Minimal automatic differentiation implementation in Python, NumPy.
Project description
SmallPebble
Minimal automatic differentiation implementation in Python, NumPy.
For an introduction to autodiff and the basic concepts of this framework, see: https://sidsite.com/posts/autodiff/
Consider this a resource on autodiff, rather than a library you should use. (Popular libraries are: JAX, PyTorch, TensorFlow...)
Features:
- Various operations, such as matmul, conv2d, maxpool2d.
- Supports broadcasting.
- Nth derivatives.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
smallpebble-0.1.0.tar.gz
(9.2 kB
view details)
Built Distribution
File details
Details for the file smallpebble-0.1.0.tar.gz
.
File metadata
- Download URL: smallpebble-0.1.0.tar.gz
- Upload date:
- Size: 9.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.25.1 setuptools/51.0.0.post20201207 requests-toolbelt/0.9.1 tqdm/4.54.0 CPython/3.8.5
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9a6bcdc781e937eb363417a1a4c061835725535bb7eda748b16a705b6256e1d4 |
|
MD5 | c3da9bc1b18efc57d0e22876827345ea |
|
BLAKE2b-256 | 7c3d9962ee79b802d03811fcdb46ac0ad2fa778dbe2d2c83833cf4cedec7c516 |
File details
Details for the file smallpebble-0.1.0-py3-none-any.whl
.
File metadata
- Download URL: smallpebble-0.1.0-py3-none-any.whl
- Upload date:
- Size: 10.4 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.25.1 setuptools/51.0.0.post20201207 requests-toolbelt/0.9.1 tqdm/4.54.0 CPython/3.8.5
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1dfa3fe8cc7064a76b86f612e6fbc502a9f2220ebc0e7e1b1e538b0b337219f9 |
|
MD5 | 10cad5ea97b087eac340995a0320d80d |
|
BLAKE2b-256 | b6a94bfab9f2eaeeebb914d4e8859fb496ad09d6032601eac94a0a8e339e93b7 |