tf.nn.moments( x, axes, shift=None, name=None, keep_dims=False )
See the guide: Neural Network > Normalization
Calculate the mean and variance of
The mean and variance are calculated by aggregating the contents of
x is 1-D and
axes =  this is just the mean
and variance of a vector.
When using these moments for batch normalization (see
- for so-called "global normalization", used with convolutional filters with
[batch, height, width, depth], pass
axes=[0, 1, 2].
- for simple batch normalization pass
axes: Array of ints. Axes along which to compute mean and variance.
shift: Not used in the current implementation
name: Name used to scope the operations that compute the moments.
keep_dims: produce moments with the same dimensionality as the input.