Hey everyone,
I am still pretty new to neural networks and pytorch. Now that my Neural Net seems to be computing pretty great results, I am having a problem with the interpretation of the losses (training loss seems to be always higher than validation loss).
But first, here is the information about the data I have been working with:

3d CT images (so only one channel)
always same sizes for height (512) and width (512)
varying size for depth

Output should be denoised images.
Since the images of course are too big  to train with the whole image, I had to work with patches and took the same number of random sample patches from each image.
For training I am using the sample patches of 47 images and for validation of 12 images.
In order to be able to compare the losses obtained during training and validation to the testing loss of the complete images, I would like to have loss plots that show the average loss per voxel (this also makes sense to me since the images are not the same sizes in depth)
Now I already tried setting reduction=‘sum’ and adding all mini-batch losses which in the end of each epoch are devided by the whole number of training-patches or validation-patches and then devided by the number of voxels per patch.
But somehow this always gives me a higher loss for training than for validation. Especially for the first Epoch, the loss is easily 10 times higher for training.
I already tried a lot of loss calculations, but nothing seems right. Could someone please try to help me out?
Thank you in advance