Date of Graduation
5-2017
Document Type
Thesis
Degree Name
Bachelor of Science in Computer Engineering
Degree Level
Undergraduate
Department
Computer Science and Computer Engineering
Advisor/Mentor
Gashler, Mike
Committee Member/Reader
Gauch, Susan
Committee Member/Second Reader
Patitz, Matt
Abstract
Artificial neural networks are function-approximating models that can improve themselves with experience. In order to work effectively, they rely on a nonlinearity, or activation function, to transform the values between each layer. One question that remains unanswered is, “Which non-linearity is optimal for learning with a particular dataset?” This thesis seeks to answer this question with the MNIST dataset, a popular dataset of handwritten digits, and vowel dataset, a dataset of vowel sounds. In order to answer this question effectively, it must simultaneously determine near-optimal values for several other meta-parameters, including the network topology, the optimization algorithm, and the number of training epochs necessary for the model to converge to good results.
Citation
Manavazhahan, M. (2017). A Study of Activation Functions for Neural Networks. Computer Science and Computer Engineering Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/csceuht/44