# tf.math.l2_normalize

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

### Used in the notebooks

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)`
```

`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`.

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