What is the purpose of BatchNormalization in Keras?

BatchNormalization is a commonly used regularization technique aimed at speeding up the training process of deep neural networks and enhancing the model’s generalization ability. Its purpose is to normalize the input data of each minibatch, making the mean of each feature close to 0 and the variance close to 1, thereby improving the stability and convergence speed of the model.

The main purpose of Batch Normalization includes:

1. Speed up training: BatchNormalization can reduce internal covariate shift in deep neural networks, stabilizing the input distribution of each layer and hence speeding up the training process of the model.
2. Improve generalization ability: BatchNormalization can reduce the risk of overfitting on the training set, thus improving the model’s generalization ability on the test set.
3. Preventing gradient vanishing or exploding: BatchNormalization can alleviate the issue of gradient vanishing or exploding in deep neural networks, making it easier to optimize the model.
4. Allow for the use of higher learning rates: BatchNormalization makes the model more stable, thereby enabling the use of larger learning rates to speed up the convergence of the model.
5. Reduce reliance on other regularization techniques: BatchNormalization itself has a regularizing effect, which can help decrease the need for other regularization techniques such as Dropout.