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

Output
tensor([[66, 66],
[62, 62]])
1D Convolutions
In Chapter 5, we learned about convolutions, their kernels and filters, and how to
perform a convolution by repeatedly applying a filter to a moving region over the
image. Those were 2D convolutions, though, meaning that the filter was moving in
two dimensions, both along the width (left to right), and the height (top to bottom)
of the image.
Guess what 1D convolutions do? They move the filter in one dimension, from left
to right. The filter works like a moving window, performing a weighted sum of the
values in the region it has moved over. Let’s use a sequence of temperature values
over thirteen days as an example:
temperatures = np.array([5, 11, 15, 6, 5, 3, 3, 0, 0, 3, 4, 2, 1])
Figure 8.30 - Moving window over series of temperatures
Then, let’s use a window (filter) of size five, like in the figure above. In its first step,
the window is over days one to five. In the next step, since it can only move to the
right, it will be over days two to six. By the way, the size of our movement to the
right is, once again, known as the stride.
Now, let’s assign the same value (0.2) for every weight in our filter and use
PyTorch’s F.conv1d() to convolve the filter with our sequence (don’t mind the
shape just yet; we’ll get back to it in the next section):
670 | Chapter 8: Sequences