Are there any other datasets where dl4j suppose to do well? As long as it does better with *some* datasets, we can go ahead with those?
On Tue, Jun 16, 2015 at 9:13 AM, Thushan Ganegedara <[email protected]> wrote: > Yes, there are few use list (Git hub and google group). I will inquire > about this in user lists. > > Thank you > > > On Tue, Jun 16, 2015 at 12:34 PM, Nirmal Fernando <[email protected]> wrote: > >> Thanks Thushan for the update. >> >> In addition to you digging into the code, can you also inquire on the >> poor performance from the DL4J user list (if there's one exist)? >> >> On Tue, Jun 16, 2015 at 5:27 AM, Thushan Ganegedara <[email protected]> >> wrote: >> >>> Dear all, >>> >>> Please find the update regarding DL4J testing >>> >>> *Poor Accuracy* >>> I have been testing DL4J extensively with *MNIST and Iris* datasets >>> (Small and Full). However, I was unable to get a reasonable accuracy with >>> DL4J for the aforementioned datasets. The F1-score was around 0.02, which >>> is very low. >>> >>> I tried with different settings mainly for the following attributes >>> >>> Weight initialization >>> Gradient Descent >>> Iterations >>> Type of units: Autoencoder/RBM >>> >>> >>> But none of the settings gave a reasonable accuracy. Furthermore, the >>> predicted values for the test data usually *belong to 1 or 2 classes *(e.g. >>> when trained on MNIST dataset, the program predict 0 and 1 only, though >>> there are 10 possible classes) >>> >>> Also there are many reports of *poor accuracy of DL4J.* The best >>> accuracy I could find reported was around 0.5 F1 score for MNIST, which is >>> still very low. (e.g. MNIST can easily reach 0.9+ accuracy for even a >>> basic deep network) >>> >>> I'm currently trying to delve in to the code for DL4J and figure out how >>> the learning is done. I'm assuming there are some faults in the learning >>> process which causes the algorithm to learn poorly. >>> >>> Thank you >>> >>> -- >>> 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 > -- ============================ Blog: http://srinathsview.blogspot.com twitter:@srinath_perera Site: http://people.apache.org/~hemapani/ Photos: http://www.flickr.com/photos/hemapani/ Phone: 0772360902
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