### Appropriateness of numbers of receptive fields in convolutional neural networks based on classifying CIFAR-10 and EEACL26 datasets

#### Abstract

**An open question is studied of how many receptive fields (filters) a ****convolutional layer of ****a ****convolutional neural network should have. ****The goal is to ****find a rule for choosing the most appropriate numbers of filters. ****The benchmark datasets are principally diverse CIFAR-10 and EEACL26 for using a common network architecture with three ****convolutional layers whose numbers of filters are changeable. ****Heterogeneity and sensitiveness of CIFAR-10 with infiniteness and scalability of EEACL26 are believed to be relevant enough for generalization and spreading of the appropriateness of filters’ numbers. ****The ****appropriateness ****rule is drawn from top accuracies obtained on ****10**** ****×**** ****20**** ****×**** ****21 ****parallelepipeds for three image sizes****. They show, knowing that the number of filters of the first ****convolutional layer ****should be set greater for the more complex dataset, the rest of appropriate ****numbers of filters ****are set at integers which are multiples of that number. The multipliers make a sequence similar to a progression, e. g., it may be 1, 3, 9, 15 or 1, 2, 8, 16, etc. With only those multipliers, such ****a rule-of-progression does not give the number of filters for the first convolutional layer.**

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