LSTM Networks in PyTorch

The process of defining the LSTM network architecture in PyTorch is similar to that of any other neural network that we have discussed so far. However, it is important to note that, when dealing with sequences of data that are different from those of numbers, there is some preprocessing required in order to feed the network with data that it can understand and process.

Considering this, we need to explain the general steps for training a model to be able to take text data as inputs and retrieve a new piece of textual data. It is important to mention that not all the steps explained here are strictly required, but as a group, they make clean and reusable code for using LSTMs with textual data.

Preprocessing the Input Data

The first step is to load the text file into the code. This data will go through a series of transformations in order to be fed into the model properly. This is necessary because neural networks perform a series of mathematical computations...