tf.sparse_segment_sum()

tf.sparse_segment_sum(data, indices, segment_ids, name=None)

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.

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]]

# 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. Must be one of the following types: float32, float64, int32, int64, uint8, int16, int8, uint16, half.
• indices: A Tensor. Must be one of the following types: int32, int64. A 1-D tensor. Has same rank as segment_ids.
• segment_ids: A Tensor of type int32. A 1-D tensor. Values should be sorted and can be repeated.
• name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as data. Has same shape as data, except for dimension 0 which has size k, the number of segments.