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< zoq>
rcurtin: Not sure if you have seen this, but when I use test, test_labels, train, train_labels = mlpack.preprocess_split(input=dataset, input_labels=labels, test_ratio=0.3) in Julia, I get the following error -> https://gist.github.com/zoq/4f5a3feb43866c6d690a407717a19069
< zoq>
rcurtin: Everything seems fine if I use: test, test_labels, train, train_labels = mlpack.preprocess_split(dataset, input_labels=labels, test_ratio=0.3) so just dataset and not input=dataset
< zoq>
rcurtin: Ahh, the example is already fixed, still wonder why that is.
< rcurtin>
zoq: right, `input` is a required option, not a keyword option, so it has to come first
< rcurtin>
I'm trying to check the documentation but my Julia session is frozen...
< rcurtin>
yeah, so at least for making `input=dataset` work (e.g. required keyword arguments), those don't appear well-supported in Julia unfortunately
< zoq>
which also has the Pkg.add("Zlib") issue :)
< rcurtin>
I guess I just need to make another release soon so that the stable documentation is updated :)
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< rcurtin>
in Python, required keyword arguments are available so `input=dataset` works there
< rcurtin>
but it looks like in Julia, the only way I can do it is as a hack... where I basically say that the required keyword takes a default value which is throwing an exception
< rcurtin>
e.g. I'd define it as `function f(; input=error("must specify 'keyword' argument"))` or similar
< Aleksandr>
Why in theory argument of derivative is sum(w*x) to the activation function and in all code realisations argument of derivative is output of activation function?
< zoq>
I see, I guess if that is nothing new to the Julia community it's fine.
< rcurtin>
yeah, I talked through the Julia bindings with a couple people from the Julia community and they didn't see any issue with it
< rcurtin>
but I agree it is a little imperfect
< rcurtin>
throwing an exception as a default value is really weird though, so I don't think it's a great idea
< zoq>
Yes, wasn't really helpful for me :)
< rcurtin>
Julia is weird---it's *kind of* like Python but there are all kinds of little differences
< rcurtin>
I work in it every day at my company, and there are some good things about it, but overall, personally, it is not a language I would choose to use for solving my machine learning problems
< zoq>
You can find strange constructs in every language.
< rcurtin>
wait, hang on
< rcurtin>
this is my other problem with Julia---everything is changing so fast
< zoq>
Also, in the README.md file we say "Julia >= 3.2.2" but I think the latest is 1.5? So I guess this is some issue introduced by the release script, looking at the version.
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< rcurtin>
zoq: you're right, something must be wrong there, let me open a PR
< rcurtin>
it looks zoom is not currently banned, but if that does change we can probably find another service
< zoq>
Yeah, I think this will effect some of us.
< kartikdutt18[m]>
Hey @KimSangYeon-DGU, @sakshamb189, I think we have a meet today.
< sakshamb189[m]>
hey kartikdutt18 I am here
< kartikdutt18[m]>
Great, I think for batchnorm PR we would only have to fix the input stream error. (I installed Ubuntu today so I could take a look too). Other than that, I opened a PR for YOLO model (Architecture only) [here](https://github.com/mlpack/models/pull/24). I'll open one for preprocessor tomorrow.
< sakshamb189[m]>
sounds good
< sakshamb189[m]>
so did you figure the bug with batchnorm yet?
< kartikdutt18[m]>
No I didn't yet, All parameters that are needed to be serialized are serialized and I added accessor methods for all parameters required during training. The only thing that I know for sure right now is that if I replace the code [here](https://github.com/mlpack/mlpack/pull/2474#discussion_r451428126) with welford method it will pass the build.
< sakshamb189[m]>
yeah the welford method was already previously passing.
< kartikdutt18[m]>
But I don't think we can use that since its value don't match. Ohh and also to see if everything was working correctly I trained mnist_cnn example and saved it and ran inference and it worked fine. Also, Training a single epoch of DarkNet on Cifar10 takes around 6-7 hrs whereas earlier it was more than 12.
< kartikdutt18[m]>
* But I don't think we can use that since its value don't match with PyTorch. Ohh and also to see if everything was working correctly I trained mnist_cnn example and saved it and ran inference and it worked fine. Also, Training a single epoch of DarkNet on Cifar10 takes around 6-7 hrs whereas earlier it was more than 12.
< sakshamb189[m]>
yup alright then hopefully we should be able to get this resolved.
< sakshamb189[m]>
do we need to discuss anything else?
< kartikdutt18[m]>
I think that's it from my side.
< sakshamb189[m]>
okay then we will meet next week! Have a nice weekend!
< kartikdutt18[m]>
Great, Have a great weeked!
< KimSangYeon-DGU[>
kartikdutt18: sakshamb189 Sorry for being away... let me follow up your discussion now
< kartikdutt18[m]>
No worries, I'm here.
< KimSangYeon-DGU[>
kartikdutt18: Can you explain the reason you introduce the Welford method?
< kartikdutt18[m]>
I removed the welford method since it was giving same value as PyTorch.
< KimSangYeon-DGU[>
Aha, Ok
< KimSangYeon-DGU[>
kartikdutt18: The serialization issue isn't be reproduced on MacOS, right?
< kartikdutt18[m]>
Right, it passes all test locally as well as on Azure on OSX.
< KimSangYeon-DGU[>
Hmm..., Ok. Good starting point to figure it out
< jeffin143[m]>
@zoq
< jeffin143[m]>
Cron job ran again today ???
< jeffin143[m]>
Do we have a meeting today ??
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< jeffin143[m]>
Suddenly
< jeffin143[m]>
Why did your name changed sudddnlu
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< zoq>
jeffin143[m]: mine? Probably because I used another client.
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< zoq>
if anybody likes to have one today?
< rcurtin>
I'd love to join, but the problem is I have meetings at 5pm on Thursdays (it's now 2pm) that I always end up working right up until the time of the meeting... so I can't devote the time today