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Range

Description

Generate a tensor containing a sequence of numbers that begin at start and extends by increments of delta up to limit (exclusive).

 

 

The number of elements in the output of range is computed as below :

number_of_elements = max( ceil( (limit - start) / delta ) , 0 )

The pseudocode determining the contents of the output is shown below :

for(int i=0; i<number_of_elements; ++i) {
  output[i] =  start + (i * delta);
}

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, scalar. First entry for the range of output values.
 limit (heterogeneous) – T : object, scalar. Exclusive upper limit for the range of output values.
 delta (heterogeneous) – T : object, scalar. Value to step by.

 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, a 1-D tensor with same type as the inputs containing generated range of values.

Type Constraints

T in (tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64)) : Constrain input types to common numeric type 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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