tf.math.segment_mean

Computes the mean along segments of a tensor.

tf.math.segment_mean(
    data: _atypes.TensorFuzzingAnnotation[TV_SegmentMean_T],
    segment_ids: _atypes.TensorFuzzingAnnotation[TV_SegmentMean_Tindices],
    name=None
) -> _atypes.TensorFuzzingAnnotation[TV_SegmentMean_T]

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.math.segment_mean(c, tf.constant([0, 0, 1])).numpy()
array([[2.5, 2.5, 2.5, 2.5],
       [5., 6., 7., 8.]], dtype=float32)

data A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, qint16, quint16, 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.