Group normalization layer
layer_group_normalization(
object,
groups = 2,
axis = -1,
epsilon = 0.001,
center = TRUE,
scale = TRUE,
beta_initializer = "zeros",
gamma_initializer = "ones",
beta_regularizer = NULL,
gamma_regularizer = NULL,
beta_constraint = NULL,
gamma_constraint = NULL,
...
)Model or layer object
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.
Integer, the axis that should be normalized.
Small float added to variance to avoid dividing by zero.
If TRUE, add offset of beta to normalized tensor. If False, beta is ignored.
If TRUE, multiply by gamma. If False, gamma is not used.
Initializer for the beta weight.
Initializer for the gamma weight.
Optional regularizer for the beta weight.
Optional regularizer for the gamma weight.
Optional constraint for the beta weight.
Optional constraint for the gamma weight.
additional parameters to pass
A tensor
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.