Description Usage Arguments Details Value References

Instance normalization layer

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`object` |
Model or layer object |

`groups` |
Integer, the number of groups for Group Normalization. Can be in the range [1, N] where N is the input dimension. The input dimension must be divisible by the number of groups. |

`axis` |
Integer, the axis that should be normalized. |

`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. |

`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. |

`...` |
additional parameters to pass |

Instance Normalization is an specific case of â€œ'GroupNormalizationsinceâ€œ' it normalizes all features of one channel. The Groupsize is equal to the channel size. Empirically, its accuracy is more stable than batch norm in a wide range of small batch sizes, if learning rate is adjusted linearly with batch sizes.

A tensor

[Instance Normalization: The Missing Ingredient for Fast Stylization](https://arxiv.org/abs/1607.08022)

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