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TruncatedNormal

Description

Initializer that generates a truncated normal distribution. Type : polymorphic.

 

 

The values generated are similar to values from a RandomNormal initializer except that values more than two standard deviations from the mean are discarded and re-drawn.

 

Input parameters

 

 Parameters : cluster,

mean : float, a scalar. Mean of the random values to generate.
stddev : float, a scalar. Standard deviation of the random values to generate.
 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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