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 examples/cnn/cnn.py

Examples

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

Convolutional Neural Network

This is a visualization of a Convolutional Neural Network. The code needed to generate this visualization is shown below.

https://user-images.githubusercontent.com/14181830/214898495-ff40c679-3f79-4954-b6fc-13992a5024cb.mp4

from manim import *

from manim_ml.neural_network.layers.convolutional_2d import Convolutional2DLayer
from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
from manim_ml.neural_network.neural_network import NeuralNetwork

# Make the specific scene
config.pixel_height = 700
config.pixel_width = 1900
config.frame_height = 7.0
config.frame_width = 7.0

class CombinedScene(ThreeDScene):
    def construct(self):
        # Make nn
        nn = NeuralNetwork([
                Convolutional2DLayer(1, 7, 3, filter_spacing=0.32),
                Convolutional2DLayer(3, 5, 3, filter_spacing=0.32),
                Convolutional2DLayer(5, 3, 3, filter_spacing=0.18),
                FeedForwardLayer(3),
                FeedForwardLayer(3),
            ],
            layer_spacing=0.25,
        )
        # Center the nn
        nn.move_to(ORIGIN)
        self.add(nn)
        # Play animation
        forward_pass = nn.make_forward_pass_animation()
        self.play(forward_pass)

You can generate the above video by copying the above code into a file called example.py and running the following in your command line (assuming everything is installed properly):

    manim -pql example.py

The above generates a low resolution rendering, you can improve the resolution (at the cost of slowing down rendering speed) by running:

    manim -pqh example.py