GPflowOpt
vizier
GPflowOpt | vizier | |
---|---|---|
1 | 5 | |
263 | 1,174 | |
0.0% | 0.8% | |
1.8 | 9.3 | |
over 3 years ago | 7 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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GPflowOpt
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[D] Choosing best parameters from an optimization
1- Hyperparameter optimization as already suggested by u/sener87 but I think your validation does not have to be change as it tests generalization as far as I understand you right. If you have more parameter/larger search space, you may look into Bayesian optimization for this task as implemented e.g. with tensorflow, torch or numpy frameworks.
vizier
- [N] Google Open Sources Vizier, Hyperparameter + Blackbox Optimization Service at Scale
- Is there any premade evolutionary algorithm selecting optimal NN architectures in TensorFlow ?
- Google just open sourced its Vizier optimisation suite
- Python-based research interface for blackbox and hyperparameter optimization
What are some alternatives?
modAL - A modular active learning framework for Python
mango - Parallel Hyperparameter Tuning in Python
agents - TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
SpaceDrones - A simple learning environment with space drones for evolution-inspired optimization.
Hyperactive - An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
Gradient-Free-Optimizers - Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
keras-tuner - A Hyperparameter Tuning Library for Keras
tune - An abstraction layer for parameter tuning
mlr3hyperband - Successive Halving and Hyperband in the mlr3 ecosystem
baybe - Bayesian Optimization and Design of Experiments
DIgging - Decision Intelligence for digging best parameters in target environment.
pyswarms - A research toolkit for particle swarm optimization in Python