tf.math.l2_normalize

Normalizes along dimension axis using an L2 norm. (deprecated arguments)

Main aliases

tf.linalg.l2_normalize, tf.nn.l2_normalize

Compat aliases for migration

See Migration guide for more details.

tf.math.l2_normalize(
    x, axis=None, epsilon=1e-12, name=None, dim=None
)

For a 1-D tensor with axis = 0, computes

output = x / sqrt(max(sum(x**2), epsilon))

For x with more dimensions, independently normalizes each 1-D slice along dimension axis.

1-D tensor example:

>>> x = tf.constant([3.0, 4.0])
>>> tf.math.l2_normalize(x).numpy()
array([0.6, 0.8], dtype=float32)

2-D tensor example:

>>> x = tf.constant([[3.0], [4.0]])
>>> tf.math.l2_normalize(x, 0).numpy()
array([[0.6],
     [0.8]], dtype=float32)

x = tf.constant([[3.0], [4.0]])
tf.math.l2_normalize(x, 1).numpy()
array([[1.],
     [1.]], dtype=float32)

Args
`x`	A `Tensor`.
`axis`	Dimension along which to normalize. A scalar or a vector of integers.
`epsilon`	A lower bound value for the norm. Will use `sqrt(epsilon)` as the divisor if `norm < sqrt(epsilon)`.
`name`	A name for this operation (optional).
`dim`	Deprecated, do not use.

Returns
A `Tensor` with the same shape as `x`.

Args