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.