Multiply SparseTensor (of rank 2) "A" by dense matrix "B".

tf.raw_ops.SparseTensorDenseMatMul( a_indices, a_values, a_shape, b, adjoint_a=False, adjoint_b=False, name=None )

No validity checking is performed on the indices of A. However, the following input format is recommended for optimal behavior:

if adjoint_a == false: A should be sorted in lexicographically increasing order. Use SparseReorder if you're not sure. if adjoint_a == true: A should be sorted in order of increasing dimension 1 (i.e., "column major" order instead of "row major" order).

Args | |
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`a_indices` | A `Tensor` . Must be one of the following types: `int32` , `int64` . 2-D. The `indices` of the `SparseTensor` , size `[nnz, 2]` Matrix. |

`a_values` | A `Tensor` . 1-D. The `values` of the `SparseTensor` , size `[nnz]` Vector. |

`a_shape` | A `Tensor` of type `int64` . 1-D. The `shape` of the `SparseTensor` , size `[2]` Vector. |

`b` | A `Tensor` . Must have the same type as `a_values` . 2-D. A dense Matrix. |

`adjoint_a` | An optional `bool` . Defaults to `False` . Use the adjoint of A in the matrix multiply. If A is complex, this is transpose(conj(A)). Otherwise it's transpose(A). |

`adjoint_b` | An optional `bool` . Defaults to `False` . Use the adjoint of B in the matrix multiply. If B is complex, this is transpose(conj(B)). Otherwise it's transpose(B). |

`name` | A name for the operation (optional). |

Returns | |
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A `Tensor` . Has the same type as `a_values` . |

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Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/raw_ops/SparseTensorDenseMatMul