I have a Keras network learner within a loop. The loop cycles through various previously saved Keras networks (saved as .h5 files with different network configurations). Specifically, feeding into the Keras Network learner are values from read-in tables (also changing in within the loop). The number of “bitvector*” columns intentionally vary from the read-in tables. The Keras Network Learner node has wildcard selection (i.e. bitvector*) for the streaming columns - i.e. input inclusion - which precisely match the size of the input layer(s) for the read-in .h5 file. One would expect that the wildcard selection in the Keras Network Learner inclusion would thus accommodate the changing number of input columns. This does not seem to be the case. Any suggestions on a work-around strategy for what appears to be a bug in the Keras Network Learner node?
Note, the loop stops after each iteration, noting this warning in the logfile:
2020-02-13 18:36:11,090 : WARN : KNIME-Worker-71-Column Filter 0:318 : : Node : Keras Network Learner : 0:495 : Input deep learning network changed. Please reconfigure the node.
Manual entry into the Keras network learning node to simple change something seems to reactivate the node for execution. Of course, this defeats the purpose of having a loop if a manual entry is required.
Any ideas on how to get through this issue? I have had no issue with the same loop structure strategy which reads in other .h5 files that all have the same input/hidden/output node configurations - but I really need to explore varying network configurations in this loop.