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

Output
tensor([[[[-4.2000, -6.6859, -4.9735, -3.5615],
[-1.2363, 0.5150, -1.8602, -4.7287],
[-2.1209, -4.1894, -4.3694, -5.5897],
[-4.3954, -6.1578, -4.5968, -5.0000]]]],
grad_fn=<MkldnnConvolutionBackward>)
These results are gibberish now (and yours are going to be different than mine)
because the convolutional module randomly initializes the weights representing
the kernel / filter.
That’s the whole point of the convolutional module: It will learn
the kernel / filter on its own.
In traditional computer vision, people would develop different
filters for different purposes: blurring, sharpening, edge
detection, and so on.
But, instead of being clever and trying to manually devise a filter
that does the trick for a given problem, why not outsource the
filter definition to the neural network as well? This way, the
network will come up with filters that highlight features that are
relevant to the task at hand.
It’s no surprise that the resulting image shows a grad_fn attribute
now: It will be used to compute gradients so the network can
actually learn how to change the weights representing the filter.
"Can we tell it to learn multiple filters at once?"
Sure we can; that’s the role of the out_channels argument. If we set it to 2, it will
generate two (randomly initialized) filters:
conv_multiple = nn.Conv2d(
in_channels=1, out_channels=2, kernel_size=3, stride=1
)
conv_multiple.weight
354 | Chapter 5: Convolutions