Parameter Optimization in Convolutional Neural Networks Using Gradient Descent

2019 ◽  
pp. 87-94
Author(s):  
Swaleha Zubair ◽  
Anjani Kumar Singha
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Parameter Optimization ◽  
Gradient Descent

scholarly journals Layer-Wise Compressive Training for Convolutional Neural Networks

2018 ◽  
Vol 11 (1) ◽  
pp. 7 ◽  
Author(s):  
Matteo Grimaldi ◽  
Valerio Tenace ◽  
Andrea Calimera
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Gradient Descent ◽  
Computational Models ◽  
Stochastic Gradient Descent ◽  
Training Algorithm ◽  
Heuristic Rules ◽  
Human Capabilities ◽  
Model Size ◽  
Large Model

Convolutional Neural Networks (CNNs) are brain-inspired computational models designed to recognize patterns. Recent advances demonstrate that CNNs are able to achieve, and often exceed, human capabilities in many application domains. Made of several millions of parameters, even the simplest CNN shows large model size. This characteristic is a serious concern for the deployment on resource-constrained embedded-systems, where compression stages are needed to meet the stringent hardware constraints. In this paper, we introduce a novel accuracy-driven compressive training algorithm. It consists of a two-stage flow: first, layers are sorted by means of heuristic rules according to their significance; second, a modified stochastic gradient descent optimization is applied on less significant layers such that their representation is collapsed into a constrained subspace. Experimental results demonstrate that our approach achieves remarkable compression rates with low accuracy loss (<1%).

Sign in / Sign up

Export Citation Format

Share Document