Hi,

By checking the dl4j documents, I know that dl4j has two types of networks (MultiLayerNetwork and Computational Graph). I have two questions:

(1) Workflow question: In a complete workflow of deeplearning4j, does the deeplearning4j framework build the MultiLayerNetwork first, then translate/convert MultiLayerNetwork to Computational Graph? Or, deeplearning4j just uses these two types of networks separately (developers have to choose either one of them in their dl4j applications).

(2) Optimization question: Does the deeplearning4j framework has any optimizations on MultiLayerNetwork and Computational Graph (for example, layer fusing in MultiLayerNetwork or vertex fusing in Computational Graph)? If there are some optimizations during the training process, does it mean we could have two different MultiLayerNetwork and Computational Graph during training (before and after optimization)?

Thanks.