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lslnf

Description

Map infinity to true and other values to false.

 

Input parameters

 

specified_outputs_name : array, this parameter lets you manually assign custom names to the output tensors of a node.
X (heterogeneous) – T1 : object, input tensor.

 Parameters : cluster,

detect_negative : boolean, whether map negative infinity to true. Default to true so that negative infinity induces true. Set this attribute to false if negative infinity should be mapped to false.
Default value “True”.
detect_positive : boolean, whether map positive infinity to true. Default to true so that positive infinity induces true. Set this attribute to false if positive infinity should be mapped to false.
Default value “True”.
 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.

Output parameters

 

 Y (heterogeneous) – T2 : object, output tensor.

Type Constraints

T1 in (tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), 
tensor(float8e5m2), tensor(float8e5m2fnuz)) : Constrain input types to float tensors.

T2 in (tensor(bool)) : Constrain output types to boolean 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).
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