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MicrosoftRange

Description

Creates a sequence of numbers that begins at start and extends by increments of delta up to but not including limit.

 

 

Input parameters

 

specified_outputs_name : array, this parameter lets you manually assign custom names to the output tensors of a node.

 Graphs in : cluster, ONNX model architecture.

start (heterogeneous) – T : object, tensor(scalar, or dims=[1]). First entry in the range.
limit (heterogeneous) – T : object, tensor(scalar, or dims=[1]). Upper limit of sequence, exclusive.
delta (optional, heterogeneous) – T : object, tensor(scalar, or dims=[1]). Number that increments start.

 Parameters : cluster,

 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

 

output (heterogeneous) – T : object, 1-D Tensor of the range.

Type Constraints

T in (tensor(float), tensor(double), tensor(int16), tensor(int32), tensor(int64)) : Constrain input and output types.

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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