Question 10
Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: [`˜drivers_license', `˜passport', `˜credit_card']. Which loss function should you use?
- A. Categorical hinge
- B. Binary cross-entropy
- C. Categorical cross-entropy
- D. Sparse categorical cross-entropy
References
- https://machinelearningmastery.com/how-to-choose-loss-functions-when-training-deep-learning-neural-networks/
- https://stackoverflow.com/questions/58565394/what-is-the-difference-between-sparse-categorical-crossentropy-and-categorical-c
- https://datascience.stackexchange.com/questions/41921/sparse-categorical-crossentropy-vs-categorical-crossentropy-keras-accuracy
- https://stats.stackexchange.com/questions/326065/cross-entropy-vs-sparse-cross-entropy-when-to-use-one-over-the-other