tf.raw_ops.SparseSegmentSum

Computes the sum along sparse segments of a tensor.

tf.raw_ops.SparseSegmentSum( data, indices, segment_ids, sparse_gradient=False, name=None )

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]))

data A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.
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. Must be one of the following types: int32, int64. A 1-D tensor. Values should be sorted and can be repeated.
sparse_gradient An optional bool. Defaults to False.
name A name for the operation (optional).

A Tensor. Has the same type as data.