Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Now we’re talking! The last hidden state, (-0.5297, 0.3551), is the representation of
the full sequence.
Figure 8.9 depicts what the loop above looks like at the neuron level. In it, you can
easily see what I call "the journey of a hidden state": It is transformed, translated
(adding the input), and activated many times over. Moreover, you can also see that
the data points are independently transformed—the model will learn the best way
to transform them. We’ll get back to this after training a model.
At this point, you may be thinking:
"Looping over the data points in a sequence?! That looks like a lot of
work!"
And you’re absolutely right! Instead of an RNN cell, we can use a full-fledged…
RNN Layer
The nn.RNN layer takes care of the hidden state handling for us, no matter how long
the input sequence is. This is the layer we’ll actually be using in the model. We’ve
been through the inner workings of its cells, but the full-fledged RNN offers many
more options (stacked and / or bidirectional layers, for instance) and one tricky
thing regarding the shapes of inputs and outputs (yes, shapes are a kinda recurrent
problem—pun intended!).
Recurrent Neural Networks (RNNs) | 601