X = Input Sequence of length n.
H = LSTM(X); Note that here the LSTM has return_sequences = True,
    so H is a sequence of vectors of length n.
s is the hidden state of the LSTM (h and c)

h is a weighted sum over H: 加权和
h = sigma(j = 0 to n-1)  alpha(j) * H(j)

weight alpha[i, j] for each hj is computed as follows:
H = [h1,h2,...,hn]
M = tanh(H)
alhpa = softmax(w.transpose * M)

h# = tanh(h)
y = softmax(W * h# + b)

J(theta) = negative_log_likelihood + regularity