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

figures at the beginning of this chapter). Let’s stick with a kernel size of three,
which will reduce the image size by two pixels in each dimension (we are not using
padding here).
Our featurizer, which will encode our images into features using convolutions,
should look like this:
Model Configuration — Featurizer
1 torch.manual_seed(13)
2 model_cnn1 = nn.Sequential()
3
4 # Featurizer
5 # Block 1: 1@10x10 -> n_channels@8x8 -> n_channels@4x4
6 n_channels = 1
7 model_cnn1.add_module('conv1', nn.Conv2d(
8 in_channels=1, out_channels=n_channels, kernel_size=3
9 ))
10 model_cnn1.add_module('relu1', nn.ReLU())
11 model_cnn1.add_module('maxp1', nn.MaxPool2d(kernel_size=2))
12 # Flattening: n_channels _ 4 _ 4
13 model_cnn1.add_module('flatten', nn.Flatten())
I am keeping the number of channels as a variable, so you can try different values
for it if you like.
Let’s follow what happens to an input image (single-channel, 10x10 pixels in
size—1@10x10):
• The image is convolved with the kernel, and the resulting image has one
channel, and is 8x8 pixels in size (1@8x8).
• A ReLU activation function is applied to the resulting image.
• The "activated" image goes under a max pooling operation with a kernel size of
two, so it is divided into 16 chunks of size two-by-two, resulting in an image
with one channel, but only 4x4 pixels in size (1@4x4).
• These 16 values can be considered features, and are flattened into a tensor
with 16 elements.
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