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.
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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