# tf.nn.moments

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.

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
• 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.