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Computes tf.sparse.maximum
of elements across dimensions of a SparseTensor.
tf.sparse.reduce_max(
sp_input, axis=None, keepdims=None, output_is_sparse=False, name=None
)
This is the reduction operation for the elementwise tf.sparse.maximum
op.
This Op takes a SparseTensor and is the sparse counterpart to
tf.reduce_max()
. In particular, this Op also returns a dense Tensor
if output_is_sparse
is False
, or a SparseTensor
if output_is_sparse
is True
.
Reduces sp_input
along the dimensions given in axis
. Unless
keepdims
is true, the rank of the tensor is reduced by 1 for each entry in
axis
. If keepdims
is true, the reduced dimensions are retained
with length 1.
If axis
has no entries, all dimensions are reduced, and a tensor
with a single element is returned. Additionally, the axes can be negative,
similar to the indexing rules in Python.
The values not defined in sp_input
don't participate in the reduce max,
as opposed to be implicitly assumed 0 -- hence it can return negative values
for sparse axis
. But, in case there are no values in
axis
, it will reduce to 0. See second example below.
For example | |
---|---|
'x' represents [[1, ?, 2][?, 3, ?]]where ? is implicitly-zero.
'y' represents [[-7, ?][ 4, 3][ ?, ?]
|
Returns | |
---|---|
The reduced Tensor or the reduced SparseTensor if output_is_sparse is
True.
|