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
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
axes: Array of ints. Axes along which to compute mean and variance.
Tensorcontaining the value by which to shift the data for numerical stability, or
Noneif 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.
Tensor objects of the same type as
- 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