Open AccessEffects of Parallel Structure and Serial Structure on Convolutional Neural Networks
Author(s) -
Xiaoya Chen,
Baoheng Xu,
Lu Han
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1792/1/012074
Subject(s) - mnist database , convolutional neural network , computer science , feature (linguistics) , set (abstract data type) , artificial intelligence , pattern recognition (psychology) , data structure , network structure , simple (philosophy) , artificial neural network , theoretical computer science , linguistics , philosophy , epistemology , programming language
Based on KERAS’ FASHION_MNIST data set, a convolutional neural network with serial structure and parallel structure was set up for training. The serial structure can be used for deep feature mining and the parallel structure can be used to describe the first impression. The results show that the parallel structure has better effect and faster speed for simple images.