Keras: Multiple outputs and multiple losses - PyImageSearch

A couple weeks ago we discussed how to perform multi-label classification using Keras and deep learning. Today we are going to discuss a more advanced technique called multi-output classification. And how are you supposed to keep track of all these terms? You can even combine multi-label classification with multi-output classification so that each fully-connected head can predict multiple outputs! If this is starting to make your head spin, no worries -- I've designed today's tutorial to guide you through multiple output classification with Keras. It's actually quite easier than it sounds. That said, this is a more advanced deep learning technique we're covering today so if you have not already read my first post on Multi-label classification with Keras make sure you do that now. From there, you'll be prepared to train your network with multiple loss functions and obtain multiple outputs from the network.