Train a NetGANOperator by alternating updates of the discriminator and updates of the generator:

Create and initialize a net with three layers:

Train this net by updating alternately the first and third layers, and collect the net arrays after each optimization iteration on a batch:

Show the evolution of the first array value through iterations and check that the arrays are alternately updated:

Create and initialize a NetGraph with named layers:

Train the net by updating a subpart of the NetGraph 10 times more than another:

Check how the arrays are updated:

Train the same NetGraph by updating arrays separately, one by one:

When a shared array occurs at several places in the network, only a unique training update schedule will be applied to all the occurrences of the shared array.

Create a network with shared arrays:

Train with a TrainingUpdateSchedule that specifies that only the first layer should be updated and collects the value of the weights of the third layer after each update:

The shared weights are updated at each epoch:

The results are the same as training without any update schedule: