tf.sparse.add

Adds two tensors, at least one of each is a SparseTensor.

View aliases

Compat aliases for migration

tf.compat.v2.sparse.add

tf.sparse.add(
    a, b, threshold=0
)

If one SparseTensor and one Tensor are passed in, returns a Tensor. If both arguments are SparseTensors, this returns a SparseTensor. The order of arguments does not matter. Use vanilla tf.add() for adding two dense Tensors.

The shapes of the two operands must match: broadcasting is not supported.

The indices of any input SparseTensor are assumed ordered in standard lexicographic order. If this is not the case, before this step run SparseReorder to restore index ordering.

If both arguments are sparse, we perform "clipping" as follows. By default, if two values sum to zero at some index, the output SparseTensor would still include that particular location in its index, storing a zero in the corresponding value slot. To override this, callers can specify threshold, indicating that if the sum has a magnitude strictly smaller than threshold, its corresponding value and index would then not be included. In particular, threshold == 0.0 (default) means everything is kept and actual thresholding happens only for a positive value.

For example, suppose the logical sum of two sparse operands is (densified):

[       2]
[.1     0]
[ 6   -.2]

Then,

threshold == 0 (the default): all 5 index/value pairs will be returned.
threshold == 0.11: only .1 and 0 will vanish, and the remaining three index/value pairs will be returned.
threshold == 0.21: .1, 0, and -.2 will vanish.

Args:

a: The first operand; SparseTensor or Tensor.
b: The second operand; SparseTensor or Tensor. At least one operand must be sparse.
threshold: A 0-D Tensor. The magnitude threshold that determines if an output value/index pair takes space. Its dtype should match that of the values if they are real; if the latter are complex64/complex128, then the dtype should be float32/float64, correspondingly.

Returns:

A SparseTensor or a Tensor, representing the sum.

Raises:

TypeError: If both a and b are Tensors. Use tf.add() instead.