Akademik Veri Yönetim Sistemi
Data Science, Machine Learning and Statistics 2019 (DMS-2019), Van, Türkiye, 26 - 29 Haziran 2019, ss.78
In our previous study, an artificial neural network was applied to the caltech 101 data set which is an international
data set, the results were analyzed and converted into a publication. In this study, image preprocessing, segmentation
and feature selection were performed. 7 invariant moments applied to geometric and colorless images were used for
feature selection. The success rate in the classification was observed to be about 25%.
In this study, deep learning and optimization techniques were applied on the same data set. Relu was used as
activation function and cross entropy was preferred as loss function. Images are resized to 64x64. Each time the
program is run, a random 6-category image is taken and 100 iterations are executed. Stochastic gradient descent (sgd),
momentum, adam, adagrad, rmsprop and adadelta optimization algorithms were used for different results and these
results were analyzed. The success rates in the classification were as follows: sgd: 64.5%, momentum: 85.56%, adam:
92.31%, adagrad: 71.25%, rmsprop: 40.26% and adadelta: 86.88%.