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# tf.sparse.reduce_max

Computes the max of elements across dimensions of a SparseTensor.

``````tf.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) ==> [, ]
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.