Abstract
ReLU (Rectified Linear Units) layers are widely used in all deep learning architectures. To improve ReLU's performance, some ReLU variants have been proposed in the literature. In this paper, we analyze and compare various kinds of ReLU variants with vReLU: a V-shaped ReLU activation function for artificial neural networks. In particular, we compare and test Leaky ReLU (LReLU), Exponential Linear Unit (ELU) and Scaled Exponential Linear Units (SELU) with vReLU on two datasets: MNIST and not-MNIST. We also analyze these ReLU variants' impact on mean and deviation values for deep artificial neural networks. The comparison and analysis show that vReLU activation function has good performance for classification tasks using artificial neural networks.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.