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L1

Description

Define L2 regularizer. L2 regularization applies a penalty proportional to the square of the weights. It discourages large weight values and helps improve generalization by smoothing the model. When selected explicitly, the l2 coefficient is user-defined, while l1 is ignored. Type : polymorphic.

 

 

Input parameters

 

 l2 : float, L2 regularization factor.

 

Output parameters

 

Regularizer : cluster, this cluster defines the regularization strategy used to constrain model weights.

enum : enum, an enumeration indicating the regularizer type (e.g., None, L1, L2, etc.). If enum is set to CustomRegularizer, the custom class will be used. Otherwise, the selected regularizer will be applied using default settings.
 Class : object, a custom regularizer class instance.

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).
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