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L1

Description

Define L1 regularizer. L1 regularization applies a penalty proportional to the absolute value of the weights. This encourages sparse models by driving some weights to zero, which can be useful for feature selection or reducing model complexity. When selected explicitly, the l1 coefficient is user-defined, while l2 is ignored. Type : polymorphic.

 

 

Input parameters

 

Β l1 : float, L1 regularization factor.

 

Output parameters

 

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

enum :Β 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.
Β Class :Β 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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