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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()
     [1.]], dtype=float32)

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.

A Tensor with the same shape as x.