Reason for Oscillating Training Accuracy
Question
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?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.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)
When training a neural network, it is common to use mini-batch training where a small subset of the training data is used at each iteration to update the model's parameters.
If the training accuracy is oscillating, it means that the model's performance is not improving steadily but fluctuating between good and bad performance. This can be caused by several factors, including the following:
A. The epochs hyperparameter is set too low: The number of epochs determines how many times the model will see the entire training dataset during training. If the epochs hyperparameter is set too low, the model may not have had enough exposure to the training data to learn the underlying patterns and may be overfitting to the training data.
B. The momentum hyperparameter is set to 0.9: The momentum hyperparameter is used to accelerate the gradient descent algorithm by adding a fraction of the previous update to the current update. A high momentum value like 0.9 can cause the model to overshoot the minimum and lead to oscillations.
C. The dropout hyperparameter is set to 0: Dropout is a regularization technique that randomly drops out some neurons during training to prevent overfitting. A dropout value of 0 means that no neurons are being dropped out, which can cause the model to overfit and oscillate between good and bad performance.
D. The learning_rate hyperparameter is set too high: The learning rate determines how much the model's parameters are updated during each iteration. A high learning rate can cause the model to overshoot the minimum and lead to oscillations.
Therefore, a likely reason for the issue of oscillating training accuracy in this case is either A or C, where the model is not able to learn the underlying patterns of the data and is overfitting to the training data. However, without more information about the specific model and data, it is difficult to determine the exact cause.
