maxpumperla/hyperas

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2173 stars · 315 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Hyperas is a Python library that wraps Hyperopt to provide convenient hyperparameter optimization for Keras models. Users annotate model parameters with a simple template syntax instead of learning Hyperopt's API.

Use cases

• tune dropout rates in a Keras neural network
• find optimal hyperparameters for a deep learning model
• run hyperopt without learning its syntax
• optimize keras model accuracy automatically
• sweep hyperparameter ranges for neural network training
• automate model tuning experiments

When to choose

• you use Keras and want simple hyperparameter tuning with minimal boilerplate
• you want Hyperopt's TPE search without learning its API
• you are prototyping models and need quick parameter sweeps

When to avoid

• you use PyTorch, TensorFlow 2.x/Keras 3, or other frameworks instead of classic Keras
• you need actively maintained tooling with modern framework support
• you need distributed or large-scale hyperparameter search (consider Optuna or Ray Tune)

Facets

library · maturity maintenance

machine-learning developer-tools machine-learning deep-learning developer-tools python hyperparameter-optimization hyperopt keras deep-learning model-tuning

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