tf.nn.moments
tf.nn.moments
tf.nn.moments
moments( x, axes, shift=None, name=None, keep_dims=False )
Defined in tensorflow/python/ops/nn_impl.py
.
See the guide: Neural Network > Normalization
Calculate the mean and variance of x
.
The mean and variance are calculated by aggregating the contents of x
across axes
. If x
is 1-D and axes = [0]
this is just the mean and variance of a vector.
Note: for numerical stability, when shift=None, the true mean would be computed and used as shift.
When using these moments for batch normalization (see tf.nn.batch_normalization
):
- for so-called "global normalization", used with convolutional filters with shape
[batch, height, width, depth]
, passaxes=[0, 1, 2]
. - for simple batch normalization pass
axes=[0]
(batch only).
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
in which case the true mean of the data is used as shift. A shift close to the true mean provides the most numerically stable results. -
name
: Name used to scope the operations that compute the moments. -
keep_dims
: produce moments with the same dimensionality as the input.
Returns:
Two Tensor
objects: mean
and variance
.
© 2017 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/moments