Weighted Random Search for CNN Hyperparameter Optimization
This addresses the computational challenge of hyperparameter optimization for CNNs, but it is incremental as it builds on prior work.
The paper tackles hyperparameter optimization for Convolutional Neural Networks by comparing the Weighted Random Search method with state-of-the-art methods, finding that WRS achieves higher classification accuracy within the same number of tested hyperparameter combinations.
Nearly all model algorithms used in machine learning use two different sets of parameters: the training parameters and the meta-parameters (hyperparameters). While the training parameters are learned during the training phase, the values of the hyperparameters have to be specified before learning starts. For a given dataset, we would like to find the optimal combination of hyperparameter values, in a reasonable amount of time. This is a challenging task because of its computational complexity. In previous work [11], we introduced the Weighted Random Search (WRS) method, a combination of Random Search (RS) and probabilistic greedy heuristic. In the current paper, we compare the WRS method with several state-of-the art hyperparameter optimization methods with respect to Convolutional Neural Network (CNN) hyperparameter optimization. The criterion is the classification accuracy achieved within the same number of tested combinations of hyperparameter values. According to our experiments, the WRS algorithm outperforms the other methods.