tf.nn.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: shift is currently not used; the true mean is computed and used.
When using these moments for batch normalization (see tf.nn.batch_normalization
):
[batch, height, width, depth]
, pass axes=[0, 1, 2]
.axes=[0]
(batch only).x
: A Tensor
.axes
: Array of ints. Axes along which to compute mean and variance.shift
: Not used in the current implementationname
: Name used to scope the operations that compute the moments.keep_dims
: produce moments with the same dimensionality as the input.Two Tensor
objects: mean
and variance
.
© 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/moments