#mlpack on 2017-07-12 — irc logs at libera.irclog.whitequark.org

2015-01-15 23:05 verne.freenode.net changed the topic of #mlpack to: http://www.mlpack.org/ -- We don't respond instantly... but we will respond. Give it a few minutes. Or hours. -- Channel logs: http://www.mlpack.org/irc/

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06:04 < ironstark> Hi getting the following error while running the matlab scripts on slake

06:05 < ironstark> https://www.irccloud.com/pastebin/3zksILa0/

06:05 < ironstark> can you please help with how to rectify it

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10:15 < zoq> ironstark: Hello, have you resolved the issue?

10:17 < ironstark> zoq: No

10:17 < ironstark> I am not able to test the scripts locally

10:17 < ironstark> so creating PR's and testing them

10:17 < ironstark> but that slows down the speed so can you please help me in solving it

10:18 < zoq> ironstark: let me take a look

10:22 < zoq> ironstark: export MATLAB_BIN=/opt/matlab/bin/ should solve the issue

10:24 < zoq> I guess, we could include this step somehow in the Makefile.

10:24 < partobs-mdp> zoq: Fixed maxiter bug @rcurtin pointed out. Getting a really nice performance on CopyTask (95% on maxLen = 8). I'll run it a bit more and push it into my fork.

10:24 < partobs-mdp> rcurtin: Could you rerun the shell scripts then?

10:25 < zoq> partobs-mdp: Nice, I think the result for the copy task are fine.

10:31 < ironstark> zoq: It worked thanks a lot :)

10:31 < ironstark> but the latest jenkins build failed because of that error too

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10:35 < zoq> ironstark: It should be fixed now.

10:35 < zoq> ironstark: http://masterblaster.mlpack.org/job/pull-requests-benchmarks-benchmark/209/ only one error left

10:36 < ironstark> yes that is the one I have been getting since morning

10:36 < ironstark> Now that things are working locally

10:36 < ironstark> I should be able to check it

10:38 < zoq> okay, not sure that is helpful, you could start matlab from the console and manually test each line

10:41 < ironstark> yes that would be nice. Thanks for the suggestion

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11:45 < zoq> partobs-mdp: What do you think about the comments I made in #1005.

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22:03 < kris1> Any way to free the parameters of the layer of a network?

22:04 < kris1> When training the nework via a sgd optmizer

22:06 < zoq> kris1: I'm not sure I get what you mean with free the parameters.

22:07 < kris1> sorry i mean freeze

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22:08 < kris1> Where you train only a part of the netwokr and free other weights/biases of the network

22:08 < zoq> So you like to train a network but like to end up with the same parameter?

22:09 < kris1> So suppose i want to train only some layers in the network

22:09 < kris1> and not every layer.

22:10 < kris1> suppose i just want to train the last layer in cnn for finetuning.

22:13 < zoq> hm, currently there is no option to do that, I think if you use the FFN class you could modify the Gradient function to take only a specific layer into account.

22:14 < zoq> I guess you could also store the parameters before the training and reset the parameter afterwards for each layer, but that's probably slow.

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22:20 < kris1> Hmmm, well i am little confused as what does the Backward() and Gradient() function do. I do understand the GradientVisitor() function would call the gradient function of all the layer gradient of each layer and the BackwardVisitor call the error at each layer propogate it backwards i guess.

22:20 < kris1> Would this be correct ?/

22:26 < zoq> yes, I could show you how to modify the Gradient function, for the FFN class, if that is helpful?

22:27 < kris1> That would be great. :)

22:33 < zoq> kris1: https://gist.github.com/zoq/253280cf0f494208749906eba71bf841 something like this

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22:49 < kris1> Ahhhh got it…. thanks.

22:55 < kris1> okay so for training a gan i need to free the discrminator and then trian the generator. So my question is i would be calling the backward() and gradient() function of the discriminator to the the error and gradient for all the layer in the discrminator network and then i would set the error of the outputlayer of the generator to the backward() function from the 1st layer of the discriminator and then call the optimizer on the generator.

22:55 < kris1> Does this make sense to you. I can elaborate if you want.

23:01 < zoq> Makes sense, yes.