# tf.compat.v1.sparse_matmul

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Multiply matrix "a" by matrix "b".

    tf.compat.v1.sparse_matmul(
        a,
        b,
        transpose_a=False,
        transpose_b=False,
        a_is_sparse=False,
        b_is_sparse=False,
        name=None
    )

The inputs must be two-dimensional matrices and the inner dimension of "a" must
match the outer dimension of "b". Both "a" and "b" must be `Tensor`s not
`SparseTensor`s. This op is optimized for the case where at least one of "a" or
"b" is sparse, in the sense that they have a large proportion of zero values.
The breakeven for using this versus a dense matrix multiply on one platform was
30% zero values in the sparse matrix.

The gradient computation of this operation will only take advantage of sparsity
in the input gradient when that gradient comes from a Relu.

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| ## Args ||
|---|---|
| `a` | A `Tensor`. Must be one of the following types: `float32`, `bfloat16`. |
| `b` | A `Tensor`. Must be one of the following types: `float32`, `bfloat16`. |
| `transpose_a` | An optional `bool`. Defaults to `False`. |
| `transpose_b` | An optional `bool`. Defaults to `False`. |
| `a_is_sparse` | An optional `bool`. Defaults to `False`. |
| `b_is_sparse` | An optional `bool`. Defaults to `False`. |
| `name` | A name for the operation (optional). |

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| ## Returns ||
|---|---|
| A `Tensor` of type `float32`. ||

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