Is training stuck, or is the model too simple?

Train a real linear classifier. Read the loss and every prediction before changing the architecture.

[0,0] 0

[0,1] 1

[1,0] 1

[1,1] 0

TENSORFLOW.JS CONTROLLED COMPARISON

2/4
→ 4/4

--linear loss

--hidden loss

DIAGNOSIS COMPLETE

A representation change solved what more waiting could not.

A single line cannot separate diagonal XOR labels. The hidden layer transforms the inputs so the output layer can draw the needed nonlinear decision.

Transfer rule: low training fit plus a stable loss plateau can signal insufficient representation; high training fit with poor validation is a different diagnosis.

The intervention comparison holds data, labels, seeds, optimizer family, and decision threshold fixed. Only architecture and scheduled epochs differ.

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Is training stuck, or is the model too simple?

2/4→ 4/4

A representation change solved what more waiting could not.

2/4
→ 4/4