Applying Batch Normalization in Keras using BatchNormalization Class

The keras.layers.BatchNormalization class in Keras implements Batch Normalization, a technique used to normalize the activations of a layer in a neural network.

Syntax of BatchNormalization Class in Keras

keras.layers.BatchNormalization(
                         axis=-1, 
                         momentum=0.99, 
                         epsilon=0.001, 
                         center=True, 
                         scale=True, 
                         beta_initializer="zeros", 
                         gamma_initializer="ones", 
                         moving_mean_initializer="zeros", 
                         moving_variance_initializer="ones", 
                         beta_regularizer=None, 
                         gamma_regularizer=None, 
                         beta_constraint=None, 
                         gamma_constraint=None, 
                         synchronized=False,
                         **kwargs)

BatchNormalization Class Parameters

Here’s a breakdown of its parameters:

• axis: Specifies the axis along which normalization is applied. By default, it normalizes along the last axis (usually the features axis).
• momentum: A float value between 0 and 1 that represents the exponential decay rate for the moving mean and moving variance estimates. A higher momentum value means the statistics from previous batches have more influence.
• epsilon: A small float value added to the variance to prevent division by zero.
• center: If True, the layer will learn an offset parameter (beta). If False, this parameter is disabled.
• scale: If True, the layer will learn a scale parameter (gamma). If False, this parameter is disabled.
• beta_initializer: Initializer for the beta (offset) parameter.
• gamma_initializer: Initializer for the gamma (scale) parameter.
• moving_mean_initializer: Initializer for the moving mean parameter.
• moving_variance_initializer: Initializer for the moving variance parameter.
• beta_regularizer: Regularizer function applied to the beta parameter.
• gamma_regularizer: Regularizer function applied to the gamma parameter.
• beta_constraint: Constraint function applied to the beta parameter.
• gamma_constraint: Constraint function applied to the gamma parameter.
• synchronized: A boolean indicating whether Batch Normalization should be synchronized across replicas during distributed training. This is useful for distributed training setups.
• kwargs: Additional keyword arguments accepted by the base Layer class.

These parameters allow for fine-tuning and customization of the Batch Normalization layer according to specific requirements and architectural considerations. For example, you can control whether to include learnable parameters (beta and gamma), specify the initialization and regularization methods, and adjust the axis of normalization.

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.