I am trying to build a complex neural network using Computation Graph implementation in Deeplearning4J. I need to have multiple outputs so that's why I can't go with the generic MultiLayerConfiguration. However, my problem is that in this case I do not know how to do the evaluation of my model and I would like at least to know the accuracy. Has anybody worked with Comp Graphs in dl4j?
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First of all yes: tons of people use computation graph. They usually start from our existing examples though and tend to mainly use it for things like seq2seq.
As for your question on evaluation, it's conceptually the same as multi layer network. How you evaluate is likely going to be task specific though. If you think about where evaluation happens, it's always tied to a task (classification,regression,binary classification,..) with an output layer . In the most common case usually you only have 1 output which outputs a classification. In that case you can just use the first array it outputs. Otherwise for multiple outputs..you'd have to define what you're evaluating. Usually tasks merge to 1 path.
If they don't, you'd have multiple output layers where you want to do an evaluation object per output.
Computation graphs and multi layer network both use a .output method to give you raw arrays. That is typically what you pass to eval.eval.
Adam Gibson
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