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Computes tf.sparse.maximum
of elements across dimensions of a SparseTensor. (deprecated arguments) (deprecated arguments)
tf.compat.v1.sparse_reduce_max(
sp_input, axis=None, keepdims=None, reduction_axes=None, keep_dims=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
instead of a sparse one.
Reduces sp_input
along the dimensions given in reduction_axes
. Unless
keepdims
is true, the rank of the tensor is reduced by 1 for each entry in
reduction_axes
. If keepdims
is true, the reduced dimensions are retained
with length 1.
If reduction_axes
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 reduction_axes
. But, in case there are no values in
reduction_axes
, 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. |