# 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.

See the guide: Math > Segmentation

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: A Tensor with data that will be assembled in the output.
• indices: A 1-D Tensor with indices into data. Has same rank as segment_ids.
• segment_ids: A 1-D Tensor with indices into the output Tensor. 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 output Tensor.

#### 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.