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SparseToDenseMatMul

Description

Performs element-wise binary addition (with Numpy-style broadcasting support). 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.

A (heterogeneous) – T : object, 2-dimensional sparse matrix A. Either COO or CSR format.
B (heterogeneous) – T1 : object, N-dimensional dense matrix B.

 Parameters : cluster,

alpha : float, scalar multiplier for the product of the input tensors.
Default value “1”.
transA : boolean, whether A should be transposed on the last two dimensions before doing multiplication.
Default value “False”.
transB : boolean, whether the layer is in training mode (can store data for backward).
Default value “False”.
 training? : boolean, whether B should be transposed on the last two dimensions before doing multiplication.
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

 

 Y (heterogeneous) – T1 : object, matrix multiply results.

Type Constraints

T in (sparese_tensor(float), sparese_tensor(double),  sparese_tensor(int64), sparese_tensor(int32), sparese_tensor(uint64), sparese_tensor(uint32)) : Constrain input and output types to float tensors.

T1 in (tensor(float), tensor(double), tensor(int64), tensor(int32), tensor(uint64), tensor(uint32)) : Constrain input and 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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