Multiscale CNN - Keras Implementation
I do not understand why to have 3 CNNs because you would mostly have the same results than on a single CNN. Maybe you could train faster.
Perhaps you could also do pooling and some resnet operation (I guess this could prove similar to what you want).
Nevertheless, for each CNN you need a cost function in order to optimize the "heuristic" you use (eg: to improve recognition). Also, you could do something as in the NN Style Transfer in which you compare results between several "targets" (the content and the style matrices); or simply train 3 CNNs then cutoff the last layers (or freeze them) and train again with the already trained weights but now with your target FN layer...
I want to implement a multiscale CNN in python. My aim is to use three different CNNs for three different scales and concatenate the final outputs of the final layers and feed them to a FC layer to take the output predictions.
But I don't understand how can I implement this. I know how to implement a single scale CNN. 
Could anyone help me in this?