Suppose I want to predict the probability of a person to buy something. I want to analyze the person image and I can use a convolutional neural network, but I also want to input in my predictive model how many times he bought something, where he goes more frequently and so on. What is a correct way to input in a deep learning model informations from different deep learning models?
Actually you can merge two different DL model into a big one.
I'm using Keras+Tensorflow for deep learning, and there's an example about models with multiple inputs(outputs). In this example, tweets are fed into a RNN, while the extra data are fed into a fully-connected layer along with the output of this RNN.