Description Usage Arguments Details Value Note

Filter response normalization layer.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
layer_filter_response_normalization(
object,
epsilon = 1e-06,
axis = c(1, 2),
beta_initializer = "zeros",
gamma_initializer = "ones",
beta_regularizer = NULL,
gamma_regularizer = NULL,
beta_constraint = NULL,
gamma_constraint = NULL,
learned_epsilon = FALSE,
learned_epsilon_constraint = NULL,
name = NULL
)
``` |

`object` |
Model or layer object |

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

`axis` |
List of axes that should be normalized. This should represent the spatial dimensions. |

`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. |

`learned_epsilon` |
(bool) Whether to add another learnable epsilon parameter or not. |

`learned_epsilon_constraint` |
learned_epsilon_constraint |

`name` |
Optional name for the layer |

Filter Response Normalization (FRN), a normalization method that enables models trained with per-channel normalization to achieve high accuracy. It performs better than all other normalization techniques for small batches and is par with Batch Normalization for bigger batch sizes.

A tensor

Input shape Arbitrary. Use the keyword argument 'input_shape' (list of integers, does not include the samples axis) when using this layer as the first layer in a model. This layer, as of now, works on a 4-D tensor where the tensor should have the shape [N X H X W X C] TODO: Add support for NCHW data format and FC layers. Output shape Same shape as input. References - [Filter Response Normalization Layer: Eliminating Batch Dependence in the training of Deep Neural Networks] (https://arxiv.org/abs/1911.09737)

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