tf.compat.v2.sparse.reduce_max

Computes the max of elements across dimensions of a SparseTensor.

tf.compat.v2.sparse.reduce_max(
    sp_input, axis=None, keepdims=None, output_is_sparse=False, name=None
)

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.
tf.sparse.reduce_max(x) ==> 3
tf.sparse.reduce_max(x, 0) ==> [1, 3, 2]
tf.sparse.reduce_max(x, 1) ==> [2, 3]  # Can also use -1 as the axis.
tf.sparse.reduce_max(x, 1, keepdims=True) ==> [[2], [3]]
tf.sparse.reduce_max(x, [0, 1]) ==> 3

# 'y' represents [[-7, ?]
#                 [ 4, 3]
#                 [ ?, ?]
tf.sparse.reduce_max(x, 1) ==> [-7, 4, 0]

Args
`sp_input`	The SparseTensor to reduce. Should have numeric type.
`axis`	The dimensions to reduce; list or scalar. If `None` (the default), reduces all dimensions.
`keepdims`	If true, retain reduced dimensions with length 1.
`output_is_sparse`	If true, returns a `SparseTensor` instead of a dense `Tensor` (the default).
`name`	A name for the operation (optional).

Returns
The reduced Tensor or the reduced SparseTensor if `output_is_sparse` is True.

tf.compat.v2.sparse.reduce_max

For example:

Args

Returns