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Computes tf.sparse.maximum 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
)

This is the reduction operation for the elementwise tf.sparse.maximum op.

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.

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.

x = tf.sparse.SparseTensor([[0, 0], [0, 2], [1, 1]], [1, 2, 3], [2, 3])
tf.sparse.reduce_max(x)
<tf.Tensor: shape=(), dtype=int32, numpy=3>
tf.sparse.reduce_max(x, 0)
<tf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 3, 2], dtype=int32)>
tf.sparse.reduce_max(x, 1)
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([2, 3], dtype=int32)>
tf.sparse.reduce_max(x, 1, keepdims=True)
<tf.Tensor: shape=(2, 1), dtype=int32, numpy=
array([[2],
       [3]], dtype=int32)>
tf.sparse.reduce_max(x, [0, 1])
<tf.Tensor: shape=(), dtype=int32, numpy=3>

y = tf.sparse.SparseTensor([[0, 0,], [1, 0], [1, 1]], [-7, 4, 3],
[3, 2])
tf.sparse.reduce_max(y, 1)
<tf.Tensor: shape=(3,), dtype=int32, numpy=array([-7,  4,  0], dtype=int32)>