Hi, I’m building an application that mimics a neural net with multiple layers, yet is designed so that each layer is decoupled from each other.
As an example, assume we have a 2 layer NN with convolution → dense layer. The standard way using DL4J framework is to create a builder and declare the respective layers in the network. My application requires each layer to be created separately, with passing of tensor values from the convolution to the dense layer.
I would like to ask if the DL4J library does expose the APIs at the neural net level for use. In particular, I’m interested only in using the forward and back propagation logic of each neural net module, without having to “build” the module as a whole.
Appreciate any advise here. Thanks