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. Author manuscript; available in PMC: 2020 Sep 1.
Published in final edited form as: Neuroimage. 2019 May 23;198:303–316. doi: 10.1016/j.neuroimage.2019.05.049

Figure 2.

Figure 2

A) Hyper-parameter search for optimum number of filters. We selected 50 filters as the optimum point in the hyper-parameter search. For every layer, we used the same number of filters. Increasing the number of filters past 50 did not improve performance on the validation set. B) Hyper-parameter search for the optimum number of layers: After choosing the number of filters, we tested different numbers of hidden layers. There was no improvement in validation performance after 19 hidden layers.