tf.keras.layers.LayerNormalization

Layer normalization layer (Ba et al., 2016).

Inherits From: Layer

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.layers.LayerNormalization, `tf.compat.v2.keras.layers.LayerNormalization`

tf.keras.layers.LayerNormalization(
    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, trainable=True, name=None, **kwargs
)

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.

Arguments
axis	Integer or List/Tuple. The axis that should be normalized (typically the features axis).
epsilon	Small float added to variance to avoid dividing by zero.
center	If True, add offset of beta to normalized tensor. If False, beta is ignored.
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.
beta_initializer	Initializer for the beta weight.
gamma_initializer	Initializer for the gamma weight.
beta_regularizer	Optional regularizer for the beta weight.
gamma_regularizer	Optional regularizer for the gamma weight.
beta_constraint	Optional constraint for the beta weight.
gamma_constraint	Optional constraint for the gamma weight.
trainable	Boolean, if True the variables will be marked as trainable.

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.

References:

• Layer Normalization