# MLP Regularization — MNIST

Comparing a baseline multilayer perceptron against a Dropout-regularized version on MNIST digit classification.

## Architecture

Both models share the same base architecture:

```
Dense(500, relu) → Dense(100, relu) → Dense(10, softmax)
```

The dropout version adds `Dropout(0.2)` after each hidden layer.

## ⚠️ Honest note on this comparison

This is **not a clean ablation** — two things changed between the baseline and dropout runs, not one:

| | Baseline | Dropout version |
|---|---|---|
| Dropout | None | 0.2 after each hidden layer |
| Optimizer | SGD, lr=0.001 | SGD, lr=0.01 |

The learning rate is 10x higher in the dropout run. That means the test loss improvement below **can't be attributed to Dropout alone** — the higher learning rate is a confound. Both notebooks are included as-is because they reflect real experimentation, but the results should be read as "these two configurations produced these two outcomes," not as proof that Dropout alone caused the improvement. A clean ablation would hold the learning rate fixed and vary only Dropout.

## Results

| Model | Test loss (categorical crossentropy) |
|---|---|
| Baseline (no dropout, lr=0.001) | 0.279 |
| Dropout (0.2, lr=0.01) | 0.099 |

## Tech stack

TensorFlow / Keras, NumPy, matplotlib

## Notebooks

- [`mnist-mlp-pred.ipynb`](mnist-mlp-pred.ipynb) — baseline
- [`mnist-mlp-pred-dropout.ipynb`](mnist-mlp-pred-dropout.ipynb) — with Dropout