I've been trying to implement a text classification routine using the tensorflow package in python. I already had a successful perceptron version working in the scikit-learn enviroment but scikit-learn does not have multilayer neural networks (except for some mythical version 0.18 that I can't seem to find/install anywhere).
I thought it was best to try something simpler in tensorflow first, to learn how the package works and what it can and cannot do, so I went with nearest neighbors. So far so good, except I just can't find a way to feed a sparse version of the vocabulary matrix (bag-of-words vectorizations of the texts) to a placeholder in tensorflow (in scikit-learn this is no problem at all). Converting the vocabulary matrix to a dense matrix solves the problem but severely slows down the algorithm and clogs up RAM.
Is there any way around this? From what I found on the web it seems tensorflow has very limited support for sparse objects (only certain operations will accept them as input), but I hope I'm wrong.
P.S. Yes, I read this thread and it did not solve my problem. And yes, I know I could stick to the perceptron of the scikit-learn or choose another package, but that's a decision I'll make based on the answers I get here.