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

• second block: produces 16-channel 5x5 pixels
• third block: produces 120-channel single pixel (1x1)
Then, these 120 values (or features) are flattened and fed to a typical hidden layer
with 84 units. The last step is, obviously, the output layer, which produces ten
logits to be used for digit classification (from 0 to 9, there are ten classes).
"Wait, we haven’t seen any of those multiclass classification problems
yet!"
You’re right, it is about time. But we’re still not using MNIST for this.
A Multiclass Classification Problem
A problem is considered a multiclass classification problem if there are more than
two classes. So, let’s keep it as simple as possible and build a model to classify
images into three classes.
Data Generation
Our images are going to have either a diagonal or a parallel line, BUT this time we
will make a distinction between a diagonal line tilted to the right, a diagonal line
tilted to the left, and a parallel line (it doesn’t matter if it is horizontal or vertical).
We can summarize the labels (y) like this:
Line
Label / Class Index
Parallel (Horizontal OR Vertical) 0
Diagonal, Tilted to the Right 1
Diagonal, Tilted to the Left 2
Also, let’s generate more and larger images: one thousand images, each one tenby-ten
pixels in size.
Data Generation
1 images, labels = generate_dataset(
2 img_size=10, n_images=1000, binary=False, seed=17
3 )
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