Binary Cross Entropy relevancy in Neural Network
1. What is Cross Entropy Loss?
It’s a loss function used mainly for classification problems, especially when our neural network is trying to predict categories (like “dog” or “cat”).
Imagine This Situation:
Let’s say our neural network is trying to predict whether an image is of a cat (1) or not a cat (0).
- The actual answer (true label) is: cat → 1
- The prediction from your model is: 0.9 (which means 90% sure it’s a cat)
This is pretty good, right? The prediction is close to the truth.We want the loss to be small in this case.
But what if our model predicted 0.1? It thinks there’s only a 10% chance it’s a cat – that’s a bad guess, so the loss should be large.
Mathematically (for binary classification):
CrossEntropyLoss=−[y⋅log(p)+(1−y)⋅log(1−p)]
Where:
- y = actual label (0 or 1)
- p = predicted probability from the model
Examples:
| Actual (y) | Predicted (p) | Loss | Comment |
|---|---|---|---|
| 1 | 0.9 | Small loss | Good prediction |
| 1 | 0.1 | Big loss | Bad prediction |
| 0 | 0.1 | Small loss | Good prediction |
| 0 | 0.9 | Big loss | Bad prediction |
2. Why Is It So Popular?
- It penalizes wrong confident predictions very strongly.
- It encourages the model to become more confident in the right direction.
3. Where Is It Used?
- Binary classification (e.g., cat vs not-cat)
- Multi-class classification (e.g., cat, dog, horse) — with a slightly extended version called Categorical Cross Entropy
Story Format (Bonus)
Imagine we’re taking a test where we have to say how confident we are in each answer. If we’re super confident but wrong, we get punished heavily. But if we’re right and confident — we’re rewarded. Cross Entropy works like that.
Binary Cross Entropy relevancy in Neural Network – Binary Cross Entropy with Simple Python