Weighted Random Search for CNN Hyperparameter Optimization
نویسندگان
چکیده
منابع مشابه
PSEUDO-RANDOM DIRECTIONAL SEARCH: A NEW HEURISTIC FOR OPTIMIZATION
Meta-heuristics have already received considerable attention in various fields of engineering optimization problems. Each of them employes some key features best suited for a specific class of problems due to its type of search space and constraints. The present work develops a Pseudo-random Directional Search, PDS, for adaptive combination of such heuristic operators. It utilizes a short term...
متن کاملHyperparameter Search Space Pruning - A New Component for Sequential Model-Based Hyperparameter Optimization
The optimization of hyperparameters is often done manually or exhaustively but recent work has shown that automatic methods can optimize hyperparameters faster and even achieve better nal performance. Sequential model-based optimization (SMBO) is the current state of the art framework for automatic hyperparameter optimization. Currently, it consists of three components: a surrogate model, an ac...
متن کاملSurrogate Benchmarks for Hyperparameter Optimization
Since hyperparameter optimization is crucial for achieving peak performance with many machine learning algorithms, an active research community has formed around this problem in the last few years. The evaluation of new hyperparameter optimization techniques against the state of the art requires a set of benchmarks. Because such evaluations can be very expensive, early experiments are often per...
متن کاملPractical Hyperparameter Optimization
Recently, the bandit-based strategy Hyperband (HB) was shown to yield good hyperparameter settings of deep neural networks faster than vanilla Bayesian optimization (BO). However, for larger budgets, HB is limited by its random search component, and BO works better. We propose to combine the benefits of both approaches to obtain a new practical state-of-the-art hyperparameter optimization metho...
متن کاملEasy Hyperparameter Search Using Optunity
Optunity is a free software package dedicated to hyperparameter optimization. It contains various types of solvers, ranging from undirected methods to direct search, particle swarm and evolutionary optimization. The design focuses on ease of use, flexibility, code clarity and interoperability with existing software in all machine learning environments. Optunity is written in Python and contains...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL
سال: 2020
ISSN: 1841-9844,1841-9836
DOI: 10.15837/ijccc.2020.2.3868