Description Usage Arguments Details Value

Group 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 |

Group Normalization divides the channels into groups and computes within each group the mean and variance for normalization. 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. Relation to Layer Normalization: If the number of groups is set to 1, then this operation becomes identical to Layer Normalization. Relation to Instance Normalization: If the number of groups is set to the input dimension (number of groups is equal to number of channels), then this operation becomes identical to Instance Normalization.

A tensor

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