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Would it make sense to instead return a scipy sparse matrix?
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In my case, this was a test to see if my layer would behave properly inside a model. So I am not using the output directly. The output will be sent to another layer.
I see no need for scipy in my use case, but it all depends on what people expect from the Model.predict() function.
What would have been really useful is that the error would say that, when using predict(), the output of the model needs to be a RaggedTensor, SparceTensor or something with a "numpy" attribute. I've spent quite some time trying to find out what I was doing wrong. However, I have no clue on where to place that check.
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I think it makes sense to return a TF SparseTensor in this case. The underlying issue is basically a TF limitation --
.numpy()
is a standard API and it's not normal that it isn't available on aSparseTensor
.Can you add a unit test for this change?