Best Practices for using BatchNormalization Class in Keras

When using Batch Normalization in Keras, several best practices can help ensure optimal performance and stability:

1. Consistent Application: It is recommended using Batch Normalization effect across all the layers of the neural network following an element-wise activation function at each layer. It ensures that network does not leverage scaled activations and adapts to modified inputs during training and evaluation (inference).
2. Initialization: The starting value of Batch Normalization parameters (B. e. a gamma and beta) will affect training process and fast convergence. Firstly, we would basically need to set these parameters with more appropriate values, for example gamma could be set to ‘1’ and beta to ‘0’. Such adaptations would improve training as well as speed up the convergence.
3. Monitoring Convergence: During training, it’s essential to monitor convergence metrics such as training loss and validation accuracy. 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.

Training deep neural networks presents difficulties such as vanishing gradients and slow convergence. In 2015, Sergey Ioffe and Christian Szegedy introduced Batch Normalization as a powerful technique to tackle these challenges. This article will explore Batch Normalization and how it can be utilized in Keras, a well-known deep-learning framework.