Hyper parameters selection for image classification in convolutional neural networks

Sajid Nazir, Shushma Patel, Dilip Patel

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    4 Citations (Scopus)
    185 Downloads (Pure)


    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 to demonstrate their effect and
    importance for image classification.
    Original languageEnglish
    Title of host publication17th IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC 2018)
    Number of pages7
    ISBN (Electronic)978-1-5386-3360-1
    ISBN (Print)9781538633601
    Publication statusPublished - 8 Oct 2018


    • Cognitive image processing
    • Convolution
    • Deep learning
    • Hyper parameters
    • Neural networks


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