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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?

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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.

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  • $\begingroup$ In general I can choose a model I want to merge informations about different inputs or is there some rules? $\endgroup$ – GGA Feb 5 '17 at 8:09
  • $\begingroup$ Personally speaking, we might merge different related data into one model, but it's hard to put the earth into a network and ask for predicting the future. More data leads to a good model, along with the difficulty on model training and tuning. $\endgroup$ – Icyblade Feb 5 '17 at 8:37

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