Instance normalization layer
layer_instance_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
Instance Normalization is an specific case of ```GroupNormalizationsince``` it normalizes all features of one channel. The Groupsize is equal to the channel size. 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.
[Instance Normalization: The Missing Ingredient for Fast Stylization](https://arxiv.org/abs/1607.08022)