Learning Curves
A learning curve shows how a metric changes during training.
Curves help answer questions a final number hides:
- did training ever improve?
- did validation improve with training?
- did loss explode?
- did progress stop early?
- did validation worsen while training improved?
The shape matters. A single final metric cannot tell whether the run was healthy, unstable, or lucky.
Learning curves are especially useful when comparing changes. If a new learning rate reaches lower validation loss faster, the curve shows that. If a new model has the same final score but unstable training, the curve shows that too.
Validation loss stays near 1.0 for many epochs while training loss also stays near 1.0. Enter 1 for likely progress or 2 for likely stuck.
Compute it first, then check your number.
Enter 1 if only the final metric matters, or 2 if the path of training can reveal instability.
Compute it first, then check your number.