Skip to main content

Neural Fractal

A Library for Visual Exploration of Dynamical Systems defined by Neural Networks

Home Page
A fractal Generated by Neural Fractal
The dynamical system that generated the above fractal has been explained in the documentation.

Features

  • Define Dynamical Systems Using Complex-Valued Neural Networks
  • GPU Support for Accelerated Sampling and Rendering
  • Pseudo-Coloring Utilities
  • Built on Top of PyTorch

About the package

Neural Fractal has been developed for exploring the properties of dynamical systems defined by neural networks and specially complex-valued neural networks. Fractals are visualizations of Chaos. They have infinite self-similar patterns. One way to generate fractals is by applying a function repeatedly on a set of points and keeping the points that do not diverge to infinity. Interestingly, Even dynamical systems constructed by simple functions in this way can generate amazing fractals. But what happens if instead of a simple function we use a neural network? Repeatedly applying a neural network is equivalent to a recurrent neural network which is able to model complicated non-linear dynamical systems. Reservoir computing has demonstrated even completely random RNNs can construct strange and interesting dynamics. This package is an attempt to explore the strange and beautiful world of fractals

Installation

pip install nfractal

Quick Start

See the Home Page

Features Under Development

  • Automatic pseudo-coloring
  • Gnerating zoom animations

Main Contributors

  • Amirabbas Asadi, Independet AI and computer science researcher

Release files for nfractal 0.3.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for nfractal 0.3.0
File Size Uploaded
nfractal-0.3.0.tar.gz 6.5 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for nfractal 0.3.0
File Interpreter ABI Platform
nfractal-0.3.0-py3-none-any.whl Python 3 none any Details

Total release size: 14.1 kB

Release files / nfractal-0.3.0.tar.gz

Download URL nfractal-0.3.0.tar.gz
Size 6.5 kB
Tags Source
SHA-256 checksum
How to use checksums
9354e7127726aef04b513b45ecc1a10db558ad049fd7f28c074736adb0b3e0c0
BLAKE2b-256 checksum
How to use checksums
21245c91fb6ee07935470217af4a94bd7fd2b7f6fb2da818daf1b67db40b12fa
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.22.0 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.10

Release files / nfractal-0.3.0-py3-none-any.whl

Download URL nfractal-0.3.0-py3-none-any.whl
Size 7.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
456749b3d93d184515f88596fd62d8030158b57bfda5e2926358660a3f53d084
BLAKE2b-256 checksum
How to use checksums
57552bed5add75bbafa76f0c3921c1d455605168be76366dbde8d006c8b900ec
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.22.0 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.10

Release history Release notifications | RSS feed

This release

0.3.0 This release

2 release files

0.2.0

2 release files

0.1.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page