Skip to content
A hyperparameter optimization and meta-learning toolbox for convenient and fast prototyping of machine-/deep-learning models.
Python Jupyter Notebook Shell Makefile
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
docs
examples
hyperactive
notebooks
tests
.gitignore
.travis.yml
LICENSE
Makefile
README.md
codecov.yml
setup.py

README.md





A hyperparameter optimization and meta-learning toolbox for convenient and fast prototyping of machine-learning models.


Master status: img not loaded: try F5 :) img not loaded: try F5 :)
Dev status: img not loaded: try F5 :) img not loaded: try F5 :)
Code quality: img not loaded: try F5 :) img not loaded: try F5 :) img not loaded: try F5 :) img not loaded: try F5 :)




Main features


Optimization Techniques Tested and Supported Packages Optimization Extentions
Local Search: Random Methods: Markov Chain Monte Carlo: Population Methods: Sequential Methods: Machine Learning: Deep Learning:
  • Tensorflow
  • Keras
  • Tensorlayer
  • Pytorch
  • Skorch
Distribution:
Position Initialization: Resource Allocation:
  • Memory
  • Proxy Datasets [1] (coming soon)

This readme provides only a short introduction. For more information check out the
full documentation


Installation

PyPI version

The most recent version of Hyperactive is available on PyPi:

pip install hyperactive

Experimental algorithms

The following algorithms are of my own design and, to my knowledge, do not yet exist in the technical literature. If any of these algorithms still exist I ask you to share it with me in an issue.

Random Annealing

A combination between simulated annealing and random search.

Scatter Initialization

Inspired by hyperband optimization.


References

[1] Proxy Datasets for Training Convolutional Neural Networks

[2] An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks


License

LICENSE

You can’t perform that action at this time.