Batch Size
Batch Size is a model training hyperparameter defining the number of training examples processed in a single forward and backward pass before the model's internal parameter weights are updated.
Frequently Asked Questions
What is the tradeoff of using a large batch size?▼
Large batch sizes utilize GPU parallel processing efficiently, accelerating epoch training speeds. However, they consume more VRAM and can lead to poorer generalization performance compared to smaller batches.
What is the difference between batch and epoch?▼
A batch size is the subset of data processed before weights are updated. An epoch is completed when the model has processed the entire dataset exactly once.
Quick Facts
- CategoryModel Training
- Key ApplicationGradient updates, training optimization, and GPU memory allocation.
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