tf.nn.sufficient_statistics(x, axes, shift=None, keep_dims=False, name=None)

tf.nn.sufficient_statistics(x, axes, shift=None, keep_dims=False, name=None)

See the guide: Neural Network > Normalization

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.
  • keep_dims: 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.

Defined in tensorflow/python/ops/nn_impl.py.