Computational issues in Optimization for Deep networks
arxiv(2024)
Abstract
The paper aims to investigate relevant computational issues of deep neural
network architectures with an eye to the interaction between the optimization
algorithm and the classification performance. In particular, we aim to analyze
the behaviour of state-of-the-art optimization algorithms in relationship to
their hyperparameters setting in order to detect robustness with respect to the
choice of a certain starting point in ending on different local solutions. We
conduct extensive computational experiments using nine open-source optimization
algorithms to train deep Convolutional Neural Network architectures on an image
multi-class classification task. Precisely, we consider several architectures
by changing the number of layers and neurons per layer, in order to evaluate
the impact of different width and depth structures on the computational
optimization performance.
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