multi-input.py

from keras.models import Model
from keras import layers
from keras import Input
text_vocabulary_size = 10000
question_vocabulary_size = 10000
answer_vocabulary_size = 500


text_input = Input(shape=(None,), dtype='int32', name='text')
embedded_text = layers.Embedding(
  64, text_vocabulary_size)(text_input)
  
encoded_text = layers.LSTM(32)(embedded_text)

question_input = Input(shape=(None,),
                       dtype='int32',
                       name='question')

embedded_question = layers.Embedding(
  32, question_vocabulary_size)(question_input)

encoded_question = layers.LSTM(16)(embedded_question)

concatenated = layers.concatenate([encoded_text, encoded_question], axis = -1)
answer = layers.Dense(answer_vocabulary_size,
                      activation='softmax')(concatenated)

model = Model([text_input, question_input], answer)

model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['acc'])
              
import numpy as np

num_samples = 1000
max_length = 100

text = np.random.randint(1, text_vocabulary_size,
                         size=(num_samples, max_length))
 
question = np.random.randint(1, question_vocabulary_size,
                             size=(num_samples, max_length))
                             
answers = np.random.randint(0, 1,
                            size=(num_samples, answer_vocabulary_size))
                            
model.fit([text, question], answers, epochs=10, batch_size=128)