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Results with ReLUs and different subjects. Using ReLUs on the hidden layers. Using more hidden units in the NSNN. 1 Using ReLUs on the hidden layers. This effect was found to disappear after around 75 epochs.
My journal for IFT6266 projects. Increasing the number of batches per iteration. Instead, a random subset of the training examples is sampled on every epoch. This way, I get feedback from the monitoring channels more often. The learning rate is also adjusted more often this way.
I proposed to use the implementation composed of a SdA and a MLP of 2 hidden layers for each phoneme. I will present in this last post the results of this proposal. The hyperparameters of the implementation are almost the same as the ones used in my final experiment post.
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