1.1 KiB
1.1 KiB
CNN BatchNorm Comparison — MNIST
Setup
A baseline CNN (Conv2D(32) → Conv2D(64) → Flatten → Dense(10, softmax)) compared against the identical architecture with BatchNormalization inserted after each conv layer. Both models use the same optimizer and learning rate (SGD, lr=0.001) — BatchNorm is the only variable that changed, making this a clean ablation.
Training curves
Baseline CNN:
CNN + BatchNorm:
The BatchNorm version's curve drops faster and settles lower — visible directly in the training dynamics, not just the final number.
Results
| Model | Test loss |
|---|---|
| Baseline CNN | 0.289 |
| CNN + BatchNorm | 0.094 |
Since the optimizer and learning rate were held constant, this ~3x reduction in test loss is attributable to BatchNormalization itself — consistent with its known role in stabilizing gradients and accelerating convergence.