tf.compat.v1.sparse_reduce_max

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

tf.compat.v1.sparse_reduce_max(
    sp_input, axis=None, keepdims=None, reduction_axes=None, keep_dims=None
)

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

Warning: SOME ARGUMENTS ARE DEPRECATED: (reduction_axes). They will be removed in a future version. Instructions for updating: reduction_axes is deprecated, use axis instead

This Op takes a SparseTensor and is the sparse counterpart to tf.reduce_max(). In particular, this Op also returns a dense Tensor instead of a sparse one.

Note: A gradient is not defined for this function, so it can't be used in training models that need gradient descent.

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, 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 reduction_axes. But, in case there are no values in reduction_axes, 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:

Returns:

The reduced Tensor.