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

Used in the notebooks

Used in the tutorials

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