Hyperparameters: Deciding How to Train AI
When preparing for an exam, creating a study plan that includes determining study time, rest periods, and study methods greatly affects your grades. Similarly, when training an AI model, you set up hypеrparаmetеrs to determine how the model learns.
Hyperparameters are parameters you configure before training an AI model. They act as input values that influence the system's behavior or outcomes, and must be set in advance before training begins.
A model that is well-trained with suitable hyperparameters, meaning it is well-optimized and performs well on various tasks, is referred to as having reached convergence.
In contrast, a model that fails to train well and performs poorly is referred to as divergence.
Just like cramming too much studying into a short period can confuse students and lead to poor exam performance, an optimized learning strategy is crucial when training AI.
The key hyperparameters in an AI training strategy include:
Learning Rate
The learning rate determines how much the model's weights change with each iteration. If it's set too high, the model might overshoot optimal solutions or become unstable, but if it's set too low, training can be very slow.
- Analogy: The speed at which a student learns new information.
- If a student learns too quickly (high learning rate), they might not understand the material well, and if they learn too slowly (low learning rate), their study might be inefficient and slow.
Batch Size
Batch size refers to the quantity of data the model processes at once. A large batch size can speed up training but requires more computational resources, while a small batch size uses fewer resources but may result in longer training times.
- Analogy: The amount of material a student studies at one time.
- Studying a lot at once (large batch size) can reduce total study time but may lead to burnout, whereas studying in smaller portions (small batch size) allows for frequent breaks but takes longer overall.
Number of Epochs
Epochs define how many times the entire dataset is used for training. Too few epochs might result in underfitting, while too many can lead to overfitting.
- Analogy: The number of times a student reviews their entire textbook.
- Reviewing the textbook multiple times (many epochs) can deepen understanding but might lead to becoming overly reliant on it. Reviewing too few times (few epochs) might result in insufficient understanding.
Learning Rate Decay
Learning rate decay involves gradually reducing the learning rate as training progresses. This allows the model to learn quickly with larger changes initially, then fine-tune with smaller adjustments later for optimization.
A larger decay reduces the learning rate quickly, while a smaller decay reduces it gradually.
- Analogy: Adjusting the speed of studying over time.
- A fast decay (large decay) means the student decreases their study speed rapidly, allowing detail focus later on. A slow decay (small decay) means the student maintains a consistent study pace throughout.
Dropout Rate
Dropout involves randomly deactivating a proportion of neurons during neural network training to improve generalization capability and prevent overfitting. The dropout rate determines the proportion of neurons to deactivate.
- Analogy: Frequency of taking breaks during study.
- Taking frequent breaks (high dropout rate) helps maintain focus but extends study time, whereas seldom breaks (low dropout rate) might lead to decreased focus.
Hyperparameters greatly influence the training process and performance of a model, requiring careful consideration and adjustment, much like a person's study habits.
In the next lesson, we'll explore which hyperparameters require particular attention when setting them.
If anything is unclear, feel free to ask the hyperparameter expert available on the practice screen!
Want to learn more?
Join CodeFriends Plus membership or enroll in a course to start your journey.