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Layer normalization layer (Ba et al., 2016).

Inherits From: `Layer`

tf.keras.layers.LayerNormalization( axis=-1, epsilon=0.001, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None, trainable=True, name=None, **kwargs )

Normalize the activations of the previous layer for each given example in a batch independently, rather than across a batch like Batch Normalization. i.e. applies a transformation that maintains the mean activation within each example close to 0 and the activation standard deviation close to 1.

Arguments | |
---|---|

`axis` | Integer or List/Tuple. The axis that should be normalized (typically the features axis). |

`epsilon` | Small float added to variance to avoid dividing by zero. |

`center` | If True, add offset of `beta` to normalized tensor. If False, `beta` is ignored. |

`scale` | If True, multiply by `gamma` . If False, `gamma` is not used. When the next layer is linear (also e.g. `nn.relu` ), this can be disabled since the scaling will be done by the next layer. |

`beta_initializer` | Initializer for the beta weight. |

`gamma_initializer` | Initializer for the gamma weight. |

`beta_regularizer` | Optional regularizer for the beta weight. |

`gamma_regularizer` | Optional regularizer for the gamma weight. |

`beta_constraint` | Optional constraint for the beta weight. |

`gamma_constraint` | Optional constraint for the gamma weight. |

`trainable` | Boolean, if `True` the variables will be marked as trainable. |

Arbitrary. Use the keyword argument `input_shape`

(tuple of integers, does not include the samples axis) when using this layer as the first layer in a model.

Same shape as input.

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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/keras/layers/LayerNormalization