Neural Network Convolution Architecture for Image Classification: Fitness Landscape Analysis
Received Date: Mar 03, 2023 / Published Date: Mar 29, 2023
Abstract
It is unclear which hyper parameter search technique will be most successful because the global structure of the hyper parameter spaces of neural networks is not well understood. In order to offer guidance on suitable search methods for these spaces, we study the topographies of convolutional neural network architectural search spaces in this research. We investigate the overall structure of these spaces using a traditional method (fitness distance correlation) and a more contemporary instrument (local optima networks).
Citation: Meattini I (2023) Neural Network Convolution Architecture for Image Classification: Fitness Landscape Analysis. J Archit Eng Tech 12: 327. Doi: 10.4172/2168-9717.1000327
Copyright: © 2023 Meattini I. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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