ML0009 Batch Size Selection

Selecting an appropriate batch size is another crucial hyperparameter choice in neural network training, impacting both performance and training efficiency. Select a batch size that balances your hardware constraints and the optimization trade-offs. Start with a moderate value (e.g., 16, 32, or 64) and adjust it based on the available memory, the stability of your gradient updates, and the model’s validation performance.

(1) Memory Constraints: Larger batch sizes require more GPU (or CPU) memory.
(2) Dataset Size: Larger datasets can generally accommodate larger batch sizes. Smaller datasets may benefit from smaller batch sizes to introduce more variability in the training process.
(3) Learning Rate Interaction: The appropriate learning rate often depends on the batch size; large batches might allow or even require a higher learning rate, while small batches might need a lower one. Some practitioners adjust the learning rate proportionally to the batch size.