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

Output
tensor([[53, 53],
[75, 75]])
Once again, the model’s accuracy is perfect. Maybe you’ve noticed it was much
faster to train too. How come this simple model performed so well? Let’s try to
figure out what it did under the hood…
Visualizing the Model
The key component of our model is the nn.Conv1d layer, so let’s take a look at its
state_dict():
model.conv1d.state_dict()
Output
OrderedDict([('weight', tensor([[[-0.2186, 2.3289],
[-2.3765, -0.1814]]], device='cuda:0')),
('bias', tensor([-0.5457], device='cuda:0'))])
Then, let’s see what this filter is doing by feeding it a "perfect" square starting at
corner A and going counterclockwise (sequence ADCB):
Figure 8.34 - Applying filter over the "perfect" square
The figure above shows us the element-wise multiplication and result for the first
region, corresponding to the AD edge of our square (without including the bias
value from the convolutional layer). The outputs on the right are the "edge features."
678 | Chapter 8: Sequences