tf.nn.sufficient_statistics
tf.nn.sufficient_statistics
tf.nn.sufficient_statistics
sufficient_statistics( x, axes, shift=None, keep_dims=False, name=None )
Defined in tensorflow/python/ops/nn_impl.py
.
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
: ATensor
. -
axes
: Array of ints. Axes along which to compute mean and variance. -
shift
: ATensor
containing the value by which to shift the data for numerical stability, orNone
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.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/nn/sufficient_statistics