Track Accuracy While You Train
Turn predictions into a score.
Track Accuracy While You Train is a free Deep Learning Academy lesson on CoddyKit — lesson 3 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.
Loss Alone Lies a Little
A falling loss feels good, but it is abstract. To know if your model is truly useful you also want accuracy, the share of predictions it gets right. 🎯
From Logits to a Choice
Your model outputs raw scores per class. To pick a prediction you take the index of the largest score with argmax along the class dimension.
preds = pred.argmax(dim=1)Compare to the Truth
Now compare each predicted class to the real label. The result is a boolean tensor where True marks every example the model got right.
correct = preds == yCount the Wins
Booleans add up like ones and zeros, so summing the matches gives a count of correct predictions in this batch.
num_correct = correct.sum().item()Turn Count Into a Rate
Divide correct predictions by the batch size and you get accuracy, a number from zero to one that is easy to read as a percentage.
acc = num_correct / y.size(0)Accumulate Across Batches
Per-batch accuracy is noisy, so keep running totals of correct and seen examples. Their ratio gives a stable epoch accuracy.
total_correct += num_correct
total_seen += y.size(0)Report Each Epoch
At the end of an epoch compute the overall rate and print it beside the loss. Now you see both how confident and how correct the model is.
epoch_acc = total_correct / total_seenKeep Metrics off the Graph
When you only measure, wrap scoring in no_grad. It stops PyTorch from tracking operations you never plan to backpropagate, saving memory.
with torch.no_grad():
preds = model(x).argmax(dim=1)Accuracy Is Not Enough Alone
On imbalanced data accuracy can fool you: always guessing the common class scores high yet learns nothing. Pair it with loss and other metrics.
Watch the Trend
One epoch's number means little; the trend is everything. Rising accuracy with falling loss is the clear sign your training is on track.
A Window Into Learning
Tracking accuracy turns training from a black box into something you can watch. Each epoch you get honest feedback on real progress.
Quick Check
How do you turn class scores into a single predicted label?
Recap
You learned to track accuracy: argmax the scores, compare to labels, count correct, and divide. Watch the trend alongside loss. 🎉
Frequently asked questions
Is the “Track Accuracy While You Train” lesson free?
Yes — the full text of “Track Accuracy While You Train” 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 “Track Accuracy While You Train”?
Turn predictions into a score. 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 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Track Accuracy While You Train” 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
- Forward Pass, Loss, Backward, Step
- Write a Minimal Training Loop
- Track Accuracy While You Train
- Train vs Eval Mode