Sorry to bother again, I have more questions:

In the unet example, if I do the image mapping, for example, if I have 10 classes for the labels, the last layer of the network is log_softmax, does it mean that the output of the network is the probability map of of every pixel.
If so, after using the NLLLoss and the Adam optimisation, the weight in the network is optimized, now I feed the network with a random training image, the output is a probability map, if I want to visualize it, I need to simply imshow(output) or I have to remap it back?