def normalize_moments(counts, mean_ss, variance_ss, shift, name=None):
"""Calculate the mean and variance of based on the sufficient statistics.
Args:
counts: A `Tensor` containing a the total count of the data (one value).
mean_ss: A `Tensor` containing the mean sufficient statistics: the (possibly
shifted) sum of the elements to average over.
variance_ss: A `Tensor` containing the variance sufficient statistics: the
(possibly shifted) squared sum of the data to compute the variance over.
shift: A `Tensor` containing the value by which the data is shifted for
numerical stability, or `None` if no shift was performed.
name: Name used to scope the operations that compute the moments.
Returns:
Two `Tensor` objects: `mean` and `variance`.
"""
with tf.variable_scope(name, "normalize", [counts, mean_ss, variance_ss, shift]):
divisor = math_ops.reciprocal(counts, name="divisor")
if shift is not None:
shifted_mean = math_ops.multiply(mean_ss, divisor, name="shifted_mean")
mean = math_ops.add(shifted_mean, shift, name="mean")
else: # no shift.
shifted_mean = math_ops.multiply(mean_ss, divisor, name="mean")
mean = shifted_mean
variance = math_ops.subtract(math_ops.multiply(variance_ss, divisor),
math_ops.square(shifted_mean),
name="variance")
return (mean, variance)
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