You are a machine learning specialist working for a social media company where your team is responsible for building a machine learning model to classify the images that your users submit to your service. You have built a neural network to classify the images. You are now performing mini-batch training of the neural network, and you see that your resulting training accuracy is oscillating. What is a likely reason for this issue?

Answer options:

A.The epochs hyperparameter is set too low.
B.The momentum hyperparameter is set to 0.9.
C.The dropout hyperparameter is set to 0.
D.The learning_rate hyperparameter is set too high.

Answer: D
Option A is incorrect. The epochs hyperparameter controls how many training epochs. A low epoch value will not cause oscillating accuracy results.
Option B is incorrect. The momentum hyperparameter is used to control the speed of the optimization process. It can be used to prevent oscillations, but it would not cause oscillation.
Option C is incorrect. The dropout hyperparameter is used to prevent overfitting. A low value, such as 0, would not cause oscillating accuracy results.
Option D is correct. The learning_rate hyperparameter, when set to a very high value, can cause oscillation of accurate results.
Reference:
Please see the AWSMachine Learning blog titled Amazon SageMaker automatic model tuning produces better models, faster (https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-automatic-model-tuning-produces-better-models-faster/), the AWSMachine Learning blog titled The importance of hyperparameter tuning for scaling deep learning training to multiple GPUs (https://aws.amazon.com/blogs/machine-learning/the-importance-of-hyperparameter-tuning-for-scaling-deep-learning-training-to-multiple-gpus/), the Amazon SageMaker developer guide titled Image Classification Hyperparameters (https://docs.aws.amazon.com/sagemaker/latest/dg/IC-Hyperparameter.html), the Nanonets article titled How To Make Deep Learning Models That Don’t Suck (https://nanonets.com/blog/hyperparameter-optimization/), and the Hackernoon article titled Hyperparameter Tuning Platforms are Becoming a New Market in the Deep Learning Space (https://medium.com/hackernoon/hyperparameter-tuning-platforms-are-becoming-a-new-market-in-the-deep-learning-space-7106f0ac1689)