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

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

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.

Note: for numerical stability, when shift=None, the true mean would be computed and used as shift.

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: A Tensor containing the value by which to shift the data for numerical stability, or None in which case the true mean of the data is used as shift. A shift close to the true mean provides the most numerically stable results.
• 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.

Defined in tensorflow/python/ops/nn_impl.py.