tf.nn.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
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.Four Tensor
objects of the same type as x
:
shift
is None.
© 2018 The TensorFlow Authors. All rights reserved.
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