A puzzling question about the implementation of ppi

[TaengguTae]

Thank you @gordicaleksa for the tutorial and implementation of GAT, which made me understand it smoothly and walk through the codes sucessfully !

Here I still have a question when I run the codes of PPI dataset. In the code you have mentioned that skip connection is important and keep it otherwise the micro-F1 is almost 0, and I am wondering that the reason of the appearance of 0 is that, something leads some values(such as loss, feature etc.) to be nan during training?

More details:
The framework I used to train is paddlepaddle instead of pytorch. When I ran my code, I found that the value of loss would be nan finally, making the micro-F1 be 0. This strange phenomenon occurs whether I use skip connection or not. Furthemore, when I set skip connection to False, the value of micro-F1 first rises normally to about 0.7, then suddenly becomes 0, however, when I set skip connection to True, the value micro-F1 also rises normally first, to around 0.9, and suddenly becomes 0.
When I checked it, I found the appearance of NaN during training. This problem has always puzzled me, so I wonder that is there the same issue during your implemrntation?