On decomposing a deep neural network into modules
On decomposing a deep neural network into modules
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DOI：
10.1145/3368089.3409668
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Rangeet Pan;Hridesh Rajan
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文献类型：
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作者：
Rangeet Pan;Hridesh Rajan
深度学习被纳入许多现代软件系统中。将其整体视为整体组件。 DNN需要使用新的示例进行重新训练。一种将多级问题的DNN分解为DNN模块的DNN，即四个规范问题，即Mnist，Emnist，FMNIST和KMNIST在不需要重新培训的DNN中，通过组成DNN模块而形成的DNN模型至少与传统的单片DNN一样好。
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.