tf.sparse.segment_sum

Computes the sum along sparse segments of a tensor.

tf.sparse.segment_sum( data, indices, segment_ids, num_segments=None, name=None, sparse_gradient=False )

Read the section on segmentation for an explanation of segments.

Like tf.math.segment_sum, 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.math.segment_sum(c, tf.constant([0, 0, 1]))

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.
num_segments An optional int32 scalar. Indicates the size of the output Tensor.
name A name for the operation (optional).
sparse_gradient An optional bool. Defaults to False. If True, the gradient of this function will be sparse (IndexedSlices) instead of dense (Tensor). The sparse gradient will contain one non-zero row for each unique index in indices.

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.