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Pow

Description

Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function f(x) = x^exponent, is applied to the data tensor elementwise. This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check Broadcasting in ONNX.

 

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.

X (heterogeneous) – T : object, first operand, base of the exponent.
Y (heterogeneous) – T1 : object, second operand, power of the exponent.

 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

 

 Z (heterogeneous) – T : object, output tensor.

Type Constraints

T in (tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)) : Constrain input X and output types to float/int tensors.

T1 in (tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), 
tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)) : Constrain input Y types to float/int 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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