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
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