Configuring CNN architectures for performance

Machine learning has exhibited significant advances through the development and application of new deep learning methods. Convolutional neural networks (CNNs) in particular are a powerful tool for object recognition in imagery and are widely used in for computer vision applications. Implementing a CNN solution involves decisions about the parameters defining the architecture, including the convolutional filter window size and number of filters, the size of the latent space, and the number of hidden layers. Generally, developers have chosen these parameters by relying on heuristics or empirical investigations. In this study, we build on previous research to understand the trade-offs associated with these design choices for a CNN. The approach explicitly models the performance, as measured by the correct classification rate, and the cost, as measured by computer times for training and testing. We develop a performance model that captures these measures as a function of the design parameters and can guide developers in assessing the tradeoffs.