One-Hot in Neural Network
1. STORY: “The Language of Robots in the Fruit Shop”
Imagine we have a robot working in a fruit shop.Each morning, the shop owner tells the robot what fruit to arrange — say, Apple, Banana, or Cherry.
But the robot doesn’t understand text like humans. It needs numbers — not words — to process information.
So, if the owner says: “Today, arrange Banana.”
We can’t feed “Banana” as-is into the robot’s neural network brain. Why?
Because computers don’t understand text directly. Also, using labels like:
Apple = 0, Banana = 1, Cherry = 2 can confuse the robot into thinking:
Cherry (2) is more than Apple (0)
But that’s not true — these are categories, not numbers to be ranked.So the fruit manager comes up with a new language:
Use a One-Hot Encoding system!
Here’s the new fruit code:
- Apple → [1, 0, 0]
- Banana → [0, 1, 0]
- Cherry → [0, 0, 1]
Each fruit has its own unique identity — no ordering, no confusion!
So now when the owner says “Banana,” the robot gets: [0, 1, 0]
and can act accordingly.
2. WHY ONE-HOT ENCODING IN NEURAL NETWORKS?
Neural Networks use vectors of numbers as input. One-Hot Encoding ensures:
- Categorical variables are converted to numerical form.
- Each category gets a distinct, equally distant vector.
- It removes ordinal assumptions (no false hierarchy).
One-Hot in Neural Network – One-Hot example with simple python