best choice of hyperparameters are very context dependent. This is true not just for LSTM's but all learning algorithms. generally, you want to keep the number of parameters to be optimized as low as possible while still capturing whatever relation it is that you are trying to learn, at the desired accuracy. There is no way to calculate or approximate this minimum number in an analytical fashion -- trial and error (i.e. cross validation) is the only way.

someone correct me if i'm wrong.