Existe uma maneira de calcular o número total de parâmetros em uma rede LSTM.

Encontrei um exemplo, mas não tenho certeza de quão corretoesta é ou se eu entendi corretamente.

Por exemplo, considere o seguinte exemplo: -

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.layers import Embedding
from keras.layers import LSTM
model = Sequential()
model.add(LSTM(256, input_dim=4096, input_length=16))
model.summary()


Output

____________________________________________________________________________________________________
Layer (type)                       Output Shape        Param #     Connected to                     
====================================================================================================
lstm_1 (LSTM)                      (None, 256)         4457472     lstm_input_1[0][0]               
====================================================================================================
Total params: 4457472
____________________________________________________________________________________________________


As per My understanding n is the input vector lenght.
And m is the number of time steps. and in this example they consider the number of hidden layers to be 1.

Hence according to the formula in the post. 4(nm+n^2) in my example m=16;n=4096;num_of_units=256

4*((4096*16)+(4096*4096))*256 = 17246978048


Why is there such a difference? 
Did I misunderstand the example or was the formula wrong ?