Hyperas

Uses double curly brackets like `{{uniform(0, 1)}}` to define hyperparameters directly in Keras code, eliminating the need to learn Hyperopt's complex syntax and speeding up experimentation.

Automatically translates templates into Hyperopt's search space, leveraging algorithms like TPE for efficient optimization, as shown in examples with `optim.minimize` and `algo=tpe.suggest`.

Enforces separate `data()` and `create_model()` functions, promoting maintainable code and allowing conditional layers, demonstrated in the complete example with if-else logic for layer addition.

Supports parallel trials via MongoDB for scalable hyperparameter searches across multiple machines, detailed in the FAQ with steps for setting up hyperopt-mongo-worker.