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

preceding hidden layer to compute its corresponding output (z 2 ).
8. z 2 is a logit, which is converted to a probability using a sigmoid function.
There are a couple of things to highlight:
• All units in the hidden layers, and the one in the output layer, take a set of
inputs (x or z) and perform the same operation (w T x or w T z, each using its own
weights, of course), producing an output (z).
• In the hidden layers, these operations are exactly like the logistic regression
models we have used so far, up to the point where the logistic regression
produced a logit.
• It is perfectly fine to think of the outputs of one layer as features of the next
layer; actually, this is at the heart of the transfer learning technique we’ll see in
Chapter 7.
• For a binary classification problem, the output layer is a logistic regression,
where the "features" are the outputs produced by the previous hidden layer.
Not so complicated, right? It actually seems like a natural extension of the logistic
regression. Let’s see how it performs in practice.
Deep-ish Model | 303