How to use CNN to deal with a 2D regression problem? I have seven measurements (Obs1-7), each measurement has the dimension of [x,y,t] where x and y are coordinates and t is time. Now I want to build a model that uses the first 6 measurements to predict the last one, saying Obs7=f(Obs1~6). I want to use CNN to distil this relationship f. I had some experience using CNN for classification, but have no idea how to deal with such 2D/3D regression. Could someone please give me some ideas? Thanks!
It should even be easier than classification: you do not need the final layer.

Your input layer should have 18 nodes $(x_i,y_i,t_i)|i=1..6$

Hidden layers as you see fit (experiment with it, depending on data and results).
I would expect the best results from a fully connected layer and a 'relu' layer.

The output layer has 3 nodes $(x_7, y_7, t_7)$

Train the model.

Evaluation is just feed forward of a set of 18 values, and retrieving 3 results from the output layer nodes.