Skip to main content

TF.Keras implementation of CORAL ordinal classification output layer

Project description

CORAL ordinal classification in tf.keras

Tensorflow Keras implementation of ordinal classification using CORAL by Cao et al. (2019), with thanks to Sebastian Raschka for the help in porting from PyTorch. This package includes an ordinal output layer and an associated loss function.

This is a work in progress, so please post any issues to the issue queue.

Source repository for the original PyTorch implementation. Docs and tests will eventually be added.

Installation

Install the stable version via pip:

pip install coral-ordinal

Install the most recent code on GitHub via pip:

pip install git+https://github.com/ck37/coral-ordinal/

Dependencies

This package relies on Python 3.6+, Tensorflow 2.2+, numpy, pandas, and scipy.

Example

See this colab notebook for an example of using an ordinal output layer with MNIST.

References

Cao, W., Mirjalili, V., & Raschka, S. (2019). Consistent rank logits for ordinal regression with convolutional neural networks. arXiv preprint arXiv:1901.07884, 6.

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

coral-ordinal-0.1.2.tar.gz (4.1 kB view details)

Uploaded Source

Built Distribution

coral_ordinal-0.1.2-py3-none-any.whl (6.0 kB view details)

Uploaded Python 3

File details

Details for the file coral-ordinal-0.1.2.tar.gz.

File metadata

  • Download URL: coral-ordinal-0.1.2.tar.gz
  • Upload date:
  • Size: 4.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/47.3.1 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.4

File hashes

Hashes for coral-ordinal-0.1.2.tar.gz
Algorithm Hash digest
SHA256 726dee3bbfc4e9fb521bdd0f851ba7dc909f98885f8c9ed0fa6286751130dc40
MD5 ea4d3573ac915b7be4b82047757bc836
BLAKE2b-256 6a696e10d4b66490d61a569ae15120956ed7faa19f311c966e736d178acc2649

See more details on using hashes here.

File details

Details for the file coral_ordinal-0.1.2-py3-none-any.whl.

File metadata

  • Download URL: coral_ordinal-0.1.2-py3-none-any.whl
  • Upload date:
  • Size: 6.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/47.3.1 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.4

File hashes

Hashes for coral_ordinal-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 e93180bbf3bebf97b9544d1489ad4f0341ba4e3c9eab30df2fcb2a2138b8d5c5
MD5 62d2495aa624ab0149c5cf71f1dc8b6e
BLAKE2b-256 211d3626fb1d733acdaf5169f8bec0351cb65cf1e0c38efe9fe2f1fe040599d4

See more details on using hashes here.

Supported by

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