Skip to content

An Eager Mode Automatic Differentiation Engine

Notifications You must be signed in to change notification settings

Kytabyte/autodiff

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

7 Commits
 
 
 
 
 
 
 
 

Repository files navigation

autodiff

This is a simple eager mode automatic differentiation engine written in Python working on scalar type (Python's Real Number).

The motivation to write this project is to give myself a solid understand of how automatic differentiation works in training a neural network. This project also is also good for education to better understand the rationale behind the autodiff.

Usage

import autodiff as ad

x1, x2 = ad.Var(2), ad.Var(3)
y = x1 * x2

y.backward()

print(x1.grad, x2.grad)
>>> 3, 2

Requirements

Python 3.6+

About

An Eager Mode Automatic Differentiation Engine

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages