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.

Table of Contents

1. Getting Started
2. 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 Neural Network 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.

from manim import *
from manim_ml.neural_network.layers import FeedForwardLayer, ImageLayer
from manim_ml.neural_network.neural_network import NeuralNetwork
from PIL import Image
import numpy as np

class ImageNeuralNetworkScene(Scene):

    def construct(self):
        image = Image.open('images/image.jpeg')
        numpy_image = np.asarray(image)
        # Make nn
        layers = [
            ImageLayer(numpy_image, height=1.0),
            FeedForwardLayer(3), 
            FeedForwardLayer(5),
            FeedForwardLayer(3)
        ]
        nn = NeuralNetwork(layers)
        # Center the nn
        nn.move_to(ORIGIN)
        self.add(nn)
        # Play animation
        self.play(nn.make_forward_pass_animation())

Variational Autoencoders

This is a visualization of a Variational Autoencoder.

VAE Disentanglement

This is a visualization of disentanglement with a Variational Autoencoder