tf.contrib.layers.layer_norm(
inputs,
center=True,
scale=True,
activation_fn=None,
reuse=None,
variables_collections=None,
outputs_collections=None,
trainable=True,
begin_norm_axis=1,
begin_params_axis=-1,
scope=None
)
Defined in tensorflow/contrib/layers/python/layers/layers.py
.
Adds a Layer Normalization layer.
Based on the paper:
"Layer Normalization"
Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton
https://arxiv.org/abs/1607.06450.
Can be used as a normalizer function for conv2d and fully_connected.
Given a tensor inputs
of rank R
, moments are calculated and normalization
is performed over axes begin_norm_axis ... R - 1
. Scaling and centering,
if requested, is performed over axes begin_params_axis .. R - 1
.
By default, begin_norm_axis = 1
and begin_params_axis = -1
,
meaning that normalization is performed over all but the first axis
(the HWC
if inputs
is NHWC
), while the beta
and gamma
trainable
parameters are calculated for the rightmost axis (the C
if inputs
is
NHWC
). Scaling and recentering is performed via broadcast of the
beta
and gamma
parameters with the normalized tensor.
The shapes of beta
and gamma
are inputs.shape[begin_params_axis:]
,
and this part of the inputs' shape must be fully defined.
Args:
inputs
: A tensor having rankR
. The normalization is performed over axesbegin_norm_axis ... R - 1
and centering and scaling parameters are calculated overbegin_params_axis ... R - 1
.center
: If True, add offset ofbeta
to normalized tensor. If False,beta
is ignored.scale
: If True, multiply bygamma
. If False,gamma
is not used. When the next layer is linear (also e.g.nn.relu
), this can be disabled since the scaling can be done by the next layer.activation_fn
: Activation function, default set to None to skip it and maintain a linear activation.reuse
: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.variables_collections
: Optional collections for the variables.outputs_collections
: Collections to add the outputs.trainable
: IfTrue
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable).begin_norm_axis
: The first normalization dimension: normalization will be performed along dimensionsbegin_norm_axis : rank(inputs)
begin_params_axis
: The first parameter (beta, gamma) dimension: scale and centering parameters will have dimensionsbegin_params_axis : rank(inputs)
and will be broadcast with the normalized inputs accordingly.scope
: Optional scope forvariable_scope
.
Returns:
A Tensor
representing the output of the operation, having the same
shape and dtype as inputs
.
Raises:
ValueError
: If the rank ofinputs
is not known at graph build time, or ifinputs.shape[begin_params_axis:]
is not fully defined at graph build time.