Merge two models - Keras I was reading through many blogs and understood the relevance and scenario of having merging two model. But, how to decide the Merge mode for two models. ie. concat, sum, dot, etc.

For eg. I am working on the task of Auto Image captioning. So, captions and Images are 2 kinds of input that I need to handle and merge them at certain point for model to let know which caption is for which image. I am learning the text representation and Image representation by 2 different network designs. Now, at the state after learning representation for both the inputs, how do i decide what ways(concat, add, etc) to use to join/merge two representations.
Generally, I'd say its the same way as finding your "best" feature list, pre-processing methods and algorithm - you'd find most answers starting with "give it a try and compare", "check which combination gives you a better cross-validation?" etc..

More specifically, when there is no farther documentation (and its an open source project) I usually take a look at the code for more intuitions.

In this case concat, dot, average and the like where strait forward implemented as it sounds (concatenating date, averaging, etc..), but it does imply about the amount of processing needed: some of the merging functions adds to the input needed to be processed (concat, add) and some reduces it like an aggregation function (average, min, max)

Beyond that the resulted tensor object will perform a bit different for different datasets. Please give it a try and let us know what merging combination worked best for you, and the dataset type :)