# 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:**

![Baseline CNN loss curve](images/cnn-baseline-loss.png)

**CNN + BatchNorm:**

![BatchNorm CNN loss curve](images/cnn-bn-loss.png)

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.

## Notebooks

- [`conv-pred.ipynb`](../cnn-batchnorm-comparison/conv-pred.ipynb)
- [`conv-pred-BN.ipynb`](../cnn-batchnorm-comparison/conv-pred-BN.ipynb)