# tf.compat.v2.sparse.segment_mean

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/ops/math_ops.py#L3800-L3835) |

Computes the mean along sparse segments of a tensor.

    tf.compat.v2.sparse.segment_mean(
        data, indices, segment_ids, num_segments=None, name=None
    )

Read [the section on
segmentation](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/math#about_segmentation)
for an explanation of segments.

Like [`tf.math.segment_mean`](https://www.tensorflow.org/versions/r1.15/api_docs/python/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.

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| ## 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. |
| `num_segments` | An optional int32 scalar. Indicates the size of the output `Tensor`. |
| `name` | A name for the operation (optional). |

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| ## Returns ||
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| 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`. ||

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