ChanServ changed the topic of #mlpack to: "mlpack: a fast, flexible machine learning library :: We don't always respond instantly, but we will respond; please be patient :: Logs at http://www.mlpack.org/irc/
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< jenkins-mlpack2> Project docker ensmallen nightly build build #8: ABORTED in 50 min: http://ci.mlpack.org/job/docker%20ensmallen%20nightly%20build/8/
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< jenkins-mlpack2> Project docker mlpack nightly build build #450: STILL UNSTABLE in 3 hr 32 min: http://ci.mlpack.org/job/docker%20mlpack%20nightly%20build/450/
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< jenkins-mlpack2> Project ensmallen.org website build build #50: FAILURE in 1.4 sec: http://ci.mlpack.org/job/ensmallen.org%20website%20build/50/
< jenkins-mlpack2> Ryan Curtin: Add callback documentation to docs.
< jenkins-mlpack2> Project ensmallen.org website build build #51: STILL FAILING in 1.4 sec: http://ci.mlpack.org/job/ensmallen.org%20website%20build/51/
< jenkins-mlpack2> Ryan Curtin: Move callbacks.md to the right directory.
< jenkins-mlpack2> Yippee, build fixed!
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< jenkins-mlpack2> Project ensmallen.org website build build #52: FIXED in 2.6 sec: http://ci.mlpack.org/job/ensmallen.org%20website%20build/52/
< jenkins-mlpack2> Ryan Curtin: Extract callbacks.md from the tar.
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< travis-ci> mlpack/ensmallen#453 (2.10.0 - e921973 : Ryan Curtin): The build passed.
< travis-ci> Change view : https://github.com/mlpack/ensmallen/compare/687b5aa8a84b^...e92197365805
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< travis-ci> coatless/ensmallen#15 (patch-2 - 73644c6 : James J Balamuta): The build passed.
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< MLJens> Hey, hope I'm right here for a question about convolutional MLPack model.
< MLJens> I tried to copy the MNIST example and just use other data and a different Inputsize.
< MLJens> I created an image of all letters of all on my computer installed fonts.
< MLJens> Stuff like "Windows Dings" or "Symbols" I skipped.
< MLJens> The resolution is 100x100 of each input image.
< MLJens> I dimensioned my network the following way:
< MLJens> 100x100x1 --conv (6 filters of size 11x11. stride = 1) -> 90x90x6
< MLJens> 90x90x6 --------------- Leaky ReLU ---------------------> 90x90x6
< MLJens> 90x90x6 --- max pooling (over 3x3 fields. stride = 3) --> 30x30x6
< MLJens> 30x30x6 --- conv (16 filters of size 9x9. stride = 1) --> 22x22x16
< MLJens> 22x22x16 ------------- Leaky ReLU ---------------------> 22x22x16
< MLJens> 22x22x16 - max pooling (over 2x2 fields. stride = 2) --> 11x11x16
< MLJens> 11x11x16 ----------------- Dense ----------------------> 62 //[0-9] && [a-z] && [A-Z]
< MLJens> Training my network leads to an access violation
< MLJens> The variables trainX and trainY seem to be ok.
< MLJens> The have dimension: 10000 x 392 and 1 x 392
< MLJens> I'm not really skilled in using MLPack and hope, you can help me a little bit.
< MLJens> If it's helpfull, I can open a ticket at stackoverflow to show you my code....
< MLJens> The error occures in "model.train" routine.
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