title: Concatenate two models with tensorflow.keras
body: <p>I'm currently studying neural network models for image analysis, with the MNIST dataset. I first used only the image to build a first model. Then I created a additionnal variable, which is :
  0 when the digit is actually between 0 and 4, and 1 when it's greater or equal than 5.</p>
  
  <p>Therefore, I want to build a model that can take these two informations : the image of the digit, and that additionnal variable I juste created.</p>
  
  <p>I created the two first models, one for the image and one for the exogenous variable, as follow :</p>
  
  <pre><code>import tensorflow as tf
  from tensorflow import keras
  
  
  image_model = keras.models.Sequential()
  
  #First conv layer :
  image_model.add( keras.layers.Conv2D( 64, kernel_size=3,
                                                 activation=keras.activations.relu,
                                        input_shape=(28, 28, 1) ) )
  
  #Second conv layer :
  image_model.add( keras.layers.Conv2D( 32, kernel_size=3, activation=keras.activations.relu ) )
  
  #Flatten layer :
  image_model.add( keras.layers.Flatten() )
  
  print( image_model.summary(), '\n' )
  
  
  
  
  info_model = keras.models.Sequential()
  
  info_model.add( keras.layers.Dense( 5, activation=keras.activations.relu, input_shape=(1,) ) )
  
  print( info_model.summary() )
  </code></pre>
  
  <p>Then I would like to concatenate both final layers, to finally put another dense layer with softmax to predict class probabilities.</p>
  
  <p>I know it's feasible using Keras functionnal API, but how could one do it using tf.keras ?</p>