I have a pytorch model which outputs `nan`

after few epochs. There are no `nan`

in the input as well as no `logs or divisions`

in the loss that can make `nan`

. On debugging I found that the last two layers of the model outputs the `nan`

at some places. However, it automatically resolves after few epochs and then again `nan`

after few epochs.

What could be the potential reason for this. My loss function consists of regression ad cross entropy-loss. For regression loss, I am calculating the the L1 loss only at places where target values are less than 1 where as cross-entropy loss over whole grid by converting target and output of models as occupancy grid (values less than 1 as 1 and greater than 1 as 0).