tf.math.segment_mean
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Computes the mean along segments of a tensor.
tf.math.segment_mean(
data, segment_ids, name=None
)
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]]
Args |
|---|
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).
|
Returns |
|---|
A Tensor. Has the same type as data.
|
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Last updated 2022-10-27 UTC.
[null,null,["Last updated 2022-10-27 UTC."],[],[],null,["# tf.math.segment_mean\n\nComputes the mean along segments of a tensor.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.math.segment_mean`](https://www.tensorflow.org/api_docs/python/tf/math/segment_mean), [`tf.compat.v1.segment_mean`](https://www.tensorflow.org/api_docs/python/tf/math/segment_mean)\n\n\u003cbr /\u003e\n\n tf.math.segment_mean(\n data, segment_ids, name=None\n )\n\nRead\n[the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation)\nfor an explanation of segments.\n\nComputes a tensor such that\n\\\\(output_i = \\\\frac{\\\\sum_j data_j}{N}\\\\) where `mean` is\nover `j` such that `segment_ids[j] == i` and `N` is the total number of\nvalues summed.\n\nIf the mean is empty for a given segment ID `i`, `output[i] = 0`. \n\n#### For example:\n\n c = tf.constant([[1.0,2,3,4], [4, 3, 2, 1], [5,6,7,8]])\n tf.segment_mean(c, tf.constant([0, 0, 1]))\n # ==\u003e [[2.5, 2.5, 2.5, 2.5],\n # [5, 6, 7, 8]]\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `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`. |\n| `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. |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `Tensor`. Has the same type as `data`. ||\n\n\u003cbr /\u003e"]]
