tf.math.segment_mean
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 . |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/math/segment_mean