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

Output
Parameter containing:
tensor([[[[ 0.0009, -0.1240, -0.0231],
[-0.2259, -0.2288, -0.1945],
[-0.1141, -0.2631, 0.2795]]],
[[[-0.0662, 0.2868, 0.1039],
[-0.2823, 0.2307, -0.0917],
[-0.1278, -0.2767, -0.3314]]]], requires_grad=True)
See? There are two filters represented by three-by-three matrices of weights
(your values are going to be different than mine).
Even if you have only one channel as input, you can have many
channels as output.
Spoiler alert: The filters learned by the network are going to
show edges, patterns, and even more complex shapes (sometimes
resembling faces, for instance). We’ll get back to visualizing
those filters later in this chapter.
We can also force a convolutional module to use a particular filter by setting its
weights:
with torch.no_grad():
conv.weight[0] = kernel_identity 1
conv.bias[0] = 0
1
1 weight[0] and bias[0] are indexing the first (and only) output channel in this
convolutional layer.
IMPORTANT: Setting the weights is a strictly no-gradient
operation, so you should always use the no_grad() context
manager.
In the code snippet above, we are forcing the module to use the (boring) identity
kernel we have been using so far. As expected, if we convolve our input image we’ll
Convolutions | 355