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Examining Hyperparameters of Neural Networks Trained Using Local Search

Deep neural networks (DNNs) have been found useful for many applications. However, training and designing those networks can be challenging and is considered more of an art or an engineering process than rigorous science. In this regard, the important process of choosing hyperparameters is relevant. In addition, training neural networks with derivative-free methods is somewhat understudied. Particularly, with regards to hyperparameter selection. The paper presents a small-scale study of 3 hyperparam-eters choice for convolutional neural networks (CNNs). The networks were trained with two single-candidate optimization algorithms: Stochastic Gradient Descent (derivative-based) and Local Search (derivative-free). The CNN is trained on a subset of the FashionMNIST dataset. Experimental results show that hyperparameter selection can be detrimental for Local Search, especially regarding network parametrization. Moreover, the best hyperparameter choices didn't match for both algorithms. Future investigation into the training dynamics of Local Search is likely needed.

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