Application of Deep Learning Skills 12-Application of Batch Normalization in Neural Network Training

Hello everyone, I am Weixue AI. Today I will introduce to you the application of deep learning skills 12-the application of batch normalization in neural network training. In deep learning, batch normalization (BN for short) is a kind of Important trick, which is widely used in many neural networks. This article will introduce the principle and application of batch normalization in detail, and build a simple neural network in combination with the PyTorch framework to demonstrate the actual effect of batch normalization.

1. The principle of batch normalization

Traditional neural networks may face the problem of gradient disappearance or gradient explosion during training, which often requires proper initialization of weights and adjustment of learning rate. Batch normalization proposes a more general solution, which speeds up the convergence process of the network by normalizing the input of each layer in the network so that it has a uniform standard deviation and mean.

The specific operation of the batch normalization method is: in the calculation process of each layer, first calculate the mean and standard deviation of each sample in the -mini-batch , and then use these two parameters to normalize each sample: