Improved artificial neural network training with advanced methods

Abstract

Artificial Neural Networks (ANNs) are used for different machine learning tasks such as classification, clustering etc. They have been utilized in important tasks and offering new services more and more in our daily lives. Learning capabilities of these networks have accelerated significantly since 2000s, with the increasing computational power and data amount. Therefore, research conducted on these networks is renamed as Deep Learning, which emerged as a major research area

not only in the neural networks, but also in the Machine Learning discipline. For such an important research field, the techniques used in the training of these networks can be seen as keys for more successful results. In this work, each part of this training procedure is investigated by using of different and improved - sometimes new - techniques on convolutional neural networks which classify grayscale and colored image datasets. Advanced methods included the ones from the literature such as He-truncated Gaussian initialization. In addition, our contributions to the literature include ones such as SinAdaMax Optimizer, Dominantly Exponential Linear Unit (DELU), He-truncated Laplacian initialization and Pyramid Approach for Max-Pool layers. In the chapters of this thesis, success rates are increased with the addition of these advanced methods accumulatively, especially with DELU and SinAdaMax which are our contributions as upgraded methods. In result, success rate thresholds for different datasets are met with simple convolutional neural networks - which are improved with these advanced methods and reached promising test success increases - within 15 to 21 hours (typically less than a day). Thus, better performances are obtained by those different and improved techniques are shown using well-known classification datasets.