tf.keras.layers.GroupNormalization

Group normalization layer.

Inherits From: Layer, Module

tf.keras.layers.GroupNormalization(
    groups=32,
    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,
    **kwargs
)

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 nearly identical to Layer Normalization (see Layer Normalization docs for details).

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.

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. Defaults to 32.
axis Integer or List/Tuple. The axis or axes to normalize across. Typically, this is the features axis/axes. The left-out axes are typically the batch axis/axes. -1 is the last dimension in the input. Defaults to -1.
epsilon Small float added to variance to avoid dividing by zero. Defaults to 1e-3
center If True, add offset of beta to normalized tensor. If False, beta is ignored. Defaults to True.
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. Defaults to True.
beta_initializer Initializer for the beta weight. Defaults to zeros.
gamma_initializer Initializer for the gamma weight. Defaults to ones.
beta_regularizer Optional regularizer for the beta weight. None by default.
gamma_regularizer Optional regularizer for the gamma weight. None by default.
beta_constraint Optional constraint for the beta weight. None by default.
gamma_constraint Optional constraint for the gamma weight. None by default. Input shape: 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. Output shape: Same shape as input.

inputs Input tensor (of any rank).
mask The mask parameter is a tensor that indicates the weight for each position in the input tensor when computing the mean and variance.

Reference: - Yuxin Wu & Kaiming He, 2018