Sentence embedding in keras

问题:

I am trying a simple document classification using sentence embeddings in keras.

I know how to feed word vectors to a network, but I have problems using sentence embeddings. In my case, I have a simple representation of sentences (adding the word vectors along the axis, for example np.sum(sequences, axis=0)).

My question is, what should I replace the Embedding layer with in the code below to feed sentence embeddings instead?

model = Sequential()
model.add(Embedding(len(embedding_weights), len(embedding_weights[0]), weights=[embedding_weights], mask_zero=True, 
input_length=MAX_SEQUENCE_LENGTH, trainable=True))
model.add(LSTM(LSTM_SIZE, activation='relu'))

I've tried Embedding layer (without setting the weights) and Input layer but both gave errors.