What is Training Instability?
Let's take a look at Training Instability.
Training instability refers to a state where the performance of an AI model changes unpredictably during training. This means that the loss function values or evaluation metrics fluctuate wildly, indicating that the model is not learning effectively.
In simpler terms, the model does not consistently improve along a stable direction but rather shows erratic performance changes.
Causes of Training Instability
1. Learning Rate is Too High
If the learning rate is too high, the model may fail to find the optimal weights, and the loss function values may fluctuate significantly.
2. Unstable Initialization
If the weights of the model are initialized in an unstable manner, the performance during training may change unpredictably.
3. Excessive Complexity
When a model is too complex, instability can arise during the training process. With too many parameters to learn, the model might not train in a consistent direction.
4. Small Batch Size
If the batch size is too small, the loss function values can vary significantly during training. This leads to instability since the model is not exposed to enough data.
Solutions to Training Instability
1. Adjust the Learning Rate
Adjust the learning rate to ensure the model learns stably. Set it not too high to allow the model to slowly yet stably find the optimal weights.
2. Improve Weight Initialization Techniques
Use stable methods to initialize the model's weights to prevent the start of training from being unstable. For instance, He initialization or Xavier initialization can be used.
3. Simplify the Model
Design the model with an appropriate size and structure to avoid excessive complexity. Reducing the complexity can help alleviate training instability.
4. Adjust the Batch Size
Set an appropriate batch size to ensure stable training. Avoid too small a batch size and use a suitable size to reduce variability in the training process.
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