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

Related tags

Deep Learningvisualizationmachine-learningneural-networkmanim3blue1brown
Overview

ManimML

GitHub license GitHub tag Pypi Downloads Follow Twitter

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 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

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Comments
  • PyPi out of date

    PyPi out of date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

    [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

    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
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