Optimizing Dense Feed-Forward Neural Networks
Luis Balderas Miguel Lastra José M. Benítez
Deep learning models have been widely used during the last decade due to
their outstanding learning and abstraction capacities. However, one of the main
challenges any scientist has to face using deep learning models is to establish
the network's architecture. Due to this difficulty, data scientists usually
build over complex models and, as a result, most of them result computationally
intensive and impose a large memory footprint, generating huge costs,
contributing to climate change and hindering their use in computational-limited
devices. In this paper, we propose a novel feed-forward neural network
constructing method based on pruning and transfer learning. Its performance has
been thoroughly assessed in classification and regression problems. Without any
accuracy loss, our approach can compress the number of parameters by more than
70%. Even further, choosing the pruning parameter carefully, most of the
refined models outperform original ones. We also evaluate the transfer learning
level comparing the refined model and the original one training from scratch a
neural network with the same hyper parameters as the optimized model. The
results obtained show that our constructing method not only helps in the design
of more efficient models but also more effective ones.