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

w_nn_equiv = w_nn_output.mm(w_nn_hidden1.mm(w_nn_hidden0))
In my opinion, the sequence of operations looks more clear using "@" for matrix
multiplication.
Next, we need to compare them to the weights of the shallow model; that is, the
logistic regression:
w_logistic_output = model_logistic.output.weight.detach()
w_logistic_output.shape
Output
torch.Size([1, 25])
Same shape, as expected. If we compare the values one by one, we’ll find that they
are similar, but not quite the same. Let’s try to grasp the full picture by looking at a
picture (yes, pun intended!).
Figure 4.9 - Comparing weights of deep-ish and shallow models
On the left, we plot all 25 weights / parameters for both models. Even though they
are not quite the same, the similarity is striking. On the right, we can appreciate
that the weights are, indeed, highly correlated.
Deep-ish Model | 309