Christine Poerschke commented on SOLR-11597:

Thanks for the ping!

bq. ... used to thinking of neural network calculations in terms of dot 
products ...

I've been reading and learning more about neural networks recently and yes, 
with some familiarity with the domain the matrix representations become less of 
a code comprehension issue. So I think we can stick with the matrix approach 
here and support for more complex networks in future would likely be via 
integration with libraries like deeplearning4j (and others?).

bq. ... The weights can be exported from Keras in any format the user wants. ...

Good to know.

On the non-linearity vs. activation-function choice, let's go with activation 
or activation function.

And can I suggest we take a function/functor style approach e.g. as sketched 
out in 
 commit (assuming 
 {{Function<float,float>}} does not work) for three reasons:
* no string comparisons at document scoring time
* no need to ensure that the validate and point-of-use activation strings match
* custom activation functions could be added by subclasses overriding the 
setActivation method

And two things perhaps in the nice-to-have bucket for the explain function:
* Might it be helpful to include the input values somehow e.g. just the input 
numerics (that are fed to the network) or the input feature-plus-score details 
similar to how LinearModel.explain does it? Ideally with a test.
* Using StringBuilder in explain should reduce the number of String objects 
being allocated and needing to be GC-ed.

> Implement RankNet.
> ------------------
>                 Key: SOLR-11597
>                 URL: https://issues.apache.org/jira/browse/SOLR-11597
>             Project: Solr
>          Issue Type: New Feature
>      Security Level: Public(Default Security Level. Issues are Public) 
>          Components: contrib - LTR
>            Reporter: Michael A. Alcorn
>            Priority: Major
> Implement RankNet as described in [this 
> tutorial|https://github.com/airalcorn2/Solr-LTR].

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