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

size = 5
weight = torch.ones(size) * 0.2
F.conv1d(torch.as_tensor(temperatures).float().view(1, 1, -1),
weight=weight.view(1, 1, -1))
Output
tensor([[[8.4000, 8.0000, 6.4000, 3.4000, 2.2000,
1.8000, 2.0000, 1.8000, 2.0000]]])
Does it look familiar? That’s a moving average, just like those we used in Chapter 6.
"Does it mean every 1D convolution is a moving average?"
Well, kinda … in the functional form above, we had to provide the weights, but, as
expected, the corresponding module (nn.Conv1d) will learn the weights itself. Since
there is no requirement that the weights must add up to one, it won’t be a moving
average but rather a moving weighted sum.
Moreover, it is very unlikely we’ll use it over a single feature like in the example
above. Things get more interesting as we include more features to be convolved
with the filter, which brings us to the next topic…
Shapes
The shapes topic, one more time, I know—unfortunately, there is no escape from it.
In Chapter 4 we discussed the NCHW shape for images:
• N stands for the Number of images (in a mini-batch, for instance)
• C stands for the number of Channels (or filters) in each image
• H stands for each image’s Height
• W stands for each image’s Width
For sequences, the shape should be NCL:
• N stands for the Number of sequences (in a mini-batch, for instance)
• C stands for the number of Channels (or filters) in each element of the
sequence
1D Convolutions | 671