Testing different RNN models

Now that we have our training data preprocessed and ready in tensor format, we can try a slightly different approach than previous chapters. Normally, we would go ahead and build a single model and then proceed to train it. Instead, we will construct several models, each reflecting a different RNN architecture, and train them successively to see how each of them do at the task of generating character-level sequences. In essence, each of these models will leverage a different learning mechanism and induct its proper language model, based on sequences of characters it sees. Then, we can sample the language models that are learned by each network. In fact, we can even sample our networks in-between training epochs to see how our network is doing at generating Shakespearean phrases at the level of each epoch. Before we continue to build our networks, we...