tf.nn.sufficient_statistics

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Calculate the sufficient statistics for the mean and variance of x.

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

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.