Normalize the activations of the previous layer for each given example in a batch independently, rather than across a batch like Batch Normalization. i.e. applies a transformation that maintains the mean activation within each example close to 0 and the activation standard deviation close to 1.

layer_layer_normalization(
object,
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,
trainable = TRUE,
name = NULL
)

## Arguments

object

What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input()). The return value depends on object. If object is:

• missing or NULL, the Layer instance is returned.

• a Sequential model, the model with an additional layer is returned.

• a Tensor, the output tensor from layer_instance(object) is returned.

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. This argument defaults to -1, the last dimension in the input.

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. Defaults to True. 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.

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.

trainable

Boolean, if True the variables will be marked as trainable. Defaults to True.

name

An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.

## Details

Given a tensor inputs, moments are calculated and normalization is performed across the axes specified in axis.