Function sufficient_statistics_v2

tensorflow/python/ops/nn_impl.py:1210–1235

Calculate the sufficient statistics for the mean and variance of `x`. These sufficient statistics are computed using the one pass algorithm on an input that's optionally shifted. See: https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Computing_shifted_data Args: x: A `T

(x, axes, shift=None, keepdims=False, name=None)

@tf_export("nn.sufficient_statistics", v1=[])
def sufficient_statistics_v2(x, axes, shift=None, keepdims=False, name=None):
  """Calculate the sufficient statistics for the mean and variance of `x`.

  These sufficient statistics are computed using the one pass algorithm on
  an input that's optionally shifted. See:
  https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Computing_shifted_data

  Args:
    x: A `Tensor`.
    axes: Array of ints. Axes along which to compute mean and variance.
    shift: A `Tensor` containing the value by which to shift the data for
      numerical stability, or `None` if no shift is to be performed. A shift
      close to the true mean provides the most numerically stable results.
    keepdims: produce statistics with the same dimensionality as the input.
    name: Name used to scope the operations that compute the sufficient stats.

  Returns:
    Four `Tensor` objects of the same type as `x`:

    * the count (number of elements to average over).
    * the (possibly shifted) sum of the elements in the array.
    * the (possibly shifted) sum of squares of the elements in the array.
    * the shift by which the mean must be corrected or None if `shift` is None.
  """
  return sufficient_statistics(
      x=x, axes=axes, shift=shift, keep_dims=keepdims, name=name)


@tf_export("nn.normalize_moments")