Pytorch layers for economic applications.
Project description
Pytorch Layers for Economics Applications
Pytorch
- Documentation: https://HighDimensionalEconLab.github.io/econ_layers
Features
- Exponential layer
- Flexible multi-layer neural network with optional nonlinear last layer
- Affine rescaling of output by an input
Development
To publish a new relase to pypi,
- Ensure that the CI is passing
- Modify setup.py to increment the minor version number, or a major version number once API stability is enforced
- Choose the "Releases" on the github page, then "Draft a new relase"
- Click on "Choose a tag" and then type a new release tag with the
v
followed by the version number you modified to be consistent - After you choose "Publish Release" it will automatically push to pypi, and you can change compatability bounds in downstream packages as required.
Credits
This package was created with Cookiecutter and the giswqs/pypackage project template.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
econ_layers-0.0.28.tar.gz
(5.6 kB
view details)
Built Distribution
File details
Details for the file econ_layers-0.0.28.tar.gz
.
File metadata
- Download URL: econ_layers-0.0.28.tar.gz
- Upload date:
- Size: 5.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.11.1
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 37571fa93a6663ff983825c61e661fb963b620a2b7bb2d7772978f652060da73 |
|
MD5 | ccaedd27fad7b9e7d09d89232d2c6c31 |
|
BLAKE2b-256 | 374c1e7f063c8cfecec8974402186db184845bbd34d22888beca628cc41cdbec |
File details
Details for the file econ_layers-0.0.28-py2.py3-none-any.whl
.
File metadata
- Download URL: econ_layers-0.0.28-py2.py3-none-any.whl
- Upload date:
- Size: 5.8 kB
- Tags: Python 2, Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.11.1
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | b272085d30a3a25a79d2bb5e7c5d70c5845026e20844438def8b049f3f2d7b7c |
|
MD5 | 42f2a6fd6b273c4abba5167ee7c7816f |
|
BLAKE2b-256 | 4bec3a736ad3d381277728a32e3f9c25c11acbf502e081a5df73e16f39e7b012 |