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

Let’s visualize it.
Figure 4.6 - Deep-ish model
By the way, in the figure above, the subscripts for both w and z represent the zerobased
indices for layer and unit: In the output layer, for instance, w 20 represents the
weights corresponding to the first unit (#0) of the third layer (#2).
What’s happening here? Let’s work out the forward pass; that is, the path from
inputs (x) to output (y):
1. An image is flattened to a tensor with 25 features, from x 0 to x 24 (not depicted
in the figure above).
2. The 25 features are forwarded to each of the five units in Hidden Layer #0.
3. Each unit in Hidden Layer #0 use its weights, from w 00 to w 04 , and the features
from the Input Layer to compute its corresponding outputs, from z 00 to z 04 .
4. The outputs of Hidden Layer #0 are forwarded to each of the three units in
Hidden Layer #1 (in a way, the outputs of Hidden Layer #0 work as if they were
features to Hidden Layer #1).
5. Each unit in Hidden Layer #1 uses its weights, from w 10 to w 12 , and the z 0 values
from the preceding hidden layer to compute its corresponding outputs, from z 10
to z 12 .
6. The outputs of Hidden Layer #1 are forwarded to the single unit in the output
layer (again, the outputs of Hidden Layer #1 work as if they were features to the
Output Layer).
7. The unit in the Output Layer uses its weights (w 20 ) and the z 1 values from the
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