Hi Sam, thank you for responding to my post,
I guess it was pretty important and I should have clarified it in my post, but the digits on the image are to be displayed from left to right in the array, as they appear in the same order in the image.
For example, if the the left most digit in the image is 1, then 5 appears somewhere to the right and 9 is the right-most digit, then the label is [1,5,9,-1,-1]. We include the two -1’s in the array as placeholder values, since the list must be of length 5 as that is how the labels are represented in the original dataset. I figured keeping all lists at length 5 would simplify training, but maybe I’m wrong? Hopefully this clears up the problem a little bit.
The reason I chose to use a CNN was because we are predicting many mutually non-exclusive class labels corresponding with a 70x70x1 image input, so I figured a convolutions was the best approach to analyzing visual imagery. But of course I’m open to any and all suggestions/advice! I’m not very familiar with expressing labels using multi-hot encoding, could you could provide a quick head-start on the topic and how it could be applied in the context of this problem?