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RandomUniformLike

Description

Generate a tensor with random values drawn from a uniform distribution. The shape of the output tensor is copied from the shape of the input tensor, and the parameters of the uniform distribution are specified by low and high. The data type is specified by the ‘dtype’ argument, or copied from the input tensor if not provided. The ‘dtype’ argument must be one of the data types specified in the ‘DataType’ enum field in the TensorProto message and be valid as an output type.

 

Input parameters

 

 specified_outputs_name : array, this parameter lets you manually assign custom names to the output tensors of a node.
 input (heterogeneous) – T1 : object, input tensor to copy shape and optionally type information from.

 Parameters : cluster,

dtype : enum, the data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.
Default value “UNDEFINED”.
high : float, upper boundary of the output values.
Default value “1”.
low : float, lower boundary of the output values.
Default value “0”.
seed : float, seed to the random generator, if not specified we will auto generate one.
Default value “0”.
 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) – T2 : object, output tensor of random values drawn from uniform distribution.

Type Constraints

T1 in (tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(int16), 
tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)) : Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.

T2 in (tensor(double), tensor(float), 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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