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UpdatedOctober 27, 2025

RandomUniform

Description

Generate a tensor with random values drawn from a uniform distribution. The shape of the tensor is specified by theΒ shapeΒ argument and the range byΒ lowΒ andΒ high. The data type is specified by the β€˜dtype’ argument. The β€˜dtype’ argument must be one of the data types specified in the β€˜DataType’ enum field in the TensorProto message.

 

Input parameters

 

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

Β Parameters :Β cluster,

dtype :Β enum, the data type for the elements of the output tensor.
Default value β€œFLOAT”.
high : float, upper boundary of the output values.
Default value β€œ1”.
low : float, lower boundary of the output values.
Default value β€œ0”.
seed : float, seed to the random generator, if not specified we will auto generate one.
Default value β€œ0”.
shape : array, the shape of the output tensor.
Default value β€œempty”.
Β training?Β :Β boolean, whether the layer is in training mode (can store data for backward).
Default value β€œTrue”.
Β lda coeff :Β 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 β€œ1”.

Β name (optional) :Β string,Β name of the node.

Output parameters

 

outputΒ (heterogeneous) –Β TΒ : object, output tensor of random values drawn from uniform distribution.

Type Constraints

T in (tensor(double),Β tensor(float),Β tensor(float16)) : Constrain output types to float 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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