tf.sparse_reduce_max_sparse

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

Defined in tensorflow/python/ops/sparse_ops.py.

Computes the max of elements across dimensions of a SparseTensor. (deprecated arguments)

SOME ARGUMENTS ARE DEPRECATED. They will be removed in a future version. Instructions for updating: keep_dims is deprecated, use keepdims instead

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 keepdims is true, the rank of the tensor is reduced by 1 for each entry in reduction_axes. If keepdims 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.
  • keepdims: If true, retain reduced dimensions with length 1.
  • reduction_axes: Deprecated name of axis.
  • keep_dims: Deprecated alias for keepdims.

Returns:

The reduced SparseTensor.