Package ai.djl.nn.norm

Contains classes that define normalizing neural network operations.

Class Summary

Class	Description
BatchNorm	In batch training (training with more than one samples per iteration), a batch normalization layer works by normalizing the values of input data to have mean of 0 and variance of 1.
BatchNorm.Builder	The Builder to construct a BatchNorm.
Dropout	A dropout layer benefits a network by allowing some units (neurons), and hence their respective connections, of a network to be randomly and temporarily removed by setting its value to 0 only during training by specified probability \(p\), usually set to 0.5.
Dropout.Builder	The Builder to construct a Dropout type of Block.
LayerNorm	Layer normalization works by normalizing the values of input data for each input sample to have mean of 0 and variance of 1.
LayerNorm.Builder	The Builder to construct a LayerNorm.