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

sequence so far, and a data point from the sequence (like the coordinates of
one of the corners from a given square).
3. The two inputs are used to produce a new hidden state (h 0 for the first data
point), representing the updated state of the sequence now that a new point
was presented to it.
4. The new hidden state is both the output of the current step and one of the
inputs of the next step.
5. If there is yet another data point in the sequence, it goes back to Step #2; if
not, the last hidden state (h 1 in the figure above) is also the final hidden state (
h f ) of the whole RNN.
Since the final hidden state is a representation of the full sequence, that’s what
we’re going to use as features for our classifier.
In a way, that’s not so different from the way we used CNNs:
There, we’d run the pixels through multiple convolutional blocks
(convolutional layer + activation + pooling) and flatten them into
a vector at the end to use as features for a classifier.
Here, we run a sequence of data points through RNN cells and
use the final hidden state (also a vector) as features for a
classifier.
There is a fundamental difference between CNNs and RNNs, though: While there
are several different convolutional layers, each learning its own filters, the RNN
cell is one and the same. In this sense, the "unrolled" representation is misleading: It
definitely looks like each input is being fed to a different RNN cell, but that’s not the
case.
There is only one cell, which will learn a particular set of weights
and biases, and which will transform the inputs exactly the same
way in every step of the sequence. Don’t worry if this doesn’t
completely make sense to you just yet; I promise it will become
more clear soon, especially in the "Journey of a Hidden State"
section.
Recurrent Neural Networks (RNNs) | 593