Effects of Hidden Layers on the Efficiency of Neural networks

Abstract

Hidden layers play a vital role in the performance of Neural network especially in the case of complex problems where the accuracy and the time complexity are the main constraints. The process of deciding the number of hidden layers and number of neurons in each hidden layer is still confusing. In this article, we reviewed different impacts of Hidden layers on the network which provides an overview of using three numbers of hidden layers that were found to be optimal in terms of reducing the time complexity and getting the qualified accuracy. The techniques implementing less than three number of hidden layers mostly had a loss in accuracy while the architecture implementing more than three numbers of hidden layers were found not to be optimal in terms of time complexity. Usually implementing three numbers of hidden layers give the optimal performance in terms of time complexity and accuracy. We had a survey on recent work about the Neural network based on the Empirical observations, in which if the number of hidden layers is reduced it has a direct impact on the accuracy of the network as with the complex problem having less number of hidden layers it might be possible that network will not be trained properly. On the other hand when the number of hidden layers cross the optimal number of hidden layers (three layers), time complexity increases in orders of magnitude as compared to the accuracy gain.