Ah okay, I have done the first two dimensions in this case, my data is in the form of numeric tokens as you mentioned, and each sentence is padded to be the same length. It is the last part with which I am not too familiar. I know of embedding techniques like word2vec and glove. So I understand what you mean when you wrote “you usually map each word to a vector which represents the meaning” but how is this done in the code itself? Is this what it means to initialise the hidden state? Is this hidden state the vector mappings of your words? I have seen code where the hidden state is initialised within the Class defining the model though, so I don’t understand how you map before instantiating the class. Or I could be way off and it is something else?
And what kind of Python data structure would you store this three dimensional data in then? Thanks!
For some more context, the RNN I used started with an embedding layer, then an lstm, followed by a fully connected layer and finally a SoftMax output. What you wrote makes it seem like my data (which is basically a list of lists, with each document being one list, inside which each sentence is another list with each work a numeric token) is in the correct form to be passed into the model as long as it begins with an embedding layer. Or maybe I am missing something.