tf.sparse.reduce_max

Computes tf.sparse.maximum of elements across dimensions of a SparseTensor.

Used in the notebooks

Used in the guide

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: