going beyond the sequential modelthe keras functional api following is an example of how you can build such model with the functional api you set up two independent branchesencoding the text input and the question input as representation vectorsthenconcatenate these vectorsand finallyadd softmax classifier on top of the concatenated representations listing funct ional api implement at ion of wo-input quest ion-answering model from keras models import model from keras import layers from keras import input text_vocabulary_size question_vocabulary_size answer_vocabulary_size the text input is variablelength sequence of integers note that you can optionally name the inputs text_input input(shape=(none,)dtype='int 'name='text'embedded_text layers embedding text_vocabulary_size)(text_inputembeds the inputs into sequence of vectors of size encodes the vectors in single vector via an lstm encoded_text layers lstm( )(embedded_textquestion_input input(shape=(none,)dtype='int 'name='question'embedded_question layers embedding question_vocabulary_size)(question_inputencoded_question layers lstm( )(embedded_questionsame process (with different layer instancesfor the question concatenated layers concatenate([encoded_textencoded_question]axis=- concatenates the encoded question and encoded text answer layers dense(answer_vocabulary_sizeactivation='softmax')(concatenatedmodel model([text_inputquestion_input]answermodel compile(optimizer='rmsprop'loss='categorical_crossentropy'metrics=['acc']adds softmax classifier on top at model instantiationyou specify the two inputs and the output nowhow do you train this two-input modelthere are two possible apisyou can feed the model list of numpy arrays as inputsor you can feed it dictionary that maps input names to numpy arrays naturallythe latter option is available only if you give names to your inputs listing feeding dat mult -input model import numpy as np generates dummy numpy data num_samples max_length text np random randint( text_vocabulary_sizesize=(num_samplesmax_length)licensed to