tf.compat.v1.sparse_matmul
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Multiply matrix "a" by matrix "b". (deprecated)
tf.compat.v1.sparse_matmul(
a: Annotated[Any, tf.raw_ops.Any
],
b: Annotated[Any, tf.raw_ops.Any
],
transpose_a: bool = False,
transpose_b: bool = False,
a_is_sparse: bool = False,
b_is_sparse: bool = False,
name=None
) -> Annotated[Any, tf.raw_ops.Any
]
Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version.
Instructions for updating:
Use tf.linalg.matmul
instead
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.
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).
Returns
A Tensor
of type float32
.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license .
Last updated 2024-01-23 UTC.
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