RNN topologies
We have seen examples of how MLP and CNN architectures can be composed to form more complex networks. RNNs offer yet another degree of freedom, in that it allows sequence input and output. This means that RNN cells can be arranged in different ways to build networks that are adapted to solve different types of problems. Figure 4 shows five different configurations of inputs, hidden layers, and outputs, represented by red, green, and blue boxes respectively:
Of these, the first one (one-to-one) is not interesting from a sequence processing point of view, since it can be implemented as a simple Dense network with one input and one output.
The one-to-many case has a single input and outputs a sequence. An example of such a network might be a network that can generate text tags from images [6], containing short text descriptions of different aspects of the image. Such a network would be trained with image input and labeled sequences of text representing the image tags...
