On Decomposing a Deep Neural Network into Modules

By: Rangeet Pan and Hridesh Rajan

Abstract

Deep learning is being incorporated in many modern software systems. Deep learning approaches train a deep neural network (DNN) model using training examples, and then use the DNN model for prediction. While the structure of a DNN model as layers is observable, the model is treated in its entirety as a monolithic component. To change the logic implemented by the model, e.g. to add/remove logic that recognizes inputs belonging to a certain class, or to replace the logic with an alternative, the training examples need to be changed and the DNN needs to be retrained using the new set of examples. We argue that decomposing a DNN into DNN modules-akin to decomposing a monolithic software code into modules-can bring the benefits of modularity to deep learning. In this work, we develop a methodology for decomposing DNNs for multi-class problems into DNN modules. For four canonical problems, namely MNIST, EMNIST, FMNIST, and KMNIST, we demonstrate that such decomposition enables reuse of DNN modules to create different DNNs, enables replacement of one DNN module in a DNN with another without needing to retrain. The DNN models formed by composing DNN modules are at least as good as traditional monolithic DNNs in terms of test accuracy for our problems.

ACM Reference

Pan, R. and Rajan, H. 2020. On Decomposing a Deep Neural Network into Modules. ESEC/FSE’2020: The 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Nov. 2020).

BibTeX Reference

@inproceedings{pan20decomposing,
  author = {Rangeet Pan and Hridesh Rajan},
  title = {On Decomposing a Deep Neural Network into Modules},
  booktitle = {ESEC/FSE'2020: The 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
  location = {Sacramento, California, United States},
  month = {November 8-November 13, 2020},
  year = {2020},
  entrysubtype = {conference},
  abstract = {
    Deep learning is being incorporated in many modern software systems.
    Deep learning approaches train a deep neural network (DNN) model using
    training examples, and then use the DNN model for prediction. While the
    structure of a DNN model as layers is observable, the model is treated in
    its entirety as a monolithic component. To change the logic implemented
    by the model, e.g. to add/remove logic that recognizes inputs belonging
    to a certain class, or to replace the logic with an alternative, the
    training examples need to be changed and the DNN needs to be retrained
    using the new set of examples. We argue that decomposing a DNN into DNN
    modules-akin to decomposing a monolithic software code into modules-can
    bring the benefits of modularity to deep learning. In this work, we
    develop a methodology for decomposing DNNs for multi-class problems into
    DNN modules. For four canonical problems, namely MNIST, EMNIST, FMNIST,
    and KMNIST, we demonstrate that such decomposition enables reuse of DNN 
    modules to create different DNNs, enables replacement of one DNN module
    in a DNN with another without needing to retrain. The DNN models formed
    by composing DNN modules are at least as good as traditional monolithic
    DNNs in terms of test accuracy for our problems.

  }
}