Skip to main content

Simple Automatic Differentiation in Python

Project description

Sympyle

Simple Symbolic Graphs in Python

Build Status codecov CodeFactor

About

Project documentation: http://harveyslash.github.io/sympyle/

Sympyle is a Python library to demonstrate the inner workings of Computational Graphs. Computational Graphs are used by highly optimised computational frameworks like tensorflow and pytorch.

However, these frameworks make several assumptions and optimisations in order to optimise for speed and memory. This often makes it harder to understand the inner workings of how these libraries work.

Sympyle is a simplified model library to demonstrate the working of computational graphs, and how backpropagation works on arbitrary 'networks'.

Examples and tutorials coming soon

For now , you can see tests/ folder for usage

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

sympyle-0.0.12-py3-none-any.whl (9.2 kB view details)

Uploaded Python 3

File details

Details for the file sympyle-0.0.12-py3-none-any.whl.

File metadata

File hashes

Hashes for sympyle-0.0.12-py3-none-any.whl
Algorithm Hash digest
SHA256 54189c9fcc6a6d75847d06bb48350de738836db1d58fbf474959c32cf6109f41
MD5 a130a9f1c33259f0f1037534c8512cf7
BLAKE2b-256 fce0da188863dcf199abb9813b0a9c162e7555462a40b1dfb9f9520669fc2519

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page