tf.keras.layers.LayerNormalization

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

Inherits From: Layer

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