|TensorFlow 1 version||View source on GitHub|
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 )
Used in the notebooks
|Used in the guide|
This is the reduction operation for the elementwise
This Op takes a SparseTensor and is the sparse counterpart to
tf.reduce_max(). In particular, this Op also returns a dense
False, or a
sp_input along the dimensions given in
keepdims is true, the rank of the tensor is reduced by 1 for each entry in
keepdims is true, the reduced dimensions are retained
with length 1.
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
axis. But, in case there are no values in
axis, it will reduce to 0. See second example below.