Gradient Descent Lab Memorization vs Generalization

Training Loss (Empirical)
Test Loss (Generalization)
Optimizer Trajectory (Adam/SGD)
Train Loss
0.000
Empirical empirical risk
Test Loss
0.000
Out-of-distribution loss
Generalization Gap
0.000
Test - Train gap
Gradient Norm ||g||
0.000
Local steepness
Weight Norm ||w||
0.000
Parameter magnitude
Optimization Status: Click canvas or hit Play to start optimization trajectory.
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