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tf.sparse.segment_mean

Computes the mean along sparse segments of a tensor.

Aliases:

  • tf.compat.v1.sparse.segment_mean
  • tf.compat.v1.sparse_segment_mean
  • tf.sparse.segment_mean
  • tf.sparse_segment_mean
tf.sparse.segment_mean(
    data,
    indices,
    segment_ids,
    name=None,
    num_segments=None
)
View source on GitHub

Read the section on segmentation for an explanation of segments.

Like SegmentMean, 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.

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.