Q: Architecture of CNN to accept 2 sets of inputs There are multiple examples how to build Tensorflow model to recognise cats and dogs from images. Now suppose I have audio associated with each picture and train separate network to recognise cats and dogs by sound.  
I want to feed predictions of both networks into another layer to combine results and increase final prediction success rate.
How should my model look like? 

A: Create two neural networks, that given a pair image-audio, you input each value to its corresponding net.
After the convolution steps or whatever you want to use, proceed as you would do with a normal CNN, in the last step before passing data to a FNN, when you flatten the data, do the same with the output of the audio NN.
So, as an example, if the output of the images one (flattened) has shape 2048 and the audio 4096 just append these two and make first layer of the FNN to have the sum of these shapes = 6144.
