Fast Evolution of CNN Architecture for Image Classification

Abstract

The performance improvement of Convolutional Neural Network (CNN) in image classification and other applications has become a yearly event. Generally, two factors are contributing to achieving this envious success: stacking of more layers resulting in gigantic networks and use of more sophisticated network architectures, e.g. modules, skip connections, etc. Since these state-of-the-art CNN models are manually designed, finding the most optimized model is not easy. In recent years, evolutionary and other nature-inspired algorithms have become human competitors in designing CNN and other deep networks automatically. However, one challenge for these methods is their very high computational cost. In this chapter, we investigate if we can find an optimized CNN model in the classic CNN architecture and if we can do that automatically at a lower cost. Towards this aim, we present a genetic algorithm for optimizing the number of blocks and layers and some other network hyperparameters in classic CNN architecture. Experimenting with CIFAR10, CIFAR100, and SVHN datasets, it was found that the proposed GA evolved CNN models which are competitive with the other best models available.