Aliases:
tf.sparse.segment_sum
tf.sparse_segment_sum
tf.sparse.segment_sum(
data,
indices,
segment_ids,
name=None,
num_segments=None
)
Defined in tensorflow/python/ops/math_ops.py
.
Computes the sum along sparse segments of a tensor.
Read the section on segmentation for an explanation of segments.
Like SegmentSum
, but segment_ids
can have rank less than data
's first
dimension, selecting a subset of dimension 0, specified by indices
.
segment_ids
is allowed to have missing ids, in which case the output will
be zeros at those indices. In those cases num_segments
is used to determine
the size of the output.
For example:
c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])
# Select two rows, one segment.
tf.sparse.segment_sum(c, tf.constant([0, 1]), tf.constant([0, 0]))
# => [[0 0 0 0]]
# Select two rows, two segment.
tf.sparse.segment_sum(c, tf.constant([0, 1]), tf.constant([0, 1]))
# => [[ 1 2 3 4]
# [-1 -2 -3 -4]]
# With missing segment ids.
tf.sparse.segment_sum(c, tf.constant([0, 1]), tf.constant([0, 2]),
num_segments=4)
# => [[ 1 2 3 4]
# [ 0 0 0 0]
# [-1 -2 -3 -4]
# [ 0 0 0 0]]
# Select all rows, two segments.
tf.sparse.segment_sum(c, tf.constant([0, 1, 2]), tf.constant([0, 0, 1]))
# => [[0 0 0 0]
# [5 6 7 8]]
# Which is equivalent to:
tf.segment_sum(c, tf.constant([0, 0, 1]))
Args:
data
: ATensor
with data that will be assembled in the output.indices
: A 1-DTensor
with indices intodata
. Has same rank assegment_ids
.segment_ids
: A 1-DTensor
with indices into the outputTensor
. Values should be sorted and can be repeated.name
: A name for the operation (optional).num_segments
: An optional int32 scalar. Indicates the size of the outputTensor
.
Returns:
A tensor
of the shape as data, except for dimension 0 which
has size k
, the number of segments specified via num_segments
or
inferred for the last element in segments_ids
.