# tf.sparse_reduce_max_sparse

tf.sparse_reduce_max_sparse(
sp_input,
axis=None,
keep_dims=False,
reduction_axes=None
)


Computes the max of elements across dimensions of a SparseTensor.

This Op takes a SparseTensor and is the sparse counterpart to tf.reduce_max(). In contrast to SparseReduceSum, this Op returns a SparseTensor.

Reduces sp_input along the dimensions given in reduction_axes. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in reduction_axes. If keep_dims 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, which are interpreted according to the indexing rules in Python.

#### 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.
• keep_dims: If true, retain reduced dimensions with length 1.
• reduction_axes: Deprecated name of axis

#### Returns:

The reduced SparseTensor.