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Functional interface for the instance normalization layer.


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

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.

A Tensor representing the output of the operation.

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.