Thanks for your reply
Ok sorry I made some changes in the code and I forgot to fix that. The initial code was based on a classifier, that’s why there were some “useless” variables that weren’t needed at all. I edited the first post to make it clearer.
The input of the nework is one single channel (Black and White) image of [H,W]. The output of the network, right now, is one single continous variable that ranges from 60 to -60 degrees (related to the orientation of a robot). Therefore, the value “outputs” from inside the loop consists of an array that depends on batch_size (8) and the number of outputs of the network (1)
Example of printing “outputs” during one iteration of the for loop with batch size = 8:
tensor([[-48.1405],
[-49.2519],
[-56.2093],
[-49.5011],
[-45.5890],
[-50.0405],
[-50.4176],
[-51.5339]], device=‘cuda:0’, grad_fn=< AddmmBackward >)