Deep-Learning-with-PyTorch

196 CHAPTER 8 Using convolutions to generalize
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Locality
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Figure 8.1
Convolution: locality and translation invariance
It’s now possible to see the connection to what we were stating earlier: a convolution is
equivalent to having multiple linear operations whose weights are zero almost everywhere
except around individual pixels and that receive equal updates during training.
Summarizing, by switching to convolutions, we get
• Local operations on neighborhoods
• Translation invariance
• Models with a lot fewer parameters
The key insight underlying the third point is that, with a convolution layer, the number
of parameters depends not on the number of pixels in the image, as was the case
in our fully connected model, but rather on the size of the convolution kernel (3 × 3,
5 × 5, and so on) and on how many convolution filters (or output channels) we decide
to use in our model.
8.2 Convolutions in action
Well, it looks like we’ve spent enough time down a rabbit hole! Let’s see some PyTorch
in action on our birds versus airplanes challenge. The torch.nn module provides convolutions
for 1, 2, and 3 dimensions: nn.Conv1d for time series, nn.Conv2d for images,
and nn.Conv3d for volumes or videos.
For our CIFAR-10 data, we’ll resort to nn.Conv2d. At a minimum, the arguments we
provide to nn.Conv2d are the number of input features (or channels, since we’re dealing