tf.sparse.reduce_sum

Computes the sum of elements across dimensions of a SparseTensor.

View aliases

Compat aliases for migration

tf.compat.v2.sparse.reduce_sum

tf.sparse.reduce_sum(
    sp_input, axis=None, keepdims=None, output_is_sparse=False, name=None
)

This Op takes a SparseTensor and is the sparse counterpart to tf.reduce_sum(). In particular, this Op also returns a dense Tensor if output_is_sparse is False, or a SparseTensor if output_is_sparse is True.

Note: if output_is_sparse is True, 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 axis. Unless keepdims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keepdims is true, the reduced dimensions are retained with length 1.

If axis 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.
tf.sparse.reduce_sum(x) ==> 3
tf.sparse.reduce_sum(x, 0) ==> [1, 1, 1]
tf.sparse.reduce_sum(x, 1) ==> [2, 1]  # Can also use -1 as the axis.
tf.sparse.reduce_sum(x, 1, keepdims=True) ==> [[2], [1]]
tf.sparse.reduce_sum(x, [0, 1]) ==> 3

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.
output_is_sparse: If true, returns a SparseTensor instead of a dense Tensor (the default).
name: A name for the operation (optional).

Returns:

The reduced Tensor or the reduced SparseTensor if output_is_sparse is True.