Composing deep networks

We have looked extensively at these three basic deep learning networks—the fully connected network (FCN), the CNN and the RNN models. While each of these have specific use cases for which they are most suited, you can also compose larger and more useful models by combining these models as Lego-like building blocks and using the Keras functional API to glue them together in new and interesting ways.

Such models tend to be somewhat specialized to the task for which they were built, so it is impossible to generalize about them. Usually, however, they involve learning from multiple inputs or generating multiple outputs. One example could be a question answering network, where the network learns to predict answers given a story and a question. Another example could be a siamese network that calculates similarity between a pair of images, where the network is trained to predict either a binary (similar/not similar) or categorical (gradations of similarity) label using a...