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QLinearWhere

Description

Return elements, either from X or Y, depending on condition.

 

 

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.

condition (heterogeneous) – B : object, when True (nonzero), yield x, otherwise yield y.
 X (heterogeneous) – T : object, Y’s zero point.
 x_scale (heterogeneous) – TF : object, X’s scale.
 x_zero_point (heterogeneous) – T : object, X’s zero point.
 Y (heterogeneous) – T : object, Y’s zero point.
 y_scale (heterogeneous) – TF : object, Y’s scale.
 y_zero_point (heterogeneous) – T : object, Y’s zero point.
 z_scale (heterogeneous) – TF : object, Z’s scale.
 z_zero_point (heterogeneous) – T : object, Z’s zero point.

 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, tensor of shape equal to the broadcasted shape of condition, X, and Y

Type Constraints

B in (tensor(bool)) : Constrain input and output types to 8 bit signed and unsigned tensors.

TF in (tensor(float)) : Constrain scale types to any float tensor type.

T in (tensor(uint8), tensor(int8)) : Constrain input and output types to 8 bit signed and unsigned 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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