Web Reference: Jul 23, 2025 · Batch Normalization can affect the training dynamics, so it's crucial to assess its impact on convergence and adjust hyperparameters accordingly. By following these best practices, practitioners can effectively leverage Batch Normalization in Keras to develop robust and efficient deep learning models. Batch normalization applies a transformation that maintains the mean output close to 0 and the output standard deviation close to 1. Importantly, batch normalization works differently during training and during inference. It is natural to wonder whether we should apply batch normalization to the input X, or to the transformed value XW+b. Ioffe and Szegedy (2015) recommend the latter.
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