Early Stopping on Val Loss
Stop before you overfit.
Early Stopping on Val Loss is a free Deep Learning Academy lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Deep Learning Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Training Too Long Hurts
More epochs are not always better. Past a point the model starts memorizing noise, and validation results get worse. Early stopping ends training at the sweet spot.
Watch Validation Loss
The signal you track is validation loss. While it keeps falling the model is still generalizing better, so you let training continue.
The Turning Point
Eventually training loss keeps dropping but validation loss bottoms out and rises. That upward turn is the moment overfitting takes over.
Remember the Best Score
Keep a variable for the best validation loss seen so far. Each epoch you compare the new score against it to decide if progress was made.
best_loss = float('inf')Count Epochs Without Gain
When an epoch fails to beat the best, increase a counter. A reset to zero happens the moment validation loss improves again.
if val_loss < best_loss:
best_loss = val_loss
counter = 0
else:
counter += 1Set a Patience
Patience is how many bad epochs you tolerate before quitting. A small value stops fast; a larger one rides out noisy dips.
patience = 5Break When Patience Runs Out
Once the counter reaches patience, stop the loop. This break ends training before the model wastes time getting worse.
if counter >= patience:
breakSave the Best Along the Way
Each time validation improves, write a checkpoint. When you finally stop, that saved file holds the strongest model, not the last one.
if val_loss < best_loss:
torch.save(model.state_dict(), 'best.pt')Add a Min Delta
Tiny wobbles can look like fake progress. A min_delta requires improvement bigger than a threshold before resetting the patience counter.
if val_loss < best_loss - min_delta:
best_loss = val_lossRestore the Best at the End
After the loop, load your saved checkpoint so you actually deploy the best model. Stopping early is pointless if you keep the worse final weights.
model.load_state_dict(torch.load('best.pt'))A Free Form of Regularization
Early stopping costs nothing extra yet fights overfitting like a regularizer. It pairs nicely with dropout and weight decay for even steadier training.
Quick Check
What does the patience setting control in early stopping?
Recap
Track validation loss, count epochs without gain, stop once patience runs out, and restore the best checkpoint. Early stopping saves time and curbs overfitting. ⏹️
Frequently asked questions
Is the “Early Stopping on Val Loss” lesson free?
Yes — the full text of “Early Stopping on Val Loss” is free to read here on the web, and the Deep Learning Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Deep Learning Academy course, upgrade to CoddyKit PRO.
What will I learn in “Early Stopping on Val Loss”?
Stop before you overfit. You practise Deep Learning Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Deep Learning Academy?
No prior experience is required. Deep Learning Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Early Stopping on Val Loss” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Deep Learning Academy lesson?
Yes. Every Deep Learning Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Split Train, Validation & Test
- An Epoch Loop with Validation
- Save & Load with state_dict
- Early Stopping on Val Loss