  In recent years, deep neural networks have had great success in machine
learning and pattern recognition. Architecture size for a neural network
contributes significantly to the success of any neural network. In this study,
we optimize the selection process by investigating different search algorithms
to find a neural network architecture size that yields the highest accuracy. We
apply binary search on a very well-defined binary classification network search
space and compare the results to those of linear search. We also propose how to
relax some of the assumptions regarding the dataset so that our solution can be
generalized to any binary classification problem. We report a 100-fold running
time improvement over the naive linear search when we apply the binary search
method to our datasets in order to find the best architecture candidate. By
finding the optimal architecture size for any binary classification problem
quickly, we hope that our research contributes to discovering intelligent
algorithms for optimizing architecture size selection in machine learning.
