Why does increasing batch size sometimes degrade Keras model accuracy?

Large batch sizes reduce gradient noise, which can harm generalization.

Smaller batches introduce stochasticity that helps escape sharp minima, improving generalization. Large batches converge to sharper minima, often reducing test performance despite faster training.