InstanceNorm

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InstanceNorm#

class brainpy.dnn.InstanceNorm(num_channels, epsilon=1e-05, affine=True, bias_initializer=ZeroInit, scale_initializer=OneInit(value=1.0), mode=None, name=None)[source]#

Instance normalization layer.

This layer normalizes the data within each feature. It can be regarded as a group normalization layer in which group_size equals to 1.

Parameters:

  • num_channels (int) – The number of channels expected in input.

  • epsilon (float) – a value added to the denominator for numerical stability. Default: 1e-5

  • affine (bool) – A boolean value that when set to True, this module has learnable per-channel affine parameters initialized to ones (for weights) and zeros (for biases). Default: True.

  • bias_initializer (Union[Initializer, TypeVar(ArrayType, Array, Variable, TrainVar, Array, ndarray), Callable]) – an initializer generating the original translation matrix

  • scale_initializer (Union[Initializer, TypeVar(ArrayType, Array, Variable, TrainVar, Array, ndarray), Callable]) – an initializer generating the original scaling matrix

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