A Genetic Algorithm based Kernel-size Selection Approach for a Multi-column Convolutional Neural Network

• URL: http://arxiv.org/abs/1912.12405v2
• Date: Mon, 16 Mar 2020 17:06:44 GMT
• Title: A Genetic Algorithm based Kernel-size Selection Approach for a Multi-column Convolutional Neural Network
• Authors: Animesh Singh, Sandip Saha, Ritesh Sarkhel, Mahantapas Kundu, Mita Nasipuri, Nibaran Das
• Abstract summary: We introduce a genetic algorithm-based technique to reduce the efforts of finding the optimal combination of a hyper-parameter ( Kernel size) of a convolutional neural network-based architecture. The method is evaluated on three popular datasets of different handwritten Bangla characters and digits.
• Score: 11.040847116812046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural network-based architectures give promising results in various domains including pattern recognition. Finding the optimal combination of the hyper-parameters of such a large-sized architecture is tedious and requires a large number of laboratory experiments. But, identifying the optimal combination of a hyper-parameter or appropriate kernel size for a given architecture of deep learning is always a challenging and tedious task. Here, we introduced a genetic algorithm-based technique to reduce the efforts of finding the optimal combination of a hyper-parameter (kernel size) of a convolutional neural network-based architecture. The method is evaluated on three popular datasets of different handwritten Bangla characters and digits. The implementation of the proposed methodology can be found in the following link: https://github.com/DeepQn/GA-Based-Kernel-Size.

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