Hi Thushan, Yes, so currently we take a training data fraction as an input from the user (check the wizard) which splits the dataset to training and test. And for other algorithms too, Spark generates an accuracy measurement using the test data predictions vs actual.
test *accuracy* sounds good as a measurement. On Fri, Jun 19, 2015 at 9:19 AM, Thushan Ganegedara <[email protected]> wrote: > Hi all, > > Thank you very much for the feedback > > One small thing, is there a some sort of accuracy measurement that we can > show for deep networks? > > Yes, there is. Usually the accuracy of the deep network is shown with a > validation dataset and a test set (i.e. validation error and test error). > In other words, after training the network, we run an independent test set > and see how accurate the algorithm is (# of correct results/ # of total > results) > > It seems the test accuracy would fit for the model comparison > > On Fri, Jun 19, 2015 at 1:01 PM, Nirmal Fernando <[email protected]> wrote: > >> Hi Thushan, >> >> Looks good for me too. One small thing, is there a some sort of accuracy >> measurement that we can show for deep networks? This is required for the >> model comparison page; >> https://docs.wso2.com/display/ML100/Model+Comparison >> >> On Fri, Jun 19, 2015 at 7:27 AM, Thushan Ganegedara <[email protected]> >> wrote: >> >>> Hi, >>> >>> as he configure, can we visualize the deep network ( use some d3 graph >>> library). >>> I don't think I understood. Are you asking if we could show the user a >>> diagram of the network he has specified (like the attached image)? >>> >> >>> Also, after he has trained, can we let him visualize the output of >>> intermediate layers ( this is a advanced feature, so optional). >>> Intermediate levels are quite tricky to visualize. Two major issues with >>> visualizing intermediate layers are, >>> 1. Intermediate layers are not linear transformations of the input so >>> the visualized filters of intermediate layers doesn't make much sense (I >>> tried it, what I saw was completely random pixels) >>> >>> 2. It is possible to overcome the above issue with a technique called >>> activation maximization (we are solving the optimization problem z = >>> sigmoid(x.W) by keeping W constant and changing x to get the maximum z). >>> However, this is highly non-convex. So it easily get stuck in local maxima. >>> Also, this is to costly for large dimensional data (it will work for MNIST >>> though). >>> >>> Thank you >>> >>> On Fri, Jun 19, 2015 at 11:50 AM, Srinath Perera <[email protected]> >>> wrote: >>> >>>> Hi Tushan, >>>> >>>> OK high level. However, as he configure, can we visualize the deep >>>> network ( use some d3 graph library). >>>> >>>> Also, after he has trained, can we let him visualize the output of >>>> intermediate layers ( this is a advanced feature, so optional). >>>> >>>> --Srinath >>>> >>>> On Fri, Jun 19, 2015 at 6:56 AM, Thushan Ganegedara <[email protected]> >>>> wrote: >>>> >>>>> Dear all, >>>>> >>>>> Please find the proposed UI changes for the Deep Network Integration >>>>> attached herewith. >>>>> >>>>> Feedback would be highly appreciated. >>>>> >>>>> -- >>>>> Regards, >>>>> >>>>> Thushan Ganegedara >>>>> School of IT >>>>> University of Sydney, Australia >>>>> >>>> >>>> >>>> >>>> -- >>>> ============================ >>>> Blog: http://srinathsview.blogspot.com twitter:@srinath_perera >>>> Site: http://people.apache.org/~hemapani/ >>>> Photos: http://www.flickr.com/photos/hemapani/ >>>> Phone: 0772360902 >>>> >>> >>> >>> >>> -- >>> Regards, >>> >>> Thushan Ganegedara >>> School of IT >>> University of Sydney, Australia >>> >> >> >> >> -- >> >> Thanks & regards, >> Nirmal >> >> Associate Technical Lead - Data Technologies Team, WSO2 Inc. >> Mobile: +94715779733 >> Blog: http://nirmalfdo.blogspot.com/ >> >> >> > > > -- > Regards, > > Thushan Ganegedara > School of IT > University of Sydney, Australia > -- Thanks & regards, Nirmal Associate Technical Lead - Data Technologies Team, WSO2 Inc. Mobile: +94715779733 Blog: http://nirmalfdo.blogspot.com/
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