You work as a machine learning specialist at a government agency that creates an image recognition program to help detect missing persons by analyzing surveillance videos.
You have built and are now training a deep learning model for your image classification.
You see that it is overfitting the training data during your model training: your training accuracy is 99%, and your testing accuracy is 75%
Why is your model overfitting the training data, and how can you address the issue?
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer: C.
Option A is incorrect.
Increasing the epoch number will cause your model to train longer.
But this alone won't allow your training to reach generalization.
Option B is incorrect.
Increasing the mini-batch size results in models with poor generalization, therefore not allowing your training to reach generalization.
Option C is correct.
Increasing the dropout rate in your deep learning model is proven to address the issue of overfitting.
See the article “Dropout Regularization in Deep Learning Models With Keras” in the reference section.
Option D is incorrect.
Increasing the learning rate alone won't bring your model to generalization.
A learning rate that is too large will result in an unstable network.
See the article “Understand the Impact of Learning Rate on Neural Network Performance” in the reference section.
References:
Please see the Machine Learning Mastery article titled Dropout Regularization in Deep Learning Models With Keras (https://machinelearningmastery.com/dropout-regularization-deep-learning-models-keras/),
The Machine Learning Mastery article titled Understand the Impact of Learning Rate on Neural Network Performance (https://machinelearningmastery.com/understand-the-dynamics-of-learning-rate-on-deep-learning-neural-networks/),
The AWS Glue developer guide titled Populating the AWS Glue Data Catalog (https://docs.aws.amazon.com/glue/latest/dg/populate-data-catalog.html),
The Amazon SageMaker developer guide titled Object2Vec Hyperparameters (particularly the descriptions of dropout and learning_rate) (https://docs.aws.amazon.com/sagemaker/latest/dg/object2vec-hyperparameters.html)
The given scenario indicates that the deep learning model is overfitting the training data. Overfitting occurs when a model is too complex or has too many parameters, causing it to fit the noise in the training data rather than the underlying pattern, which causes poor performance on unseen data.
Option A suggests that the optimization stopped before model training bounced out of a local minimum, which could be a possible reason for overfitting. Local minima refer to a point in the training process where the optimization algorithm cannot improve the model's performance further. If the algorithm stops too early, the model may not be optimized enough to capture the underlying pattern in the data. Increasing the epoch number, which represents the number of times the model sees the entire dataset during training, could address the issue.
Option B suggests that the mini-batch size is too low, causing the model to overfit the training data. Mini-batch size refers to the number of training examples processed in a single iteration during the training process. If the mini-batch size is too small, the model may not see enough data to generalize well, causing overfitting. Increasing the mini-batch size could address the issue.
Option C suggests that the model is not generalized and that increasing the dropout rate at the flatten layer could address the issue. Dropout is a regularization technique that randomly drops out some of the neurons during training, preventing them from being too reliant on any one feature. This regularization technique could help prevent the model from overfitting the training data.
Option D suggests that the optimization is trapped at a local minimum during training, and increasing the learning rate could address the issue. A low learning rate may cause the optimization algorithm to get stuck in a local minimum rather than finding the global minimum, leading to overfitting. Increasing the learning rate could help the model converge to the global minimum and prevent overfitting.
Overall, options A, B, C, and D are all possible solutions to address overfitting, but the best solution may depend on the specific situation and the model architecture. It is best to experiment with different techniques and evaluate the model's performance on the validation or test set to select the most effective solution.