Hi K. Frank,
Thank you for taking the time to answer my questions! I appreciate it!



 KFrank:

I might call what you’re talking
about something like continuous value estimation , rather
than “classification.”


I think that’s a better name for this project. I’ll use that from now on.



 KFrank:

So the straightforward approach would be to have a data file
that has the real-number attributes in it. It could be as
simple as containing a sequence of (pairs of) real numbers,
with the understanding that the order in which you read in
your images will (somehow) match the order of the attribute
values in your file. Or your data file could have a character
string for the filename of each image file, followed by two
numbers for the values of the two attributes.


Yes, I have this setup nicely. I can parse a png file name for the attributes and make them into labels.



 KFrank:

For your loss function, you would probably simply use the squared
error between your predictions and actuals ( nn.MSELoss ).


I agree, after looking through the loss functions this seems like a good choice for real value estimation.



 KFrank:

You might want to weight the two terms so they have comparable
size lest you preferentially optimize for accuracy in A at the
cost of B.


This is very true. Since the values ranges for A and B are much different I would need to go through this. I haven’t looked into how to weight the MSE loss function yet, but is it possible that you could give some guidance on this?
Lastly, I wanted to make batched sets of data. I found that Pytorch does this automatically through the Dataloader class via a parameter. I am confused on whether this Dataloader still needs the dataset parameter structured in the hierarchical fashion previously mentioned. If that’s the case would there be anything else to quickly batch data or should I code my own function?
Thanks again,
Andrew