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MicrosoftGatherND

Description

Given data tensor of rank r >= 1, and indices tensor of rank q >= 1, gather slices of data into an output tensor of rank q – 1 + r – indices[-1]. Example 1: data = [[0,1],[2,3]] indices = [[0,0],[1,1]] output = [0,3] Example 2: data = [[0,1],[2,3]] indices = [[1],[0]] output = [[2,3],[0,1]] Example 3: data = [[[0,1],[2,3]],[[4,5],[6,7]]] indices = [[0,1],[1,0]] output = [[2,3],[4,5]] Example 4: data = [[[0,1],[2,3]],[[4,5],[6,7]]] indices = [[[0,1]],[[1,0]]] output = [[[2,3]],[[4,5]]]

 

 

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.

 data (heterogeneous) – T : object, tensor of rank r >= 1.
 indices (heterogeneous) – Tind : object, tensor of rank q >= 1.

 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, tensor of rank q-1+r-indices[-1].

Type Constraints

T in (tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32),
tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)) : Constrain input and output types to any tensor type.

Tind in (tensor(int32), tensor(int64)) : Constrain indice type to int32 or int64.

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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