Hyperas

What it is

Hyperas is a simple and convenient tool for optimizing hyperparameters of machine learning models. It wraps around Keras to provide an easy-to-use interface for tuning parameters, making it easier to improve model performance without manual trial and error.

Hyperas stands out for its simplicity and ease of integration with Keras, making it an accessible choice for optimizing machine learning models without extensive setup or configuration.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Limited to Keras models only

   Hyperas wraps around Keras, making it incompatible with other deep learning frameworks like TensorFlow or PyTorch.

2. 02

   Genetic algorithm may not be the most efficient method for all problems

   While genetic algorithms can be effective for some hyperparameter optimization tasks, they might not converge as quickly or effectively as gradient-based methods in certain scenarios.

3. 03

   Small community and limited support

   Hyperas is an open-source project with a relatively small user base, which can lead to fewer contributions, slower bug fixes, and less comprehensive documentation compared to larger projects.

What it does well

• Simple integration with Keras models for hyperparameter tuning.

• Uses a genetic algorithm to optimize hyperparameters efficiently.

• Easy-to-use API for defining search spaces and optimization objectives.

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.