Learn the proper one-hot-coding representation for rock, paper, scissors labels when training an AI model. Prepare for the Google AI for Anyone certification exam with this detailed explanation.
Table of Contents
Question
You are training a model to classify hand poses as rock, paper, and scissors. Which of the following is a valid one-hot-coding representation of the labels you need to provide?
A. Rock – [1]; Paper – [2]; Scissors – [3]
B. Rock – [0, 0, 1]; Paper – [0, 1, 1]; Scissors – [1. 1. 1]
C. Rock – [1, 0, 0]; Paper – [0, 1, 0]; Scissors – [0, 0, 1
D. Rock – [1, 0, 0]; Paper – [1, 1, 0]; Scissors – [1, 1, 1]
Answer
C. Rock – [1, 0, 0]; Paper – [0, 1, 0]; Scissors – [0, 0, 1
Explanation
When training a model to classify hand poses as rock, paper, and scissors, it’s important to use the correct one-hot-coding representation for the labels. One-hot-coding is a technique where each label is represented by a binary vector, with a single “1” indicating the presence of that label and “0s” for all other positions.
The correct one-hot-coding representation for the given labels is:
C. Rock – [1, 0, 0]; Paper – [0, 1, 0]; Scissors – [0, 0, 1]
Here’s why:
- Each label is represented by a unique binary vector.
- The vector has a “1” in the position corresponding to the label and “0s” elsewhere.
- The vectors are mutually exclusive, meaning only one label can be “1” at a time.
Option A is incorrect because it uses single integer values instead of binary vectors. Options B and D are incorrect because they do not use mutually exclusive vectors, with multiple “1s” present in some vectors.
Using the correct one-hot-coding ensures that the model can effectively learn and distinguish between the different hand poses during training and inference.
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