tf.nn.moments

Calculates the mean and variance of x.

tf.nn.moments(
    x, axes, shift=None, keepdims=False, name=None
)

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):

• for so-called "global normalization", used with convolutional filters with shape [batch, height, width, depth], pass axes=[0, 1, 2].
• for simple batch normalization pass axes=[0] (batch only).

Args
x	A Tensor.
axes	Array of ints. Axes along which to compute mean and variance.
shift	Not used in the current implementation.
keepdims	produce moments with the same dimensionality as the input.
name	Name used to scope the operations that compute the moments.

Returns
Two Tensor objects: mean and variance.