I want to make a network, specifically a CNN for image recognition, that takes an input, processes it the same way for several layers, and then at some point splits before coming to two different outputs. Is it possible to create a network such as this? It would look something like this:
Input -> Conv -> Pool -> Conv -> Pool ---------> Dense -> Output 1
|| ----> Dense -> Output 2
I.E. it splits off after the second pooling layer into separate fully connected layers. Of course, it has to train to both outputs, so that it is producing minimal error on both separate outputs using these common convolutional layers. Also, I am using Python Keras, and it would help if there was some way to do this using Keras in some way. Thank you!