Code Architecture — Dropout (MLP with/without Dropout on Fashion-MNIST)

Notebook: solution.ipynb Task: Build a small MLP in PyTorch, train it with and without Dropout on Fashion-MNIST, and plot train vs test loss curves side by side to show the overfitting gap closing.

Section-by-Section Breakdown

1. Setup & Imports

  • Import PyTorch, torchvision, NumPy, and Matplotlib.
  • Set a random seed for reproducibility.
  • Detect GPU (cuda) if available; Kaggle kernels provide a GPU runtime for this notebook.

2. Dataset Loading (Fashion-MNIST)

  • Use torchvision.datasets.FashionMNIST with torchvision.transforms.ToTensor().
  • Create train/test DataLoaders with moderate batch size (e.g., 128).
  • Data flow: images [B, 1, 28, 28] → flatten to [B, 784] → input to MLP.

3. Model Definition

  • Define a small MLP class with 2 hidden layers (e.g., 784 → 256 → 128 → 10).
  • Use ReLU activations.
  • Add an optional dropout_p argument that controls dropout probability.
  • Apply nn.Dropout(p) after each ReLU in the trainable version.
  • Key function: class MLP(nn.Module) with forward path x → fc1 → relu → dropout → fc2 → relu → dropout → fc3.

4. Train/Eval Helpers

  • train_epoch(model, loader, optimizer, criterion, device) — one training epoch; sets model.train() so dropout is active.
  • evaluate(model, loader, criterion, device) — computes average loss and accuracy; sets model.eval() so dropout is disabled.
  • Data flow per batch:
    • Input: [B, 784] → model → logits [B, 10]
    • Loss: CrossEntropyLoss(logits, labels) scalar
    • Backward pass → optimizer step

5. Training Two Models

  • Create two identical MLPs: model_no_dropout with p=0.0 and model_dropout with p=0.5.
  • Train both for the same number of epochs (e.g., 20–30) using the same optimizer and learning rate.
  • Record training loss and test loss per epoch for both models.

6. Side-by-Side Loss Plot

  • Plot train loss and test loss curves for both models on the same axes.
  • Include a legend: No dropout train, No dropout test, Dropout train, Dropout test.
  • The no-dropout model should show a large train/test gap (overfitting), while the dropout model should show a smaller gap.

7. Accuracy Comparison

  • Print final train/test accuracy for both models.
  • Optionally show a small confusion matrix or per-class accuracy for the dropout model.

8. Dropout Mask Visualization (Optional)

  • For a single forward pass in train mode, capture and visualize which neurons were dropped.
  • This is purely illustrative; it is not required by the code template but helps explain the method.

Key Functions/Classes

Function/Class Responsibility
get_fashion_mnist_loaders(batch_size) Download and prepare train/test DataLoaders
MLP(input_dim, hidden_dims, num_classes, dropout_p) Defines the MLP with optional dropout layers
train_epoch(model, loader, optimizer, criterion, device) Runs one training epoch, returns avg loss
evaluate(model, loader, criterion, device) Runs inference, returns avg loss and accuracy
plot_losses(history) Plots train/test curves for both models

Deliberate Simplifications vs. the Full Paper

Simplification Original Paper Notebook Why
Dataset MNIST, CIFAR-10, ImageNet, speech Fashion-MNIST (10 classes, 28×28) Small, quick, and well-known toy dataset
Model Large conv nets + fully connected Small 2-hidden-layer MLP Demonstrates dropout concept without CNN complexity
Dropout rate Input 0.8, hidden 0.5 tested Hidden 0.5, input 0.0 Input dropout less critical for grayscale images; hidden 0.5 is standard
Epochs Trained to convergence 20–30 epochs Enough to show overfitting gap; keeps runtime reasonable
Weight scaling Exact averaging over 2^H networks Multiply weights by p at test time Standard PyTorch nn.Dropout already handles this automatically
Tasks Vision, speech, bioinformatics Single classification task Keeps notebook self-contained and focused
Evaluation metric Benchmark records Train/test accuracy + loss curves Sufficient to demonstrate regularization effect