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 <thu...@gmail.com> 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/
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