TensorFlow 2 version |
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 .
|