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# tf.nn.sufficient_statistics

```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

#### 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.
• `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.

© 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