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< hlu_> got an assertion failure "Assertion `splitCol > begin' failed." @ core/tree/binary_space_tree/binary_space_tree_impl.hpp:848 What does it mean? What do I need to check?
< hlu_> Thanks
< hlu_> I am using a custom distance metric to do nearest neighbor search: mlpack::neighbor::NeighborSearch<mlpack::neighbor::NearestNeighborSort, LineDistance<2>, arma::mat, mlpack::tree::BallTree > indexer(segments);
< hlu_> The assertion failure happens at constructor.
< rcurtin> hlu_: do any of the dimensions of your data have only NaNs or 0s or some other single value?
< rcurtin> also I wonder if maybe LineDistance<2> might be the issue
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< naywhayare> hmm, I lost connection for about the last 5-10 minutes, so if any messages were sent I (and the logs) missed them
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< naywhayare> wait, what? now I am really confused, how am I in this channel twice?
< naywhayare> wait, what? now I am really confused, how am I in this channel twice?
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< rcurtin> ok... that was strange ...
< hlu_> thanks rcurtin, I found the problem with my distance function. now there is no error.
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< rcurtin> great to hear you got it figured out :)
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< s1998> Zoq: i will make the necessary changes and open a pr.
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< zoq> rcurtin: You don't missed any message.
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< rf_sust2018> hello everyone
< rf_sust2018> :)
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< zoq> rf_sust2018: Hello there!
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< manthan> zoq : rcurtin : for the same input the checkgradient is failing when i run that specific test(annlayertest/gradientflexiblerelulayertest) and passing when i run all tests of that module (annlayertest)
< manthan> on logging, i found that errors after evaluate() and gradient are different in both cases even though they have same input.
< manthan> any ideas on such a situation?
< rcurtin> manthan: I saw your comment on the issue but I hadn't had a chance to reply
< rcurtin> have you tried with different random seeds?
< rcurtin> that is usually the first thing I do in situations like this
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< manthan> rcurtin : i have made the input fixed. Will changing random seeds make a difference then?
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< zoq> manthan: Fixed weights as well?
< manthan> no, just the input and target to the network is fixed (hardcoded)
< manthan> we should expect same error and same weight update and so same gradients right?
< zoq> if we use random weights?
< manthan> ya i thought of that, but for specific test case, the gradients are evaluating to 0
< manthan> and not once but for all cases
< manthan> to make it clearer, the orgGradient is always evaluating to 0 during the specific test but non zero values(which makes the test pass) during the entire test module execution.
< zoq> hm, I wonder if adding the test to the 'parallel_tests' in tests/CMakeLists.txt helps
< manthan> i am not quite clear with the benefit of adding it there
< manthan> also, parametric relu is not tested and i think writing checkgradient() for it would fail too (as it similar to flexible relu)
< zoq> you might be right, I'll make time to take a closer look into the code tomorrow
< manthan> ya sure, thanks
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< rcurtin> ok, I have a PyPI mlpack3 package compiling and appears to be working, but now it is too large for PyPI so I had to file an issue: https://github.com/pypa/packaging-problems/issues/136
< zoq> rcurtin: Hopefully they can easily increase the size.
< rcurtin> yeah, I see that there are many issues opened in the past that get resolved fairly quickly
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