International Business Machines Corporation
Deep learning experiment content generation based on single design

Abstract:

Embodiments of the present invention enable a comparison of different machine-learning models based on a single neural network design may be provided. A deep learning architecture for an experimentation framework is represented as a directed acyclic graph with nodes representing neural network layers. Embodiments of the present invention specify a first machine-learning model in a first branch and a second machine-learning model in a second branch of the directed acyclic graph. Each branch has its own optimizer node. Embodiments of the present invention generate source code for the first machine-learning model and the second machine-learning model out of the directed acyclic graph, and train the first machine-learning model and the second machine-learning model simultaneously, thereby enabling the comparison of different machine-learning models.