6.1.6 Regularization

Section Overview

Regularization is not about making training loss as low as possible. It is about making the model generalize better to validation and future data.

What You Will Build

This lesson runs one PyTorch lab that compares:

• no regularization;
• dropout;
• weight decay;
• dropout plus weight decay;
• early stopping behavior through best_epoch.

Setup

python -m pip install -U torch scikit-learn

Run the Complete Lab

Create regularization_lab.py:

import torch
import torch.nn as nn
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler


def make_data():
    X, y = make_moons(n_samples=500, noise=0.28, random_state=42)
    X = StandardScaler().fit_transform(X)
    X_train, X_val, y_train, y_val = train_test_split(
        X, y, test_size=0.35, random_state=42, stratify=y
    )
    return (
        torch.tensor(X_train, dtype=torch.float32),
        torch.tensor(y_train.reshape(-1, 1), dtype=torch.float32),
        torch.tensor(X_val, dtype=torch.float32),
        torch.tensor(y_val.reshape(-1, 1), dtype=torch.float32),
    )


class MLP(nn.Module):
    def __init__(self, dropout=0.0):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(2, 32), nn.ReLU(), nn.Dropout(dropout),
            nn.Linear(32, 32), nn.ReLU(), nn.Dropout(dropout),
            nn.Linear(32, 1),
        )

    def forward(self, x):
        return self.net(x)


def accuracy(logits, y):
    pred = (torch.sigmoid(logits) >= 0.5).float()
    return (pred == y).float().mean().item()


def train_case(name, dropout=0.0, weight_decay=0.0, epochs=120):
    torch.manual_seed(42)
    X_train, y_train, X_val, y_val = make_data()
    model = MLP(dropout=dropout)
    loss_fn = nn.BCEWithLogitsLoss()
    optimizer = torch.optim.AdamW(model.parameters(), lr=0.01, weight_decay=weight_decay)
    best_val = 10**9
    patience = 0
    best_epoch = 0
    for epoch in range(1, epochs + 1):
        model.train()
        loss = loss_fn(model(X_train), y_train)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        model.eval()
        with torch.no_grad():
            val_loss = loss_fn(model(X_val), y_val).item()
        if val_loss < best_val:
            best_val = val_loss
            best_epoch = epoch
            patience = 0
        else:
            patience += 1
        if patience >= 20:
            break

    model.eval()
    with torch.no_grad():
        train_loss = loss_fn(model(X_train), y_train).item()
        val_loss = loss_fn(model(X_val), y_val).item()
        train_acc = accuracy(model(X_train), y_train)
        val_acc = accuracy(model(X_val), y_val)
    print(
        f"{name:<14} epochs={epoch:<3} best_epoch={best_epoch:<3} "
        f"train_loss={train_loss:.3f} val_loss={val_loss:.3f} "
        f"train_acc={train_acc:.3f} val_acc={val_acc:.3f}"
    )


print("regularization_lab")
train_case("plain", dropout=0.0, weight_decay=0.0)
train_case("dropout", dropout=0.25, weight_decay=0.0)
train_case("weight_decay", dropout=0.0, weight_decay=0.05)
train_case("both", dropout=0.25, weight_decay=0.05)

Run it:

python regularization_lab.py

Expected output:

regularization_lab
plain          epochs=87  best_epoch=67  train_loss=0.141 val_loss=0.155 train_acc=0.945 val_acc=0.931
dropout        epochs=101 best_epoch=81  train_loss=0.158 val_loss=0.162 train_acc=0.945 val_acc=0.943
weight_decay   epochs=87  best_epoch=67  train_loss=0.141 val_loss=0.154 train_acc=0.948 val_acc=0.931
both           epochs=101 best_epoch=81  train_loss=0.159 val_loss=0.162 train_acc=0.942 val_acc=0.949

Read the Result

The plain model has lower training loss:

plain train_loss=0.141 val_acc=0.931

But the combined regularized model has better validation accuracy:

both train_loss=0.159 val_acc=0.949

That is the point of regularization. You may accept a slightly worse training fit to get better generalization.

Dropout

nn.Dropout(0.25) randomly drops activations during training:

nn.Linear(2, 32), nn.ReLU(), nn.Dropout(dropout)

It makes the network less dependent on any single hidden unit. Use it mainly in hidden layers. During model.eval(), dropout is disabled automatically.

Weight Decay

Weight decay is L2-style regularization applied by the optimizer:

torch.optim.AdamW(model.parameters(), lr=0.01, weight_decay=0.05)

It discourages overly large weights. In modern PyTorch work, AdamW is often preferred over older Adam-with-L2 behavior because weight decay is decoupled from the adaptive gradient update.

Early Stopping

The lab tracks:

best_epoch=67

Early stopping means: keep the best validation checkpoint and stop after validation loss fails to improve for a while. It prevents you from training long past the point where validation performance stopped improving.

What to Try First

Problem	First action
training loss low, validation loss high	add weight decay or dropout
validation improves then worsens	early stopping
model underfits both train and validation	reduce regularization or improve model
validation is noisy	lower LR, use more data, average across folds

Practical Debugging Checklist

Symptom	Likely cause	Fix
dropout hurts training badly	dropout too high or model too small	lower dropout
train and validation both poor	underfitting	reduce regularization
validation best epoch much earlier	training too long	save best checkpoint
weight decay has no effect	value too small or model already simple	increase gradually
eval results change randomly	forgot model.eval()	switch eval mode before validation

Practice

1. Change dropout to 0.1, 0.5, and 0.7.
2. Change weight decay to 0.001, 0.01, and 0.1.
3. Print train and validation loss every 20 epochs.
4. Save the best model state when val_loss improves.
5. Remove model.eval() during validation and explain what changes.

Pass Check

You are done when you can explain:

• regularization aims at validation performance, not only training loss;
• dropout randomly disables hidden activations during training;
• weight decay discourages large weights;
• early stopping keeps the best validation point;
• too much regularization can cause underfitting.