How to make dynamic hierarchical TensorFlow neural net?
I don't know if and how this would work in TF. But specific "dynamic" deep learning libraries exist that might give a better fit to your use case. PyTorch for example.
I'm investigating on using tensorflow for an experimental AI algorithm using dynamic neural nets alowing the system to scale(remove and add) layers and width of layers. How should one go about this?
And the followup is if I want to also make the nets hierarchical so that they converge to two values (the classifier and the estimate of how sure it is). E.g. If there is a great variance not explained by the neural net it might give a 0,4 out of 1 as a classifier but also a "sure" value indicating how good the neural net "feels" about the estimate. To compare to us humans we can grasp a concept and also grade how confident we are. Thet hierarchical structure I also want to be dynamic connecting subnets together and disconnecting and also removing them entirely from the system.
My main question: Is this an experiment I should do in tensorflow?
I understand this is not a true technical question. But I hope if you feel it being out of bounds try to edit it to a more objective question.