Laura_Montalvo:

I know that with RNN’s we must be careful about gradient exploding so we need to use gradient clipping technique but does this apply to LSTM too?


In Sequence to Sequence Learning with Neural Networks 2 (which might be considered a bit old by now) the authors claim:

Although LSTMs tend to not suffer from the vanishing gradient problem, they can have exploding gradients. Thus we enforced a hard constraint on the norm of the gradient [10,25] by scaling it when its norm exceeded a threshold. …

So I would assume that LSTMs can also suffer from exploding gradients.



 Laura_Montalvo:

Also, how could I see gradient values so I know which value to set in gradient clipping?


You could register hooks for each parameter and check the gradient norm, min/max values etc. via:
for param in model.parameters():
    param.register_hook(your_check_method)




 Laura_Montalvo:

And how to know when the gradient is exploding?


You could use the same approach as used in the linked paper, but you might also find more recent papers with more “modern” approaches.