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HeNormal

Description

He normal initializer. Type : polymorphic.

 

 

It draws samples from a truncated normal distribution centered on 0 with stddev = sqrt(2 / fan_in) where fan_in is the number of input units in the weight tensor.

 

Input parameters

 

 seed : integer, used to make the behavior of the initializer deterministic. Note that an initializer seeded with an integer or -1 (unseeded) will produce the same random values across multiple calls.

 

 

Output parameters

 

Initializer : cluster, this cluster defines the weight initialization strategy for a model.

enum : enum, an enumeration indicating the initialization type (e.g., Zeros, Glorot, HeNormal, etc.). If enum is set to CustomInitializer, the custom class on the right will be used. Otherwise, the selected initialization strategy will be applied with default parameters.
 Class : object, a custom initializer 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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