tf.contrib.layers.instance_norm

Functional interface for the instance normalization layer.

tf.contrib.layers.instance_norm(
    inputs, center=True, scale=True, epsilon=1e-06, activation_fn=None,
    param_initializers=None, reuse=None, variables_collections=None,
    outputs_collections=None, trainable=True, data_format=DATA_FORMAT_NHWC,
    scope=None
)

Reference: https://arxiv.org/abs/1607.08022

"Instance Normalization: The Missing Ingredient for Fast Stylization" Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky

Args
inputs	A tensor with 2 or more dimensions, where the first dimension has batch_size. The normalization is over all but the last dimension if data_format is NHWC and the second dimension if data_format is NCHW.
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 can be done by the next layer.
epsilon	Small float added to variance to avoid dividing by zero.
activation_fn	Activation function, default set to None to skip it and maintain a linear activation.
param_initializers	Optional initializers for beta, gamma, moving mean and moving variance.
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	If True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
data_format	A string. NHWC (default) and NCHW are supported.
scope	Optional scope for variable_scope.

Returns
A Tensor representing the output of the operation.

Raises
ValueError	If data_format is neither NHWC nor NCHW.
ValueError	If the rank of inputs is undefined.
ValueError	If rank or channels dimension of inputs is undefined.