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
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