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Updated
TransposeMatMul
Description
Duplicate of FusedMatMul. Going forward FusedMatMul should be used. This OP will be supported for backward compatibility. Matrix product that behaves like numpy.matmul:Β https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html
Β
Input parameters
specified_outputs_name :Β array, this parameter lets you manually assign custom names to the output tensors of a node.
Β Graphs in :Β cluster, ONNX model architecture.
AΒ (heterogeneous) –Β T : object, N-dimensional matrix A. B (heterogeneous) – T : object, N-dimensional matrix B.
Β Parameters : cluster,
alpha : float, scalar multiplier for the product of the input tensors.
Default value β1β.
transAΒ :Β boolean, whether A should be transposed on the last two dimensions before doing multiplication.
Default value βFalseβ. transBΒ :Β boolean, whether B should be transposed on the last two dimensions before doing multiplication.
Default value βFalseβ.Β training?Β :Β boolean, whether the layer is in training mode (can store data for backward).
Default value βTrueβ.
Β lda coeff :Β 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 β1β.
Β name (optional) :Β string, name of the node.
Output parameters
Y (heterogeneous) – T : object, matrix multiply results.
Type Constraints
tensor(float16), Β tensor(float),Β tensor(double),Β tensor(bfloat16)) : 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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