Summarizing All the Models
In this chapter, we've looked at different variants of RNNs – from plain RNNs to LSTMs to GRUs. We also looked at the bidirectional approach and the stacking approach to using RNNs. Now is a good time to take a holistic look at things and make a comparison between the models. Let's look at the following table, which compares the five models in terms of parameters, training time, and performance (that is, the level of accuracy on our dataset):
Note
As mentioned earlier in the chapter, while working through the practical elements, you may have obtained values different from the ones shown above; however, the test accuracies you obtain should largely agree with ours. If the model's performance is very different, you may want to tweak the number of epochs.
Plain RNNs are the lowest on parameters and have the lowest training times but have the lowest accuracy of all the...