NormalizationLayer

NormalizationLayer[] represents a trainable net layer that normalizes its input data across the second and subsequent dimensions and applies an independent scaling and bias to each component of the first dimension.

• NormalizationLayer[aggregationlevels] normalizes data across the specified aggregation levels and applies a learned scaling and bias on the remaining levels.
• NormalizationLayer[aggregationlevels, scalinglevels] applies a learned scaling and bias at the specified scaling levels.

Examples

Basic normalization:

NormalizationLayer[]

Batch normalization:

NormalizationLayer[{2, 3}]

Layer normalization:

NormalizationLayer[{1}]

Please visit the official Wolfram Language Reference for more details.