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

Output
tensor([[[[9., 5., 0., 7.],
[0., 2., 4., 6.],
[7., 6., 6., 8.],
[3., 8., 5., 1.]]]])
As expected, we got the same result shown in the previous section. No surprises
there.
Now, let’s turn our attention to PyTorch’s convolution module, nn.Conv2d. It has
many arguments; let’s focus on the first four of them:
• in_channels: number of channels of the input image
• out_channels: number of channels produced by the convolution
• kernel_size: size of the (square) convolution filter / kernel
• stride: the size of the movement of the selected region
There are a couple of things to notice here. First, there is no argument for the
kernel / filter itself, there is only a kernel_size argument.
The actual filter, that is, the square matrix used to perform
element-wise multiplication, is learned by the module.
Second, it is possible to produce multiple channels as output. It simply means the
module is going to learn multiple filters. Each filter is going to produce a different
result, which is being called a channel here.
So far, we’ve been using a single-channel image as input, and applying one filter
(size three by three) to it, moving one pixel at a time, resulting in one output per
channel. Let’s do it in code:
conv = nn.Conv2d(
in_channels=1, out_channels=1, kernel_size=3, stride=1
)
conv(image)
Convolutions | 353