Feature Space: What Does a Linear Model Actually See?

Fri, 28 Aug 2026 15:30:00 +0100

Here is a sequence classifier. Eight examples, 128 positions each, 768 features per position, and a linear head that produces one score.

x = torch.randn(8, 128, 768)
classifier = nn.Linear(768, 1)

scores = classifier(x)

input           (8, 128, 768)
Linear(768,1)  -> (8, 128, 1)

One thousand and twenty-four scores where eight were expected. Nothing raised, nothing is non-finite, and the shape is entirely predictable once you know the rule. The layer did exactly what the tensor asked of it: nn.Linear transforms the last axis and preserves every axis before it, so it produced one score for every (example, position) pair — 128 scores per example, computed independently.

Feature Space on Programmer.ie

Feature Space: What Does a Linear Model Actually See?