Hyper Parameters Selection for Image Classification in Convolutional Neural Networks

Cognitive image processing is made possible by the availability of faster and cheaper memories, increased processing power of multicore processors and the explosive growth in visual data generation. Object classification remains an active research area with rapid advancements. The recent advances in deep neural networks are providing better than expected results for image classification. The process involves training models with large labelled datasets to learn the underlying features from various image classes to support cognitive inferences on the test data. By training on large annotated image datasets contextual and semantic information can be automatically extracted from the images. An important determinant of better results is the choice of hyper parameters which is difficult to get right. In this paper we empirically investigate the effect of selected hyper parameters of a convolutional neural network on CIFAR-10 dataset and provide results demonstrate their effect and importance for image classification.