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tf.sparse.segment_mean

Computes the mean along sparse segments of a tensor.

Read the section on segmentation for an explanation of segments.

Like tf.math.segment_mean, 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.

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.

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.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]