lobes, using LBFGS optimization. This code has b

41 Dec 14, 2022

Repository of best practices for deep learning in Julia, inspired by fastai

FastAI Docs: Stable | Dev FastAI.jl is inspired by fastai, and is a repository of best practices for deep learning in Julia. Its goal is to easily ena

532 Jan 02, 2023

This repository contains the implementations related to the experiments of a set of publicly available datasets that are used in the time series forecasting research space.

TSForecasting This repository contains the implementations related to the experiments of a set of publicly available datasets that are used in the tim

80 Dec 30, 2022

A torch implementation of "Pixel-Level Domain Transfer"

Pixel Level Domain Transfer A torch implementation of "Pixel-Level Domain Transfer". based on dcgan.torch. Dataset The dataset used is "LookBook", fro

260 Sep 02, 2022

A custom DeepStack model for detecting 16 human actions.

DeepStack_ActionNET This repository provides a custom DeepStack model that has been trained and can be used for creating a new object detection API fo

16 Nov 11, 2022

Demonstrational Session git repo for H SAF User Workshop (28/1)

5th H SAF User Workshop The 5th H SAF User Workshop supported by EUMeTrain will be held in online in January 24-28 2022. This repository contains inst

4 Aug 04, 2022

Library for time-series-forecasting-as-a-service.

TIMEX TIMEX (referred in code as timexseries) is a framework for time-series-forecasting-as-a-service. Its main goal is to provide a simple and generi

8 Jan 06, 2023

A PyTorch implementation of the continual learning experiments with deep neural networks

Brain-Inspired Replay A PyTorch implementation of the continual learning experiments with deep neural networks described in the following paper: Brain

182 Dec 27, 2022

GAN-generated image detection based on CNNs

GAN-image-detection This repository contains a GAN-generated image detector developed to distinguish real images from synthetic ones. The detector is

17 Dec 15, 2022

CHERRY is a python library for predicting the interactions between viral and prokaryotic genomes

CHERRY is a python library for predicting the interactions between viral and prokaryotic genomes. CHERRY is based on a deep learning model, which consists of a graph convolutional encoder and a link

12 Dec 15, 2022

Python library for analysis of time series data including dimensionality reduction, clustering, and Markov model estimation

deeptime Releases: Installation via conda recommended. conda install -c conda-forge deeptime pip install deeptime Documentation: deeptime-ml.github.io

495 Dec 28, 2022

VolumeGAN - 3D-aware Image Synthesis via Learning Structural and Textural Representations

VolumeGAN - 3D-aware Image Synthesis via Learning Structural and Textural Representations 3D-aware Image Synthesis via Learning Structural and Textura

116 Dec 26, 2022

Deep Reinforcement Learning by using an on-policy adaptation of Maximum a Posteriori Policy Optimization (MPO)

V-MPO Simple code to demonstrate Deep Reinforcement Learning by using an on-policy adaptation of Maximum a Posteriori Policy Optimization (MPO) in Pyt

9 Jun 06, 2022

Robot Hacking Manual (RHM). From robotics to cybersecurity. Papers, notes and writeups from a journey into robot cybersecurity.

RHM: Robot Hacking Manual Download in PDF RHM v0.4 ┃ Read online The Robot Hacking Manual (RHM) is an introductory series about cybersecurity for robo

233 Dec 30, 2022