Building Neural Networks as Dataflow Graphs

Generally, data processing programs can be organized into a data flow graph that defines the operations to be performed sequentially on the data. The operation of neural networks can also be interpreted in a similar way, in which the input data to be processed is a specific data set and the operations to be performed on the data are the layers of the net. Due to architectural reasons, the entire neural network graph must be built before actual running, thus it is necessary to change data flows' topological execution to evaluation preceding graph building since knowing the layers separately is not enough to operate the nets. As a solution for displaying editable program graphs, we created a framework in which data processing related Python packages can be described and the programs built from them can be visualized and executed.