LpNormalization

LpNormalization

Description

Given a matrix, apply Lp-normalization along the provided axis.

 

Documentation illustration (illustration unavailable in the archive).

Input parameters

 

specified_outputs_name : array, this parameter lets you manually assign custom names to the output tensors of a node.
 input (heterogeneous) – T : object, input matrix.
 Parameters : cluster,

axis : integer, the axis on which to apply normalization, -1 mean last axis.
Default value “0”.
p : integer, the order of the normalization, only 1 or 2 are supported.
Default value “1”.
 training? : boolean, whether the layer is in training mode (can store data for backward).
Default value “True”.
 lda coeff : float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Default value “1”.

 name (optional) : string, name of the node.
Documentation illustration (illustration unavailable in the archive).

Output parameters

 

 output (heterogeneous) – T : object, matrix after normalization.

Type Constraints

T in (tensor(double), tensor(float), tensor(float16)) : Constrain input and output types to float tensors.

Example

All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Deep Learning library to run it).