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

Output
tensor([[22., 23., 11., 24., 7., 1., 13., 13., 13.]])
Dimensions
We’ve performed convolutions, padding, and pooling in two dimensions because
we’re handling images. But there are one- and three-dimensional versions of some
of them as well:
• nn.Conv1d and F.conv1d(); nn.Conv3d and F.conv3d()
• nn.ConstandPad1d and nn.ConstandPad3d
• nn.ReplicationPad1d and nn.ReplicationPad3d
• nn.ReflectionPad1d
• nn.MaxPool1d and F.max_pool1d(); nn.MaxPool3d and F.max_pool3d()
• nn.AvgPool1d and F.avg_pool1d(); nn.AvgPool3d and F.avg_pool3d()
We will not venture into the third dimension in this book, but we’ll get back to onedimension
operations later.
"Aren’t color images three-dimensional since they have three
channels?"
Well, yes, but we will still be applying two-dimensional convolutions to them. We’ll
go through a detailed example using a three-channel image in the next chapter.
Typical Architecture
A typical architecture uses a sequence of one or more typical convolutional
blocks, with each block consisting of three operations:
1. Convolution
2. Activation function
3. Pooling
As images go through these operations, they will shrink in size. After three of these
blocks (assuming kernel size of two for pooling), for instance, an image will be
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