tf.nn.moments(
x,
axes,
shift=None,
name=None,
keep_dims=False
)
Defined in tensorflow/python/ops/nn_impl.py
.
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.
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
: 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.
Returns:
Two Tensor
objects: mean
and variance
.