Package ai.djl.nn.norm

package ai.djl.nn.norm

Contains classes that define normalizing neural network operations.

Related Packages

Package	Description
ai.djl.nn	Contains classes to construct neural networks.
ai.djl.nn.convolutional	Contains classes that define convolutional operations extending Convolution and Deconvolution.
ai.djl.nn.core	Contains classes that define simple neural network operations.
ai.djl.nn.pooling	Contains pooling neural network operations in Pool and helpers for it.
ai.djl.nn.recurrent	Contains classes for recurrent neural network operations.
ai.djl.nn.transformer	Contains blocks for transformer models.

Classes

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.BaseBuilder<T extends BatchNorm.BaseBuilder<T>>	The Builder to construct a BatchNorm type of Block.
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.
GhostBatchNorm	GhostBatchNorm is similar to BatchNorm except that it splits a batch into a smaller sub-batches aka ghost batches, and normalize them individually to have a mean of 0 and variance of 1 and finally concatenate them again to a single batch.
GhostBatchNorm.Builder	The Builder to construct a GhostBatchNorm.
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.