Normalizes along dimension `axis`

using an L2 norm.

tf.compat.v2.linalg.l2_normalize( x, axis=None, epsilon=1e-12, name=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`

.

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). |

Returns | |
---|---|

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

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Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/compat/v2/linalg/l2_normalize