Multiply matrix "a" by matrix "b".
tf.raw_ops.SparseMatMul( 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.
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 . |
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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/SparseMatMul