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Computes tf.sparse.add of elements across dimensions of a SparseTensor. (deprecated arguments) (deprecated arguments)

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

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

This Op takes a SparseTensor and is the sparse counterpart to tf.reduce_sum(). 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.

For example:

'x' represents [[1, ?, 1]

[?, 1, ?]]

where ? is implicitly-zero.

x = tf.sparse.SparseTensor([[0, 0], [0, 2], [1, 1]], [1, 1, 1], [2, 3])
tf.sparse.reduce_sum(x)
<tf.Tensor: shape=(), dtype=int32, numpy=3>
tf.sparse.reduce_sum(x, 0)
<tf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 1, 1], dtype=int32)>
tf.sparse.reduce_sum(x, 1)  # Can also use -1 as the axis
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([2, 1], dtype=int32)>
tf.sparse.reduce_sum(x, 1, keepdims=True)
<tf.Tensor: shape=(2, 1), dtype=int32, numpy=
array([[2],
       [1]], dtype=int32)>
tf.sparse.reduce_sum(x, [0, 1])
<tf.Tensor: shape=(), dtype=int32, numpy=3>