tf.keras.layers.LayerNormalization

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

Inherits From: Layer, Operation

tf.keras.layers.LayerNormalization(
    axis=-1,
    epsilon=0.001,
    center=True,
    scale=True,
    rms_scaling=False,
    beta_initializer='zeros',
    gamma_initializer='ones',
    beta_regularizer=None,
    gamma_regularizer=None,
    beta_constraint=None,
    gamma_constraint=None,
    **kwargs
)

Used in the notebooks

Used in the tutorials

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.

If scale or center are enabled, the layer will scale the normalized outputs by broadcasting them with a trainable variable gamma, and center the outputs by broadcasting with a trainable variable beta. gamma will default to a ones tensor and beta will default to a zeros tensor, so that centering and scaling are no-ops before training has begun.

So, with scaling and centering enabled the normalization equations are as follows:

Let the intermediate activations for a mini-batch to be the inputs.

For each sample x_i in inputs with k features, we compute the mean and variance of the sample:

mean_i = sum(x_i[j] for j in range(k)) / k
var_i = sum((x_i[j] - mean_i) ** 2 for j in range(k)) / k

and then compute a normalized x_i_normalized, including a small factor epsilon for numerical stability.

x_i_normalized = (x_i - mean_i) / sqrt(var_i + epsilon)

And finally x_i_normalized is linearly transformed by gamma and beta, which are learned parameters:

output_i = x_i_normalized * gamma + beta

gamma and beta will span the axes of inputs specified in axis, and this part of the inputs' shape must be fully defined.

For example:

layer = keras.layers.LayerNormalization(axis=[1, 2, 3])
layer.build([5, 20, 30, 40])
print(layer.beta.shape)
(20, 30, 40)
print(layer.gamma.shape)
(20, 30, 40)

Note that other implementations of layer normalization may choose to define gamma and beta over a separate set of axes from the axes being normalized across. For example, Group Normalization (Wu et al. 2018) with group size of 1 corresponds to a Layer Normalization that normalizes across height, width, and channel and has gamma and beta span only the channel dimension. So, this Layer Normalization implementation will not match a Group Normalization layer with group size set to 1.

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. -1 is the last dimension in the input. Defaults to -1.
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. 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. Defaults to True.
rms_scaling If True, center and scale are ignored, and the inputs are scaled by gamma and the inverse square root of the square of all inputs. This is an approximate and faster approach that avoids ever computing the mean of the input.
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.
**kwargs Base layer keyword arguments (e.g. name and dtype).

Reference:

input Retrieves the input tensor(s) of a symbolic operation.

Only returns the tensor(s) corresponding to the first time the operation was called.

output Retrieves the output tensor(s) of a layer.

Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

from_config

View source

@classmethod
from_config(
    config
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Args
config A Python dictionary, typically the output of get_config.

Returns
A layer instance.

symbolic_call

View source

symbolic_call(
    *args, **kwargs
)