tf.math.unsorted_segment_mean

Computes the mean along segments of a tensor.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.math.unsorted_segment_mean, tf.compat.v1.unsorted_segment_mean

tf.math.unsorted_segment_mean(
    data, segment_ids, num_segments, name=None
)

Read the section on segmentation for an explanation of segments.

This operator is similar to the tf.math.unsorted_segment_sum operator. Instead of computing the sum over segments, it computes the mean of all entries belonging to a segment such that:

\(output_i = 1/N_i \sum_{j...} data[j...]\) where the sum is over tuples j... such that segment_ids[j...] == i with \N_i\ being the number of occurrences of id \i\.

If there is no entry for a given segment ID i, it outputs 0.

If the given segment ID i is negative, the value is dropped and will not be added to the sum of the segment.

Caution: On CPU, values in segment_ids are always validated to be less than num_segments, and an error is thrown for out-of-bound indices. On GPU, this does not throw an error for out-of-bound indices. On Gpu, out-of-bound indices result in safe but unspecified behavior, which may include ignoring out-of-bound indices or outputting a tensor with a 0 stored in the first dimension of its shape if num_segments is 0.

Args
data	A Tensor with floating point or complex dtype.
segment_ids	An integer tensor whose shape is a prefix of data.shape. The values must be less than num_segments. The values are always validated to be in range on CPU, never validated on GPU.
num_segments	An integer scalar Tensor. The number of distinct segment IDs.
name	A name for the operation (optional).

Returns
A Tensor. Has same shape as data, except for the first segment_ids.rank dimensions, which are replaced with a single dimension which has size num_segments.