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# tf.raw_ops.SegmentMean

Computes the mean along segments of a tensor.

Read the section on segmentation for an explanation of segments.

Computes a tensor such that $$output_i = \frac{\sum_j data_j}{N}$$ where mean is over j such that segment_ids[j] == i and N is the total number of values summed.

If the mean is empty for a given segment ID i, output[i] = 0.

#### For example:

c = tf.constant([[1.0,2,3,4], [4, 3, 2, 1], [5,6,7,8]])
tf.segment_mean(c, tf.constant([0, 0, 1]))
# ==> [[2.5, 2.5, 2.5, 2.5],
#      [5, 6, 7, 8]]


data A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64.
segment_ids A Tensor. Must be one of the following types: int32, int64. A 1-D tensor whose size is equal to the size of data's first dimension. Values should be sorted and can be repeated.
name A name for the operation (optional).

A Tensor. Has the same type as data.

[{ "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"label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]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" }]