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.
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 .
|